Suppression of GNAI2 message in ovarian cancer

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome sequencing

Indicine cattle, also referred to as zebu (Bos taurus indicus), play a central role in pastoral communities across a wide range of agro-ecosystems, from extremely hot semiarid regions to hot humid tropical regions. However, their adaptive genetic changes following their dispersal into East Asia from the Indian subcontinent have remained poorly documented. Here, we characterize their global genetic diversity using high-quality whole-genome sequencing data from 354 indicine cattle of 57 breeds/populations, including major indicine phylogeographic groups worldwide. We reveal their probable migration into East Asia was along a coastal route rather than inland routes and we detected introgression from other bovine species. Genomic regions carrying morphology-, immune-, and heat-tolerance-related genes underwent divergent selection according to Asian agro-ecologies. We identify distinct sets of loci that contain promising candidate variants for adaptation to hot semi-arid and hot humid tropical ecosystems. Our results indicate that the rapid and successful adaptation of East Asian indicine cattle to hot humid environments was promoted by localized introgression from banteng and/or gaur. Our findings provide insights into the history and environmental adaptation of indicine cattle.

Combination Analysis of Ferroptosis and Immune Status Predicts Patients Survival in Breast Invasive Ductal Carcinoma

Ferroptosis is a new form of iron-dependent cell death and plays an important role during the occurrence and development of various tumors. Increasingly, evidence shows a convincing interaction between ferroptosis and tumor immunity, which affects cancer patients' prognoses. These two processes cooperatively regulate different developmental stages of tumors and could be considered important tumor therapeutic targets. However, reliable prognostic markers screened based on the combination of ferroptosis and tumor immune status have not been well characterized. Here, we chose the ssGSEA and ESTIMATE algorithms to evaluate the ferroptosis and immune status of a TCGA breast invasive ductal carcinoma (IDC) cohort, which revealed their correlation characteristics as well as patients' prognoses. The WGCNA algorithm was used to identify genes related to both ferroptosis and immunity. Univariate COX, LASSO regression, and multivariate Cox regression models were used to screen prognostic-related genes and construct prognostic risk models. Based on the ferroptosis and immune scores, the cohort was divided into three groups: a high-ferroptosis/low-immune group, a low-ferroptosis/high-immune group, and a mixed group. These three groups exhibited distinctive survival characteristics, as well as unique clinical phenotypes, immune characteristics, and activated signaling pathways. Among them, low-ferroptosis and high-immune statuses were favorable factors for the survival rates of patients. A total of 34 differentially expressed genes related to ferroptosis-immunity were identified among the three groups. After univariate, Lasso regression, and multivariate stepwise screening, two key prognostic genes (GNAI2, PSME1) were identified. Meanwhile, a risk prognosis model was constructed, which can predict the overall survival rate in the validation set. Lastly, we verified the importance of model genes in three independent GEO cohorts. In short, we constructed a prognostic model that assists in patient risk stratification based on ferroptosis-immune-related genes in IDC. This model helps assess patients' prognoses and guide individualized treatment, which also further eelucidatesthe molecular mechanisms of IDC.

GNAI2 Is a Risk Factor for Gastric Cancer: Study of Tumor Microenvironment (TME) and Establishment of Immune Risk Score (IRS)

Purpose

Although the G protein subunit α i2 (GNAI2) is upregulated in multiple cancers, its prognostic value and exact role in the development of gastric cancer (GC) remain largely unknown.

Methods

This study evaluated the effect of GNAI2 on the tumor microenvironment (TME) in GC, constructed an immune risk score (IRS) model based on differentially-expressed immune genes, and systematically correlated GNAI2 and epigenetic factor expression patterns with TME and IRS. Also, RT-qPCR, flow cytometry, Western blotting (WB), and transwell assays were carried out to explore the regulatory mechanism of GNAI2 in GC.

Results

High GNAI2 expression was associated with poor prognosis. Cytokine activation, an increase in tumor-infiltrating immune cells (TIIC), and the accumulation of regulatory T cells in the tumor immune cycle were all promoted by the TME, which was significantly associated with GNAI2 expression. Two different differentially expressed mRNA (DER) modification patterns were determined. These two DERs-clusters had significantly different TME cell infiltrations and were classified as either noninflamed or immune-inflamed phenotypes. The IRS model constructed using differentially expressed genes (DEGs) had great potential in predicting GC prognosis. The IRS model was also used in assessing clinicopathological features, such as microsatellite instability (MSI) status, epithelial-mesenchymal transition (EMT) status, clinical stages, tumor mutational burden (TMB), and tumor immune dysfunction and exclusion (TIDE) scores. Low IRS scores were associated with high immune checkpoint gene expression. Cell and animal studies confirmed that GNAI2 activated PI3K/AKT pathway and promoted the growth and migration of GC cells.

