Large‐scale genome‐wide screening of circulating microRNAs in clear cell renal cell carcinoma reveals specific signatures in late‐stage disease

Circulating miRNAs have shown great promises as noninvasive diagnostic and predictive biomarkers in several solid tumors. While the miRNA profiles of renal tumors have been extensively explored, knowledge of their circulating counterparts is limited. Our study aimed to provide a large‐scale genome‐wide profiling of plasma circulating miRNA in clear‐cell renal cell carcinoma (ccRCC). Plasma samples from 94 ccRCC cases and 100 controls were screened for 754 circulating micro‐RNAs (miRNA) by TaqMan arrays. Analyses including known risk factors for renal cancer—namely, age, sex, hypertension, obesity, diabetes, tobacco smoking and alcohol consumption—highlighted that circulating miRNA profiles were tightly correlated with the stage of the disease. Advanced tumors, characterized as stage III and IV, were associated with specific miRNA signatures that significantly differ from both controls and earlier stage ccRCC cases. Molecular pathway enrichment analyses of their gene targets showed high similarities with alterations observed in renal tumors. Plasma circulating levels of miR‐150 were significantly associated with RCC‐specific survival and could marginally improve the predictive accuracy of clinical parameters in our series, including age at diagnosis, sex and conventional staging. In summary, our results suggest that circulating miRNAs may provide insights into renal cell carcinoma progression.     

Ectrodactyly

A rare case of ectrodactyly or lobster claw without any other ectodermal involvement is presented. His family history was non-contributory.     

Transcatheter Closure of a Chronic Iatrogenic Arteriovenous Fistula Between the Carotid Artery and the Brachiocephalic Vein with an Amplatzer Duct Occluder in Combination with a Carotid Stent

We report an original method of transcatheter closure of an arteriovenous fistula using the combination of an Amplatzer PDA occluder and a carotid stent. The fistula was between the left carotid artery and the brachiocephalic vein. The patient had significant left-to-right shunt and was highly symptomatic. Due to the large orifice and pseudoaneurysmatic enlargement of the fistula, we had to use a large Amplatzer PDA occluder and the protruding part of the PDA device disk had to be covered with a carotid stent. The fistula was completely closed. The patient stopped having symptoms and, 2 years after the procedure, the effect persists.     

Folate metabolism genes, vegetable intake and renal cancer risk in central Europe

In a multicenter case-control study of renal cell carcinoma (RCC) conducted in central and eastern Europe, we reported a strong inverse association with high vegetable intake and RCC risk. The odds ratio (OR) for high compared to the lowest tertile of vegetable intake was OR = 0.67; (95% confidence interval (CI): 0.53-0.83; p-trend < 0.001). We hypothesized that variation in key folate metabolism genes may modify this association. Common variation in 5 folate metabolism genes (CBS: Ex9+33C > T (rs234706), Ex13 +41C > T (rs1801181), Ex18 -391 G > A (rs12613); MTHFR: A222V Ex5+79C > T (rs1801133), Ex8-62A > C (rs1801131); MTR: Ex26 20A > G (rs1805087), MTRR: Ex5+136 T > C (rs161870), and TYMS:IVS2-405 C > T (rs502396), Ex8+157 C > T (rs699517), Ex8+227 A > G (rs2790)) were analyzed among 1,097 RCC cases and 1,555 controls genotyped in this study. Having at least 1 variant T allele of MTHFR A222V was associated with higher RCC risk compared to those with 2 common (CC) alleles (OR = 1.44; 95% CI: 1.17-1.77; p = 0.001). After stratification by tertile of vegetable intake, the higher risk associated with the variant genotype was only observed in the low and medium tertiles (p-trend = 0.001), but not among those in the highest tertile (p-interaction = 0.22). The association remained robust after calculation of the false discovery rate (FDR = 0.05). Of the 3 TYMS SNPs examined, only the TYMS IVS2 -405 C (rs502396) variant was associated with a significantly lower risk compared to the common genotype (OR = 0.73; 95% CI: 0.57-0.93). Vegetable intake modified the association between all 3 TYMS SNPs and RCC risk (p-interaction < 0.04 for all). In summary, these findings suggest that common variation in MTHFR and TYMS genes may be associated with RCC risk, particularly when vegetable intake is low.     

