Metformin Affects the Transcriptomic Profile of Chicken Ovarian Cancer Cells

Ovarian cancer is the most lethal gynecological malignancy in women. Metformin intake is associated with a reduced incidence of ovarian cancer and increased overall survival rate. We determined the effect of metformin on sphere formation, extracellular matrix invasion, and transcriptome profile of ovarian cancer cells (COVCAR) isolated from ascites of chickens that naturally developed ovarian cancer. We found that metformin treatment significantly decreased sphere formation and invasiveness of COVCAR cells. RNA-Seq data analysis revealed 0, 4, 365 differentially expressed genes in cells treated with 0.5, 1, 2 mM metformin, respectively compared to controls. Transcriptomic and ingenuity pathway analysis (IPA) revealed significant downregulation of MMP7, AICDA, GDPD2, APOC3, APOA1 and predicted inhibition of upstream regulators NFKB, STAT3, TP53 that are involved in epithelial–mesenchymal transition, DNA repair, and lipid metabolism. The analysis revealed significant upregulation of RASD2, IHH, CRABP-1 and predicted activation of upstream regulators VEGF and E2F1 that are associated with angiogenesis and cell cycle. Causal network analysis revealed novel pathways suggesting predicted inhibition of ovarian cancer through master regulator ASCL1 and dataset genes DCX, SEMA6B, HEY2, and KCNIP2. In summary, advanced pathway analysis in IPA revealed novel target genes, upstream regulators, and pathways affected by metformin treatment of COVCAR cells.

Comprehensive Analyses of the Expression, Genetic Alteration, Prognosis Significance, and Interaction Networks of m6A Regulators Across Human Cancers

Accumulating lines of evidence indicate that the deregulation of m6A is involved in various cancer types. The m6A RNA methylation is modulated by m6A methyltransferases, demethylases, and reader proteins. Although the aberrant expression of m6A RNA methylation contributes to the development and progression of multiple cancer types, the roles of m6A regulators across numerous types of cancers remain largely unknown. Here, we comprehensively investigated the expression, genetic alteration, and prognosis significance of 20 commonly studied m6A regulators across diverse cancer types using TCGA datasets via bioinformatic analyses. The results revealed that the m6A regulators exhibited widespread dysregulation, genetic alteration, and the modulation of oncogenic pathways across TCGA cancer types. In addition, most of the m6A regulators were closely relevant with significant prognosis in many cancer types. Furthermore, we also constructed the protein–protein interacting network of the 20 m6A regulators, and a more complex interacting regulatory network including m6A regulators and their corresponding interacting factors. Besides, the networks between m6A regulators and their upstream regulators such as miRNAs or transcriptional factors were further constructed in this study. Finally, the possible chemicals targeting each m6A regulator were obtained by bioinformatics analysis and the m6A regulators–potential drugs network was further constructed. Taken together, the comprehensive analyses of m6A regulators might provide novel insights into the m6A regulators’ roles across cancer types and shed light on their potential molecular mechanisms as well as help develop new therapy approaches for cancers.

RNA-Seq Analysis of Activated PBMC Treated With LMWF5A Predicts an Anti-Inflammatory Mode of Action and Similar Drug Targets to Dexamethasone

Background: The low molecular weight fraction of human serum albumin (LMWF5A) has immunomodulatory activity via its effects on multiple inflammatory mediators and is currently being evaluated for the treatment of hyperactive or persistent inflammatory conditions. To gain further insight into the mechanism of action (MOA) of LMWF5A, an investigation of its effects on activated immune cells was performed. Methods and Results: Peripheral blood mononuclear cells (PBMC) were treated with vehicle control or LMWF5A and stimulated with lipopolysaccharide (LPS), LPS/interferon γ, or interleukin (IL)-4/IL-13, and RNAseq was performed to determine differentially expressed genes (DEGs) within each condition. Unbiased Ingenuity Pathway Analysis (IPA) of DEGs revealed anti-inflammatory and pro-resolving activities for LMWF5A. Moreover, comparison to all IPA upstream regulators predicted that the LMWF5A MOA is opposite to pro-inflammatory regulators and significantly matches the activity of several anti-inflammatory molecules. These analyses identified the glucocorticoid dexamethasone (DEX) as the most significantly similar regulator to LMWF5A. To further explore similarities to DEX, LMWF5A DEGs were compared to two publicly available datasets of activated, DEX-treated PBMC. These comparisons showed continuity between predicted upstream regulators, affording further support to the hypothesis that LMWF5A acts in a manner like DEX. Nevertheless, not all LMWF5A-targeted DEGs showed directional regulation identical to DEX. Conclusions: This study further defines the MOA of LMWF5A and provides hypotheses for future investigations. Because of its predicted similar biological effects and known safety profile, LMWF5A could potentially be used to treat conditions that are supported for DEX with fewer or less harmful side effects.