Conclusion

The IRS model can be used for survival prediction and GNAI2 serves as a candidate therapeutic target for GC patients.

Neighborhood characteristics and breast tumor methylation: using epigenomics to explore cancer outcome disparities

BACKGROUND

Social exposures may drive epigenetic alterations that affect racial disparities in breast cancer outcomes. This study examined the association between neighborhood-level factors and DNA methylation in non-Hispanic Black and White women diagnosed with breast cancer.

METHODS

Genome-wide DNA methylation was measured using the EPIC array in the tumor tissue of 96 women. Linear regression models were used to examine the association between nine neighborhood-level factors and methylation, regressing β values for each cytosine-phosphate guanine dinucleotide (CpG) site on neighborhood-level factors while adjusting for covariates. Neighborhood data were obtained from the Opportunity Atlas. We used a false discovery rate (FDR) threshold < 0.05, and for CpGs below this threshold, we examined interactions with race. We employed multivariable Cox proportional-hazards models to estimate whether aberrant methylation was associated with all-cause mortality.

RESULTS

26 of the CpG sites were associated with job density or college education (FDR < 0.05). Further exploration of these 26 CpG sites revealed no interactions by race, but a single probe in TMEM204 was associated with all-cause mortality.

CONCLUSION

We identified novel associations between neighborhood-level factors and the breast tumor DNA methylome. Our data are the first to show that dysregulation in neighborhood associated CpG sites may be associated with all-cause mortality. Neighborhood-level factors may contribute to differential tumor methylation in genes related to tumor progression and metastasis. This contributes to the increasing body of evidence that area-level factors (such as neighborhood characteristics) may play an important role in cancer disparities through modulation of the breast tumor epigenome.

Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

The Connection between MicroRNAs and Oral Cancer Pathogenesis: Emerging Biomarkers in Oral Cancer Management

Oral cancer is a common human malignancy that still maintains an elevated mortality rate despite scientific progress. Tumorigenesis is driven by altered gene expression patterns of proto-oncogenes and tumor-suppressor genes. MicroRNAs, a class of short non-coding RNAs involved in gene regulation, seem to play important roles in oral cancer development, progression, and tumor microenvironment modulation. As properties of microRNAs render them stable in diverse liquid biopsies, together with their differential expression signature in cancer cells, these features place microRNAs at the top of promising biomarkers for diagnostic and prognostic values. In this review, we highlight eight expression levels and functions of the most relevant microRNAs involved in oral cancer development, progression, and microenvironment sustainability. Furthermore, we emphasize the potential of using these small RNA species as non-invasive biomarkers for the early detection of oral cancerous lesions. Conclusively, we highlight the perspectives and limitations of microRNAs as novel diagnostic tools, as well as therapeutic models.

GNAi2/gip2-Regulated Transcriptome and Its Therapeutic Significance in Ovarian Cancer

Increased expression of GNAi2, which encodes the α-subunit of G-protein i2, has been correlated with the late-stage progression of ovarian cancer. GNAi2, also referred to as the proto-oncogene gip2, transduces signals from lysophosphatidic acid (LPA)-activated LPA-receptors to oncogenic cellular responses in ovarian cancer cells. To identify the oncogenic program activated by gip2, we carried out micro-array-based transcriptomic and bioinformatic analyses using the ovarian cancer cell-line SKOV3, in which the expression of GNAi2/gip2 was silenced by specific shRNA. A cut-off value of 5-fold change in gene expression (p < 0.05) indicated that a total of 264 genes were dependent upon gip2-expression with 136 genes coding for functional proteins. Functional annotation of the transcriptome indicated the hitherto unknown role of gip2 in stimulating the expression of oncogenic/growth-promoting genes such as KDR/VEGFR2, CCL20, and VIP. The array results were further validated in a panel of High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines that included Kuramochi, OVCAR3, and OVCAR8 cells. Gene set enrichment analyses using DAVID, STRING, and Cytoscape applications indicated the potential role of the gip2-stimulated transcriptomic network involved in the upregulation of cell proliferation, adhesion, migration, cellular metabolism, and therapy resistance. The results unravel a multi-modular network in which the hub and bottleneck nodes are defined by ACKR3/CXCR7, IL6, VEGFA, CYCS, COX5B, UQCRC1, UQCRFS1, and FYN. The identification of these genes as the critical nodes in GNAi2/gip2 orchestrated onco-transcriptome establishes their role in ovarian cancer pathophysiology. In addition, these results also point to these nodes as potential targets for novel therapeutic strategies.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