The effect of trimetazidine on left ventricular systolic function and physical tolerance in patients with ischaemic cardiomyopathy

OBJECTIVE: Trimetazidine (TMZ) is the first of novel antianginal drugs with a cardioprotective effect, selectively inhibiting mitochondrial long-chain 3-ketoacyl coenzyme A thiolase. This study tested the hypothesis that the cytoprotective beneficial effect of this agent can lead to the improvement of left ventricular (LV) systolic function and tolerance to physical activity in patients with ischaemic cardiomyopathy. METHODS AND RESULTS: In 82 consecutive patients with ischaemic cardiomyopathy, a subgroup of patients (n = 42) was assigned to receive a modified form of TMZ (35 mg twice daily) in addition to the conventional therapy for the duration of three months. All patients underwent clinical, echocardiographic examination and a six-minute walk test at baseline and after a three-month treatment. The therapy with TMZ significantly improved the functional class in these patients. Left ventricular ejection fraction (LVEF) increased by 3.5 +/- 6.72% (from 34.5 +/- 3.8% to 38.0 +/- 4.8%) in the TMZ group vs. 0.8 +/- 8.06% (from 32.4 +/- 5.6% to 33.2 +/- 5.8%) in the control group (P = 0.05). The tolerance to physical activity improved by 30.0 +/- 20.7 m in the TMZ group (from 215 +/- 17.5 m to 245 +/- 20.7 m) vs. 2.0 +/- 18.85 m (from 208.2 +/- 12.4 m to 210.2 +/- 14.2 m) in the control group (P < 0.001). CONCLUSIONS: A therapeutic intervention with TMZ in conjunction with the standard therapy, over a three-month period, is associated with an increase in LVEF and improved tolerance to physical activity in patients with ischaemic cardiomyopathy.     

Aldehyde dehydrogenases are regulators of hematopoietic stem cell numbers and B-cell development

High levels of the aldehyde dehydrogenase isoform ALDH1A1 are expressed in hematopoietic stem cells (HSCs); however, its importance in these cells remains unclear. Consistent with an earlier report, we find that loss of ALDH1A1 does not affect HSCs. Intriguingly, however, we find that ALDH1A1 deficiency is associated with increased expression of the ALDH3A1 isoform, suggesting its potential to compensate for ALDH1A1. Mice deficient in ALDH3A1 have a block in B-cell development as well as abnormalities in cell cycling, intracellular signaling, and gene expression. Early B cells from these mice exhibit excess reactive oxygen species and reduced metabolism of reactive aldehydes. Mice deficient in both ALDH3A1 and ALDH1A1 have reduced numbers of HSCs as well as aberrant cell cycle distribution, increased reactive oxygen species levels, p38 mitogen-activated protein kinase activity and sensitivity to DNA damage. These findings demonstrate that ALDH3A1 can compensate for ALDH1A1 in bone marrow and is important in B-cell development, both ALDH1A1 and 3A1 are important in HSC biology; and these effects may be due, in part, to changes in metabolism of reactive oxygen species and reactive aldehydes.     

