Gut Microbiome Activity Contributes to Prediction of Individual Variation in Glycemic Response in Adults.

Limiting postprandial glycemic response (PPGR) is an important intervention in reducing the risk of chronic metabolic diseases and has been shown to impart significant health benefits in people with elevated levels of blood sugar. In this study, we collected gut microbiome activity data by assessing the metatranscriptome, and we measured the glycemic responses of 550 adults who consumed more than 30,000 meals, collectively, from omnivore or vegetarian/gluten-free diets. We demonstrate that gut microbiome activity, anthropometric factors, and food macronutrients modulate individual variation in glycemic response. We employ two predictive models, including a mixed-effects linear regression model (R = 0.77) and a gradient boosting machine model (Rtrain = 0.80/R2train = 0.64; Rtest = 0.64/R2test = 0.40), which demonstrate variation in PPGR between individuals when ingesting the same foods. All features in the final mixed-effects linear regression model were significant (p < 0.05) except for two features which were retained as suggestive: glutamine production pathways (p = 0.08) and the interaction between tyrosine metabolizers and carbs (p = 0.06). We introduce molecular functions as features in these two models, aggregated from microbial activity data, and show their statistically significant contributions to glycemic control. In summary, we demonstrate for the first time that metatranscriptomic activity of the gut microbiome is correlated with PPGR among adults.

Blood sugar dysregulation is caused by various underlying conditions, including type 2 diabetes, and this may lead to extended periods of hypoglycemia or hyperglycemia, which can be harmful or deadly. Clinically, glycemic control is a primary therapeutic target for dysglycemia, and food and nutrition are frequent interventions used to reduce postprandial blood glucose excursions. Primary determinants of postprandial glycemic response (PPGR) include dietary carbohydrates, individual phenotypes, and individual molecular characteristics which include the gut microbiome. Typical investigations of gut microbiomes depend on analysis methods which have poor taxonomic resolution, cannot identify certain microorganisms, and are prone to errors. In this study, each RNA molecule was identified and counted, allowing quantitative strain-level taxonomic classification and molecular pathway analysis. The primary goal of the study was to assess the impact of microbial functional activity on PPGR. The study was conducted in the USA and involved a multiethnic population of healthy adults with HbA1c levels below 6.5. All participants received 14-day omnivore diets or vegetarian/gluten-free diets, depending on nutritional requirements (omnivore diets include meat while vegetarian/gluten-free diets exclude both gluten and meat). Over this timeframe, blood glucose levels were measured in 15-min intervals, 24 h per day, capturing postprandial responses for more than 27,000 meals, including more than 18,000 provided meals which spanned a wide range of foods and macronutrient characteristics. Computational modeling demonstrated the statistical significance of all features and identified new features which may be relevant to glycemic control. These results show, for the first time, that a person's glycemic response depends on individual traits, including both their anthropometrics and their gut metatranscriptome, representing the activity of gut microbiomes.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

A clinically validated human capillary blood transcriptome test for global systems biology studies.

To prevent and treat chronic diseases, including cancer, a global application of systems biology is needed. We report here a whole blood transcriptome test that needs only 50 µl of capillary (fingerprick) blood. This test is suitable for global applications because the samples are preserved at ambient temperature for up to 4 weeks and the RNA preservative inactivates all pathogens, enabling safe transportation. Both the laboratory and bioinformatic steps are automated and performed in a clinical lab, which minimizes batch effects and creates unbiased datasets. Given its clinical testing performance and accessibility to traditionally underrepresented and diverse populations, this test offers a unique ability to reveal molecular mechanisms of disease and enable longitudinal, population-scale studies.

A Robust Metatranscriptomic Technology for Population-Scale Studies of Diet, Gut Microbiome, and Human Health.

A functional readout of the gut microbiome is necessary to enable precise control of the gut microbiome's functions, which support human health and prevent or minimize a wide range of chronic diseases. Stool metatranscriptomic analysis offers a comprehensive functional view of the gut microbiome, but despite its usefulness, it has rarely been used in clinical studies due to its complexity, cost, and bioinformatic challenges. This method has also received criticism due to potential intrasample variability, rapid changes, and RNA degradation. Here, we describe a robust and automated stool metatranscriptomic method, called Viomega, which was specifically developed for population-scale studies. Viomega includes sample collection, ambient temperature sample preservation, total RNA extraction, physical removal of ribosomal RNAs (rRNAs), preparation of directional Illumina libraries, Illumina sequencing, taxonomic classification based on a database of >110,000 microbial genomes, and quantitative microbial gene expression analysis using a database of ~100 million microbial genes. We applied this method to 10,000 human stool samples and performed several small-scale studies to demonstrate sample stability and consistency. In summary, Viomega is an inexpensive, high-throughput, automated, and accurate sample-to-result stool metatranscriptomic technology platform for large-scale studies and a wide range of applications.

