Deficient Endoplasmic Reticulum-Mitochondrial Phosphatidylserine Transfer Causes Liver Disease.

Non-alcoholic fatty liver is the most common liver disease worldwide. Here, we show that the mitochondrial protein mitofusin 2 (Mfn2) protects against liver disease. Reduced Mfn2 expression was detected in liver biopsies from patients with non-alcoholic steatohepatitis (NASH). Moreover, reduced Mfn2 levels were detected in mouse models of steatosis or NASH, and its re-expression in a NASH mouse model ameliorated the disease. Liver-specific ablation of Mfn2 in mice provoked inflammation, triglyceride accumulation, fibrosis, and liver cancer. We demonstrate that Mfn2 binds phosphatidylserine (PS) and can specifically extract PS into membrane domains, favoring PS transfer to mitochondria and mitochondrial phosphatidylethanolamine (PE) synthesis. Consequently, hepatic Mfn2 deficiency reduces PS transfer and phospholipid synthesis, leading to endoplasmic reticulum (ER) stress and the development of a NASH-like phenotype and liver cancer. Ablation of Mfn2 in liver reveals that disruption of ER-mitochondrial PS transfer is a new mechanism involved in the development of liver disease.

CBL/CAP Is Essential for Mitochondria Respiration Complex I Assembly and Bioenergetics Efficiency in Muscle Cells.

CBL is rapidly phosphorylated upon insulin receptor activation. Mice whole body CBL depletion improved insulin sensitivity and glucose clearance; however, the precise mechanisms remain unknown. We depleted either CBL or its associated protein SORBS1/CAP independently in myocytes and assessed mitochondrial function and metabolism compared to control cells. CBL- and CAP-depleted cells showed increased mitochondrial mass with greater proton leak. Mitochondrial respiratory complex I activity and assembly into respirasomes were reduced. Proteome profiling revealed alterations in proteins involved in glycolysis and fatty acid degradation. Our findings demonstrate CBL/CAP pathway couples insulin signaling to efficient mitochondrial respiratory function and metabolism in muscle.

Allostatic hypermetabolic response in PGC1α/ß heterozygote mouse despite mitochondrial defects.

Aging, obesity, and insulin resistance are associated with low levels of PGC1α and PGC1ß coactivators and defective mitochondrial function. We studied mice deficient for PGC1α and PGC1ß [double heterozygous (DH)] to investigate their combined pathogenic contribution. Contrary to our hypothesis, DH mice were leaner, had increased energy dissipation, a pro-thermogenic profile in BAT and WAT, and improved carbohydrate metabolism compared to wild types. WAT showed upregulation of mitochondriogenesis/oxphos machinery upon allelic compensation of PGC1α4 from the remaining allele. However, DH mice had decreased mitochondrial OXPHOS and biogenesis transcriptomes in mitochondria-rich organs. Despite being metabolically healthy, mitochondrial defects in DH mice impaired muscle fiber remodeling and caused qualitative changes in the hepatic lipidome. Our data evidence first the existence of organ-specific compensatory allostatic mechanisms are robust enough to drive an unexpected phenotype. Second, optimization of adipose tissue bioenergetics is sufficient to maintain a healthy metabolic phenotype despite a broad severe mitochondrial dysfunction in other relevant metabolic organs. Third, the decrease in PGC1s in adipose tissue of obese and diabetic patients is in contrast with the robustness of the compensatory upregulation in the adipose of the DH mice.

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms.

The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds' multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.

Diacetyl control during brewery fermentation via adaptive laboratory engineering of the lager yeast Saccharomyces pastorianus.

Diacetyl contributes to the flavor profile of many fermented products. Its typical buttery flavor is considered as an off flavor in lager-style beers, and its removal has a major impact on time and energy expenditure in breweries. Here, we investigated the possibility of lowering beer diacetyl levels through evolutionary engineering of lager yeast for altered synthesis of α-acetolactate, the precursor of diacetyl. Cells were exposed repeatedly to a sub-lethal level of chlorsulfuron, which inhibits the acetohydroxy acid synthase responsible for α-acetolactate production. Initial screening of 7 adapted isolates showed a lower level of diacetyl during wort fermentation and no apparent negative influence on fermentation rate or alcohol yield. Pilot-scale fermentation was carried out with one isolate and results confirmed the positive effect of chlorsulfuron adaptation. Diacetyl levels were over 60% lower at the end of primary fermentation relative to the non-adapted lager yeast and no significant change in fermentation performance or volatile flavor profile was observed due to the adaptation. Whole-genome sequencing revealed a non-synonymous SNP in the ILV2 gene of the adapted isolate. This mutation is known to confer general tolerance to sulfonylurea compounds, and is the most likely cause of the improved tolerance. Adaptive laboratory evolution appears to be a natural, simple and cost-effective strategy for diacetyl control in brewing.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

BACKGROUND: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. METHODS: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. FINDINGS: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. INTERPRETATION: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. FUNDING: Sanofi US Services, Project Data Sphere.