A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions

Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

The design of robust and effective medical therapies, drug targeting strategies, and bioengineering relies on a systems level understanding of biology. To this end, metabolic networks and annotated genomes are often used to gain a holistic picture of the cell functions. However, not all metabolic capabilities of cells are known, i.e., all known genomes are missing functional annotations for a relatively high portion of the open reading frames. For example, one of the best characterized organisms, Escherichia coli, lacks annotation for ∼1,600 genes, which represents 35% of its total number of genes (1). A limited knowledge of cell function is especially troublesome in infectious pathogens and organisms that could be used as a chassis in the industry to produce valuable compounds. Systematically identifying missing metabolic capabilities of the cell and accelerating the functional annotation of genomes can expedite and facilitate a wide range of medical and biotechnology applications.The systematic analysis of metabolic functions and identification of knowledge gaps relies on computational models of metabolism. In fact, all known metabolic functions of different organisms are organized into databases termed genome-scale models (GEMs). These GEMs rely on the functional annotation of genes for their reconstruction, with better quality gene annotation leading to better predictions of cellular physiology. GEMs have been widely used to study the metabolism of model organisms, such as E. coli (2) and yeast (3), and pathogens such as Salmonella Typhimurium (4) and Plasmodium falciparum (5), and to identify host–pathogen interactions (6), drug targets (7), and metabolic engineering strategies (8), among others (9). Hence, the fact that all GEMs are currently missing knowledge and annotations can lead to false predictions that can affect both research as well as biomedical applications. Approaches to performing functional annotation of genomes involve both physical experiments (10) (e.g., in vitro assays) and bioinformatics (11) (e.g., sequence similarity). However, experiments require specific hypotheses and are time- and resource-consuming. Moreover, sequence similarity (12) and other computational approaches are so far limited to the space of known annotated proteins and biochemistry.Exploring the space of unknown biochemistry is thus necessary to accelerate our understanding of cell function and include novel chemistry in our models of cells. The strategies to explore such unknown biochemical space are primarily based on machine learning (ML) or mechanistic approaches (13, 14). Recently, an ATLAS of Biochemistry was constructed based on a mechanistic understanding of enzyme function (15, 16) as a database of novel biochemistry, meaning not yet experimentally observed reactions, and the optimization-based exploration of metabolic models to identify missing biochemistry. The ATLAS of Biochemistry includes over 150,000 putative reactions between known metabolites. Hence, it represents the upper limit of the possible biochemical space and allows an efficient exploration of the uncharacterized metabolic functions in cells. Furthermore, the tool BridgIT was recently developed as a method to map orphan biochemistry to enzymes (17), providing a tool for identifying uncharacterized genes. Together, these are separate tools for exploring the unknown biochemistry of GEMs at the reaction and the enzyme level, respectively, but are not currently integrated.Therefore, we hypothesized that we use GEMs and leverage the potential of the ATLAS of Biochemistry (15, 16) coupled with BridgIT (17) to identify metabolic gaps and identify possible reactions with associated catalyzing enzymes and genes. This powerful combination of tools and methods came together to form our workflow, Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame). We applied NICEgame to suggest novel biochemistry in E. coli strain MG1655 and further enhance its genome annotation. From the most recently published E. coli GEM (2), NICEgame identified metabolic gaps that are responsible for 148 false gene essentiality predictions linked to 152 reactions in glucose minimal media. This refers to genes that the GEM considers essential for growth, but experimental data shows otherwise, meaning that there should be available biochemistry in the cell to perform these reactions in the case of gene knockout. We proposed 77 biochemical reactions linked to 35 candidate genes to fill 47% of these gaps. We integrated this information into a thermodynamically curated GEM of E. coli, iEcoMG1655, which has an increased gene essentiality prediction accuracy of 23.6% with respect to its predecessor iML1515 (2). Importantly, the NICEgame workflow is applicable to any organism or cell with a GEM and is available as a GitHub repository (https://github.com/EPFL-LCSB/NICEgame) with the combined use of available online resources, the ATLAS of Biochemistry (15, 16) and BridgIT (17). Overall, NICEgame is a workflow for the rapid and systematic identification of metabolic gaps, missing biochemistry, and candidate catalyzing genes. Hence it will accelerate the complete identification of metabolic functions and annotation of genomes and enable the design of robust bioengineering and drug targeting strategies.