A Systems Biology Approach Reveals Converging Molecular Mechanisms that Link Different POPs to Common Metabolic Diseases.

BACKGROUND: A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published. OBJECTIVES: We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs [2,3,7,8-tetrachlorodibenzodioxin (TCDD), 2,2´,4,4´,5,5´-hexachlorobiphenyl (PCB 153), and 4,4´-dichlorodiphenyldichloroethylene (p,p´-DDE)] were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases. METHODS: For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes. CONCLUSIONS: Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies. CITATION: Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034-1041; http://dx.doi.org/10.1289/ehp.1510308.

The transcription factor ATF2 promotes melanoma metastasis by suppressing protein fucosylation.

Melanoma is one of the most lethal skin cancers worldwide, primarily because of its propensity to metastasize. Thus, the elucidation of mechanisms that govern metastatic propensity is urgently needed. We found that protein kinase Cε (PKCε)-mediated activation of activating transcription factor 2 (ATF2) controls the migratory and invasive behaviors of melanoma cells. PKCε-dependent phosphorylation of ATF2 promoted its transcriptional repression of the gene encoding fucokinase (FUK), which mediates the fucose salvage pathway and thus global cellular protein fucosylation. In primary melanocytes and cell lines representing early-stage melanoma, the abundance of PKCε-phosphorylated ATF2 was low, thereby enabling the expression of FUK and cellular protein fucosylation, which promoted cellular adhesion and reduced motility. In contrast, increased expression of the gene encoding PKCε and abundance of phosphorylated, transcriptionally active ATF2 were observed in advanced-stage melanomas and correlated with decreased FUK expression, decreased cellular protein fucosylation, attenuated cell adhesion, and increased cell motility. Restoring fucosylation in mice either by dietary fucose supplementation or by genetic manipulation of murine Fuk expression attenuated primary melanoma growth, increased the number of intratumoral natural killer cells, and decreased distal metastasis in murine isograft models. Tumor microarray analysis of human melanoma specimens confirmed reduced fucosylation in metastatic tumors and a better prognosis for primary melanomas that had high abundance of fucosylation. Thus, inhibiting PKCε or ATF2 or increasing protein fucosylation in tumor cells may improve clinical outcome in melanoma patients.

Downregulation of the Ubiquitin Ligase RNF125 Underlies Resistance of Melanoma Cells to BRAF Inhibitors via JAK1 Deregulation.

Despite the remarkable clinical response of melanoma harboring BRAF mutations to BRAF inhibitors (BRAFi), most tumors become resistant. Here, we identified the downregulation of the ubiquitin ligase RNF125 in BRAFi-resistant melanomas and demonstrated its role in intrinsic and adaptive resistance to BRAFi in cultures as well as its association with resistance in tumor specimens. Sox10/MITF expression correlated with and contributed to RNF125 transcription. Reduced RNF125 was associated with elevated expression of receptor tyrosine kinases (RTKs), including EGFR. Notably, RNF125 altered RTK expression through JAK1, which we identified as an RNF125 substrate. RNF125 bound to and ubiquitinated JAK1, prompting its degradation and suppressing RTK expression. Inhibition of JAK1 and EGFR signaling overcame BRAFi resistance in melanoma with reduced RNF125 expression, as shown in culture and in in vivo xenografts. Our findings suggest that combination therapies targeting both JAK1 and EGFR could be effective against BRAFi-resistant tumors with de novo low RNF125 expression.

Reconstitution of the ERG Gene Expression Network Reveals New Biomarkers and Therapeutic Targets in ERG Positive Prostate Tumors.

BACKGROUND: Despite a growing number of studies evaluating cancer of prostate (CaP) specific gene alterations, oncogenic activation of the ETS Related Gene (ERG) by gene fusions remains the most validated cancer gene alteration in CaP. Prevalent gene fusions have been described between the ERG gene and promoter upstream sequences of androgen-inducible genes, predominantly TMPRSS2 (transmembrane protease serine 2). Despite the extensive evaluations of ERG genomic rearrangements, fusion transcripts and the ERG oncoprotein, the prognostic value of ERG remains to be better understood. Using gene expression dataset from matched prostate tumor and normal epithelial cells from an 80 GeneChip experiment examining 40 tumors and their matching normal pairs in 40 patients with known ERG status, we conducted a cancer signaling-focused functional analysis of prostatic carcinoma representing moderate and aggressive cancers stratified by ERG expression. RESULTS: In the present study of matched pairs of laser capture microdissected normal epithelial cells and well-to-moderately differentiated tumor epithelial cells with known ERG gene expression status from 20 patients with localized prostate cancer, we have discovered novel ERG associated biochemical networks. CONCLUSIONS: Using causal network reconstruction methods, we have identified three major signaling pathways related to MAPK/PI3K cascade that may indeed contribute synergistically to the ERG dependent tumor development. Moreover, the key components of these pathways have potential as biomarkers and therapeutic target for ERG positive prostate tumors.