Early metabolic markers identify potential targets for the prevention of type 2 diabetes.

AIMS/HYPOTHESIS: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. METHODS: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study. RESULTS: Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as α-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and α-hydroxybutyrate and routinely used clinical risk factors. CONCLUSIONS/INTERPRETATION: This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.

De novo transcriptome assembly of Conium maculatum L. to identify candidate genes for coniine biosynthesis.

Poison hemlock (Conium maculatum L.) is a notorious weed containing the potent alkaloid coniine. Only some of the enzymes in the coniine biosynthesis have so far been characterized. Here, we utilize the next-generation RNA sequencing approach to report the first-ever transcriptome sequencing of five organs of poison hemlock: developing fruit, flower, root, leaf, and stem. Using a de novo assembly approach, we derived a transcriptome assembly containing 123,240 transcripts. The assembly is deemed high quality, representing over 88% of the near-universal ortholog genes of the Eudicots clade. Nearly 80% of the transcripts were functionally annotated using a combination of three approaches. The current study focuses on describing the coniine pathway by identifying in silico transcript candidates for polyketide reductase, L-alanine:5-keto-octanal aminotransferase, γ-coniceine reductase, and S-adenosyl-L-methionine:coniine methyltransferase. In vitro testing will be needed to confirm the assigned functions of the selected candidates.

Production of biopolymer precursors beta-alanine and L-lactic acid from CO2 with metabolically versatile Rhodococcus opacus DSM 43205.

Hydrogen oxidizing autotrophic bacteria are promising hosts for conversion of CO2 into chemicals. In this work, we engineered the metabolically versatile lithoautotrophic bacterium R. opacus strain DSM 43205 for synthesis of polymer precursors. Aspartate decarboxylase (panD) or lactate dehydrogenase (ldh) were expressed for beta-alanine or L-lactic acid production, respectively. The heterotrophic cultivations on glucose produced 25 mg L-1 beta-alanine and 742 mg L-1 L-lactic acid, while autotrophic cultivations with CO2, H2, and O2 resulted in the production of 1.8 mg L-1 beta-alanine and 146 mg L-1 L-lactic acid. Beta-alanine was also produced at 345 µg L-1 from CO2 in electrobioreactors, where H2 and O2 were provided by water electrolysis. This work demonstrates that R. opacus DSM 43205 can be engineered to produce chemicals from CO2 and provides a base for its further metabolic engineering.

Characterizing the Quality of Insight by Interactions: A Case Study.

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.

Lager Yeast Design Through Meiotic Segregation of a Saccharomyces cerevisiae × Saccharomyces eubayanus Hybrid.

Yeasts in the lager brewing group are closely related and consequently do not exhibit significant genetic variability. Here, an artificial Saccharomyces cerevisiae × Saccharomyces eubayanus tetraploid interspecies hybrid was created by rare mating, and its ability to sporulate and produce viable gametes was exploited to generate phenotypic diversity. Four spore clones obtained from a single ascus were isolated, and their brewing-relevant phenotypes were assessed. These F1 spore clones were found to differ with respect to fermentation performance under lager brewing conditions (15°C, 15 °Plato), production of volatile aroma compounds, flocculation potential and temperature tolerance. One spore clone, selected for its rapid fermentation and acetate ester production was sporulated to produce an F2 generation, again comprised of four spore clones from a single ascus. Again, phenotypic diversity was introduced. In two of these F2 clones, the fermentation performance was maintained and acetate ester production was improved relative to the F1 parent and the original hybrid strain. Strains also performed well in comparison to a commercial lager yeast strain. Spore clones varied in ploidy and chromosome copy numbers, and faster wort fermentation was observed in strains with a higher ploidy. An F2 spore clone was also subjected to 10 consecutive wort fermentations, and single cells were isolated from the resulting yeast slurry. These isolates also exhibited variable fermentation performance and chromosome copy numbers, highlighting the instability of polyploid interspecific hybrids. These results demonstrate the value of this natural approach to increase the phenotypic diversity of lager brewing yeast strains.

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers.

CONTEXT: Early prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c). OBJECTIVE: To evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT). DESIGN AND SETTING: We measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes. RESULTS: The 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker. CONCLUSIONS: The 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry.

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells.

BACKGROUND: Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity. METHODS: We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments. RESULTS: Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment. CONCLUSIONS: We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.