Identification the prognostic value of glutathione peroxidases expression levels in acute myeloid leukemia

Background

Glutathione peroxidases (GPXs) are an enzyme family with peroxidase activity. Abnormal GPX expression is associated with carcinogenesis. However, the potential role of the GPX gene family in acute myeloid leukemia (AML) remains to be comprehensively examined.

Methods

We analyzed GPX mRNA expression levels and determined the correlation between gene expression and the prognostic value via multiple universally acknowledged databases including the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), PROGgeneV2, UALCAN, Cancer Cell Line Encyclopedia (CCLE), and The European Bioinformatics Institute (EMBL-EBI) databases. The functional network of differentially expressed GPXs was investigated via the NetworkAnalyst platform. Correlated genes as well as kinase, microRNA (miRNA), and transcription factor (TF) targets were identified using LinkedOmics.

Results

We observed that the transcriptional expression levels of GPX-1, -2, -4, -7, and -8 had significant difference between AML patients samples and normal samples, and that AML patients with high expression of GPX-1, -3, -4, and -7 were associated with poorer prognosis of overall survival (OS). Functional enrichment analysis showed that the differentially expressed GPXs were mainly enriched in response to oxidative stress, regulation of immune response, and inflammatory response, along with glutathione metabolism and ferroptosis. Overexpression of correlated genes, PSMB10, VPS13D, NDUFS8, ATP5D, POLR2E, and HADH were linked to adverse OS in AML. Regulatory network analysis indicated that differentially expressed GPXs regulated cell proliferation, cancer progression, apoptosis, and cell cycle signaling via pathways involving cancer-related kinases (such as DAPK1 and SRC), miRNAs (such as miR-202 and miR-181), and TFs (such as SRF and E2F1).

Conclusions

Our findings offer novel insights into the differential expression and prognostic potential of the GPX family in AML, and lay a foundation for subsequent research of GPX’s role in the carcinogenesis and regulatory network of AML.

Hepatocyte-specific suppression of microRNA-221-3p mitigates liver fibrosis

BACKGROUND & AIMS

Fibrosis, a cardinal feature of a dysfunctional liver, significantly contributes to the ever-increasing mortality due to end-stage chronic liver diseases. The crosstalk between hepatocytes and hepatic stellate cells (HSCs) plays a key role in the progression of fibrosis. Although ample efforts have been devoted to elucidate the functions of HSCs during liver fibrosis, the regulatory functions of hepatocytes remain elusive.

METHODS

Using an unbiased functional microRNA (miRNA) screening, we investigated the ability of hepatocytes to regulate fibrosis by fine-tuning gene expression via miRNA modulation. The in vivo functional analyses were performed by inhibiting miRNA in hepatocytes using adeno-associated virus in carbon-tetrachloride- and 3,5-di-diethoxycarbonyl-1,4-dihydrocollidine-induced liver fibrosis.

RESULTS

Blocking miRNA-221-3p function in hepatocytes during chronic liver injury facilitated recovery of the liver and faster resolution of the deposited extracellular matrix. Furthermore, we demonstrate that reduced secretion of C-C motif chemokine ligand 2, as a result of post-transcriptional regulation of GNAI2 (G protein alpha inhibiting activity polypeptide 2) by miRNA-221-3p, mitigates liver fibrosis.

CONCLUSIONS

Collectively, miRNA modulation in hepatocytes, an easy-to-target cell type in the liver, may serve as a potential therapeutic approach for liver fibrosis.

LAY SUMMARY

Liver fibrosis majorly contributes to mortality resulting from various liver diseases. We discovered a small RNA known as miRNA-221-3p, whose downregulation in hepatocytes results in reduced liver fibrosis. Thus, inhibition of miRNA-221-3p may serve as one of the therapeutic approaches for treatment of liver fibrosis.