A systems biology strategy for predicting similarities and differences of drug effects: evidence for drug-specific modulation of inflammation in atherosclerosis.

BACKGROUND: Successful drug development has been hampered by a limited understanding of how to translate laboratory-based biological discoveries into safe and effective medicines. We have developed a generic method for predicting the effects of drugs on biological processes. Information derived from the chemical structure and experimental omics data from short-term efficacy studies are combined to predict the possible protein targets and cellular pathways affected by drugs. RESULTS: Validation of the method with anti-atherosclerotic compounds (fenofibrate, rosuvastatin, LXR activator T0901317) demonstrated a great conformity between the computationally predicted effects and the wet-lab biochemical effects. Comparative genome-wide pathway mapping revealed that the biological drug effects were realized largely via different pathways and mechanisms. In line with the predictions, the drugs showed differential effects on inflammatory pathways (downstream of PDGF, VEGF, IFNγ, TGFß, IL1ß, TNFα, LPS), transcriptional regulators (NFκB, C/EBP, STAT3, AP-1) and enzymes (PKCδ, AKT, PLA2), and they quenched different aspects of the inflammatory signaling cascade. Fenofibrate, the compound predicted to be most efficacious in inhibiting early processes of atherosclerosis, had the strongest effect on early lesion development. CONCLUSION: Our approach provides mechanistic rationales for the differential and common effects of drugs and may help to better understand the origins of drug actions and the design of combination therapies.

Integrated pathway analysis of rat urine metabolic profiles and kidney transcriptomic profiles to elucidate the systems toxicology of model nephrotoxicants.

In this study, approximately 40 endogenous metabolites were identified and quantified by (1)H NMR in urine samples from male rats dosed with two proximal tubule toxicants, cisplatin and gentamicin. The excreted amount of a majority of those metabolites in urine was found to be dose-dependent and exhibited a strong correlation with histopathology scores of overall proximal tubule damage. MetaCore pathway analysis software (GeneGo Inc.) was employed to identify nephrotoxicant-associated biochemical changes via an integrated quantitative analysis of both urine metabolomic and kidney transcriptomic profiles. Correlation analysis was applied to establish quantitative linkages between pairs of individual metabolite and gene transcript profiles in both cisplatin and gentamicin studies. This analysis revealed that cisplatin and gentamicin treatments were strongly linked to declines in mRNA transcripts for several luminal membrane transporters that handle each of the respective elevated urinary metabolites, such as glucose, amino acids, and monocarboxylic acids. The integrated pathway analysis performed on these studies indicates that cisplatin- or gentamicin-induced renal Fanconi-like syndromes manifested by glucosuria, hyperaminoaciduria, lactic aciduria, and ketonuria might be better explained by the reduction of functional proximal tubule transporters rather than by the perturbation of metabolic pathways inside kidney cells. Furthermore, this analysis suggests that renal transcription factors HNF1alpha, HNF1beta, and HIF-1 might be the central mediators of drug-induced kidney injury and adaptive response pathways.

Distinct transcription profiles of primary and secondary glioblastoma subgroups.

Glioblastomas are invasive and aggressive tumors of the brain, generally considered to arise from glial cells. A subset of these cancers develops from lower-grade gliomas and can thus be clinically classified as "secondary," whereas some glioblastomas occur with no prior evidence of a lower-grade tumor and can be clinically classified as "primary." Substantial genetic differences between these groups of glioblastomas have been identified previously. We used large-scale expression analyses to identify glioblastoma-associated genes (GAG) that are associated with a more malignant phenotype via comparison with lower-grade astrocytomas. We have further defined gene expression differences that distinguish primary and secondary glioblastomas. GAGs distinct to primary or secondary tumors provided information on the heterogeneous properties and apparently distinct oncogenic mechanisms of these tumors. Secondary GAGs primarily include mitotic cell cycle components, suggesting the loss of function in prominent cell cycle regulators, whereas primary GAGs highlight genes typical of a stromal response, suggesting the importance of extracellular signaling. Immunohistochemical staining of glioblastoma tissue arrays confirmed expression differences. These data highlight that the development of gene pathway-targeted therapies may need to be specifically tailored to each subtype of glioblastoma.