Enhancer RNA-based modeling of adverse events and objective responses of immunotherapy

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 or CTLA-4 are emerging and effective immunotherapy strategies. However, ICI treated patients present heterogeneous responses and adverse events, thus demanding effective ways to assess benefit over risk before treatment. Here, by integrating pan-cancer clinical and molecular data, we tried to predict immune-related adverse events (irAEs, risk) and objective response rates (ORRs, benefit) based on enhancer RNAs (eRNAs) expression among patients receiving anti-PD-1/PD-L1 therapy. We built two effective regression models, explaining 71% variance (R=0.84) of irAEs with three eRNAs and 79% (R=0.89) of ORRs with five eRNAs. Interestingly, target genes of irAE-related enhancers, including upstream regulators of MYC, were involved in metabolism, inflammation, and immune activation, while ORR-related enhancers target PAK2 and DLG1 which directly participate in T cell activation. Our study provides references for the identification of immunotherapy-related biomarkers and potential therapeutic targets during immunotherapy.

Activation Mechanisms of the VPS34 Complexes

Phosphatidylinositol-3-phosphate (PtdIns(3)P) is essential for cell survival, and its intracellular synthesis is spatially and temporally regulated. It has major roles in two distinctive cellular pathways, namely, the autophagy and endocytic pathways. PtdIns(3)P is synthesized from phosphatidylinositol (PtdIns) by PIK3C3C/VPS34 in mammals or Vps34 in yeast. Pathway-specific VPS34/Vps34 activity is the consequence of the enzyme being incorporated into two mutually exclusive complexes: complex I for autophagy, composed of VPS34/Vps34–Vps15/Vps15-Beclin 1/Vps30-ATG14L/Atg14 (mammals/yeast), and complex II for endocytic pathways, in which ATG14L/Atg14 is replaced with UVRAG/Vps38 (mammals/yeast). Because of its involvement in autophagy, defects in which are closely associated with human diseases such as cancer and neurodegenerative diseases, developing highly selective drugs that target specific VPS34/Vps34 complexes is an essential goal in the autophagy field. Recent studies on the activation mechanisms of VPS34/Vps34 complexes have revealed that a variety of factors, including conformational changes, lipid physicochemical parameters, upstream regulators, and downstream effectors, greatly influence the activity of these complexes. This review summarizes and highlights each of these influences as well as clarifying key questions remaining in the field and outlining future perspectives.

Mechanisms and regulation of organic acid accumulation in plant vacuoles

In fleshy fruits, organic acids are the main source of fruit acidity and play an important role in regulating osmotic pressure, pH homeostasis, stress resistance, and fruit quality. The transport of organic acids from the cytosol to the vacuole and their storage are complex processes. A large number of transporters carry organic acids from the cytosol to the vacuole with the assistance of various proton pumps and enzymes. However, much remains to be explored regarding the vacuolar transport mechanism of organic acids as well as the substances involved and their association. In this review, recent advances in the vacuolar transport mechanism of organic acids in plants are summarized from the perspectives of transporters, channels, proton pumps, and upstream regulators to better understand the complex regulatory networks involved in fruit acid formation.

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data

Background: Parkinson’s disease (PD), Alzheimer’s disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them.Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model’s performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators.Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies.Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.

Oncogenic KRAS blockade therapy: renewed enthusiasm and persistent challenges

Across a broad range of human cancers, gain-of-function mutations in RAS genes (HRAS, NRAS, and KRAS) lead to constitutive activity of oncoproteins responsible for tumorigenesis and cancer progression. The targeting of RAS with drugs is challenging because RAS lacks classic and tractable drug binding sites. Over the past 30 years, this perception has led to the pursuit of indirect routes for targeting RAS expression, processing, upstream regulators, or downstream effectors. After the discovery that the KRAS-G12C variant contains a druggable pocket below the switch-II loop region, it has become possible to design irreversible covalent inhibitors for the variant with improved potency, selectivity and bioavailability. Two such inhibitors, sotorasib (AMG 510) and adagrasib (MRTX849), were recently evaluated in phase I-III trials for the treatment of non-small cell lung cancer with KRAS-G12C mutations, heralding a new era of precision oncology. In this review, we outline the mutations and functions of KRAS in human tumors and then analyze indirect and direct approaches to shut down the oncogenic KRAS network. Specifically, we discuss the mechanistic principles, clinical features, and strategies for overcoming primary or secondary resistance to KRAS-G12C blockade.

Integration of genetic colocalizations with physiological and pharmacological perturbations identifies cardiometabolic disease genes

BackgroundIdentification of causal genes for polygenic human diseases has been extremely challenging, and our understanding of how physiological and pharmacological stimuli modulate genetic risk at disease-associated loci is limited. Specifically, insulin resistance (IR), a common feature of cardiometabolic disease, including type 2 diabetes, obesity, and dyslipidemia, lacks well-powered GWAS, and therefore few associated loci and causal genes have been identified.ResultsHere, we perform and integrate LD-adjusted colocalization analyses across nine cardiometabolic traits combined with eQTLs and sQTLs from five metabolically relevant human tissues (subcutaneous and visceral adipose, skeletal muscle, liver, and pancreas). We identify 470 colocalized loci and prioritize 207 loci with a single colocalized gene. To elucidate upstream regulators and functional mechanisms for these genes, we integrate their transcriptional responses to 21 physiological and pharmacological cardiometabolic regulators in human adipocytes, hepatocytes, and skeletal muscle cells, and map their protein-protein interactions.ConclusionsOur use of transcriptional responses under metabolic perturbations to contextualize genetic associations from our state-of-the-art colocalization approach provides a list of likely causal genes and their upstream regulators in the context of IR-associated cardiometabolic risk.