Lysophosphatidic acid stimulates epithelial to mesenchymal transition marker Slug/Snail2 in ovarian cancer cells via Gαi2, Src, and HIF1α signaling nexus

Recent studies have identified a critical role for lysophosphatidic acid (LPA) in the progression of ovarian cancer. Using a transcription factor activation reporter array, which analyzes 45 distinct transcription factors, it has been observed that LPA observed robustly activates the transcription factor hypoxia-induced factor-1α (HIF1α) in SKOV3.ip ovarian cancer cells. HIF1α showed 150-fold increase in its activation profile compared to the untreated control. Validation of the array analysis indicated that LPA stimulates a rapid increase in the levels of HIF1α in ovarian cancer cells, with an observed maximum level of HIF1α-induction by 4 hours. Our report demonstrates that LPA stimulates the increase in HIF1α levels via Gαi2. Consistent with the role of HIF1α in epithelial to mesenchymal transition (EMT) of cancer cells, LPA stimulates EMT and associated invasive cell migration along with an increase in the expression levels N-cadherin and Slug/Snail2. Using the expression of Slug/Snail2 as a marker for EMT, we demonstrate that the inhibition of Gαi2, HIF1α or Src attenuates this response. In line with the established role of EMT in promoting invasive cell migration, our data demonstrates that the inhibition of HIF1α with the clinically used HIF1α inhibitor, PX-478, drastically attenuates LPA-stimulates invasive migration of SKOV3.ip cells. Thus, our present study demonstrates that LPA utilizes a Gαi2-mediated signaling pathway via Src kinase to stimulate an increase in HIF1α levels and downstream EMT-specific factors such as Slug, leading to invasive migration of ovarian cancer cells.

Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression

It is well-known that the conversion of normal colon epithelium to adenoma and then to carcinoma stems from acquired molecular changes in the genome. The genetic basis of colorectal cancer has been elucidated to a certain extent, and much remains to be known about the identity of specific cancer genes that are associated with the advancement of colorectal cancer from one stage to the next. Here in this study we attempted to identify novel cancer genes that could underlie the stage-specific progression and metastasis of colorectal cancer. We conducted a stage-based meta-analysis of the voluminous tumor genome-sequencing data and mined using multiple approaches for novel genes driving the progression to stage-II, stage-III and stage-IV colorectal cancer. The consensus of these driver genes seeded the construction of stage-specific networks, which were then analyzed for the centrality of genes, clustering of subnetworks, and enrichment of gene-ontology processes. Our study identified three novel driver genes as hubs for stage-II progression: DYNC1H1, GRIN2A, GRM1. Four novel driver genes were identified as hubs for stage-III progression: IGF1R, CPS1, SPTA1, DSP. Three novel driver genes were identified as hubs for stage-IV progression: GSK3B, GGT1, EIF2B5. We also identified several non-driver genes that appeared to underscore the progression of colorectal cancer. Our study yielded potential diagnostic biomarkers for colorectal cancer as well as novel stage-specific drug targets for rational intervention. Our methodology is extendable to the analysis of other types of cancer to fill the gaps in our knowledge.

A New Generation of FRET Sensors for Robust Measurement of Gαi1, Gαi2 and Gαi3 Activation Kinetics in Single Cells

G-protein coupled receptors (GPCRs) can activate a heterotrimeric G-protein complex with subsecond kinetics. Genetically encoded biosensors based on Förster resonance energy transfer (FRET) are ideally suited for the study of such fast signaling events in single living cells. Here we report on the construction and characterization of three FRET biosensors for the measurement of Gα_i1, Gα_i2 and Gα_i3 activation. To enable quantitative long-term imaging of FRET biosensors with high dynamic range, fluorescent proteins with enhanced photophysical properties are required. Therefore, we use the currently brightest and most photostable CFP variant, mTurquoise2, as donor fused to Gα_i subunit, and cp173Venus fused to the Gγ₂ subunit as acceptor. The Gα_i FRET biosensors constructs are expressed together with Gβ₁ from a single plasmid, providing preferred relative expression levels with reduced variation in mammalian cells. The Gα_i FRET sensors showed a robust response to activation of endogenous or over-expressed alpha-2A-adrenergic receptors, which was inhibited by pertussis toxin. Moreover, we observed activation of the Gα_i FRET sensor in single cells upon stimulation of several GPCRs, including the LPA₂, M₃ and BK₂ receptor. Furthermore, we show that the sensors are well suited to extract kinetic parameters from fast measurements in the millisecond time range. This new generation of FRET biosensors for Gα_i1, Gα_i2 and Gα_i3 activation will be valuable for live-cell measurements that probe Gα_i activation.

Genome-Wide Association Study of Golden Retrievers Identifies Germ-Line Risk Factors Predisposing to Mast Cell Tumours

Canine mast cell tumours (CMCT) are one of the most common skin tumours in dogs with a major impact on canine health. Certain breeds have a higher risk of developing mast cell tumours, suggesting that underlying predisposing germ-line genetic factors play a role in the development of this disease. The genetic risk factors are largely unknown, although somatic mutations in the oncogene C-KIT have been detected in a proportion of CMCT, making CMCT a comparative model for mastocytosis in humans where C-KIT mutations are frequent. We have performed a genome wide association study in golden retrievers from two continents and identified separate regions in the genome associated with risk of CMCT in the two populations. Sequence capture of associated regions and subsequent fine mapping in a larger cohort of dogs identified a SNP associated with development of CMCT in the GNAI2 gene (p = 2.2x10-16), introducing an alternative splice form of this gene resulting in a truncated protein. In addition, disease associated haplotypes harbouring the hyaluronidase genes HYAL1, HYAL2 and HYAL3 on cfa20 and HYAL4, SPAM1 and HYALP1 on cfa14 were identified as separate risk factors in European and US golden retrievers, respectively, suggesting that turnover of hyaluronan plays an important role in the development of CMCT.

Plasma membrane proteomics of human breast cancer cell lines identifies potential targets for breast cancer diagnosis and treatment

The use of broad spectrum chemotherapeutic agents to treat breast cancer results in substantial and debilitating side effects, necessitating the development of targeted therapies to limit tumor proliferation and prevent metastasis. In recent years, the list of approved targeted therapies has expanded, and it includes both monoclonal antibodies and small molecule inhibitors that interfere with key proteins involved in the uncontrolled growth and migration of cancer cells. The targeting of plasma membrane proteins has been most successful to date, and this is reflected in the large representation of these proteins as targets of newer therapies. In view of these facts, experiments were designed to investigate the plasma membrane proteome of a variety of human breast cancer cell lines representing hormone-responsive, ErbB2 over-expressing and triple negative cell types, as well as a benign control. Plasma membranes were isolated by using an aqueous two-phase system, and the resulting proteins were subjected to mass spectrometry analysis. Overall, each of the cell lines expressed some unique proteins, and a number of proteins were expressed in multiple cell lines, but in patterns that did not always follow traditional clinical definitions of breast cancer type. From our data, it can be deduced that most cancer cells possess multiple strategies to promote uncontrolled growth, reflected in aberrant expression of tyrosine kinases, cellular adhesion molecules, and structural proteins. Our data set provides a very rich and complex picture of plasma membrane proteins present on breast cancer cells, and the sorting and categorizing of this data provides interesting insights into the biology, classification, and potential treatment of this prevalent and debilitating disease.

Development of inhibitors of heterotrimeric Gαi subunits

Heterotrimeric G-proteins are the immediate downstream effectors of G-protein coupled receptors (GPCRs). Endogenous protein guanine nucleotide dissociation inhibitors (GDIs) like AGS3/4 and RGS12/14 function through GPR/Goloco GDI domains. Extensive characterization of GPR domain peptides indicate they function as selective GDIs for Gαi by competing for the GPCR and Gβγ and preventing GDP release. We modified a GPR consensus peptide by testing FGF and TAT leader sequences to make the peptide cell permeable. FGF modification inhibited GDI activity while TAT preserved GDI activity. TAT-GPR suppresses G-protein coupling to the receptor and completely blocked α2-adrenoceptor (α2AR) mediated decreases in cAMP in HEK293 cells at 100nM. We then sought to discover selective small molecule inhibitors for Gαi. Molecular docking was used to identify potential molecules that bind to and stabilize the Gαi-GDP complex by directly interacting with both Gαi and GDP. Gαi-GTP and Gαq-GDP were used as a computational counter screen and Gαq-GDP was used as a biological counter screen. Thirty-seven molecules were tested using nucleotide exchange. STD NMR assays with compound 0990, a quinazoline derivative, showed direct interaction with Gαi. Several compounds showed Gαi specific inhibition and were able to block α2AR mediated regulation of cAMP. In addition to being a pharmacologic tool, GDI inhibition of Gα subunits has the advantage of circumventing the upstream component of GPCR-related signaling in cases of overstimulation by agonists, mutations, polymorphisms, and expression-related defects often seen in disease.