Stefano Cacciatore

Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer

Earlier detection of patients with metastatic colorectal cancer (mCRC) might improve their treatment and survival outcomes. In this study, we used proton nuclear magnetic resonance ((1)H-NMR) to profile the serum metabolome in patients with mCRC and determine whether a disease signature may exist that is strong enough to predict overall survival (OS). In 153 patients with mCRC and 139 healthy subjects from three Danish hospitals, we profiled two independent sets of serum samples in a prospective phase II study. In the training set, (1)H-NMR metabolomic profiling could discriminate patients with mCRC from healthy subjects with a cross-validated accuracy of 100%. In the validation set, 96.7% of subjects were correctly classified. Patients from the training set with maximally divergent OS were chosen to construct an OS predictor. After validation, patients predicted to have short OS had significantly reduced survival (HR, 3.4; 95% confidence interval, 2.06-5.50; P = 1.33 × 10(-6)). A number of metabolites concurred with the (1)H-NMR fingerprint of mCRC, offering insights into mCRC metabolic pathways. Our findings establish that (1)H-NMR profiling of patient serum can provide a strong metabolomic signature of mCRC and that analysis of this signature may offer an independent tool to predict OS.

AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry-based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our findings show how prostate tumors undergo a metabolic reprogramming that reflects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.

In vitro fermentation of potential prebiotic flours from natural sources: Impact on the human colonic microbiota and metabolome

SCOPE Fibers and prebiotics represent a useful dietary approach for modulating the human gut microbiome. Therefore, aim of the present study was to investigate the impact of four flours (wholegrain rye, wholegrain wheat, chickpeas and lentils 50:50, and barley milled grains), characterized by a naturally high content in dietary fibers, on the intestinal microbiota composition and metabolomic output. METHODS AND RESULTS A validated three-stage continuous fermentative system simulating the human colon was used to resemble the complexity and diversity of the intestinal microbiota. Fluorescence in situ hybridization was used to evaluate the impact of the flours on the composition of the microbiota, while small-molecule metabolome was assessed by NMR analysis followed by multivariate pattern recognition techniques. HT29 cell-growth curve assay was used to evaluate the modulatory properties of the bacterial metabolites on the growth of intestinal epithelial cells. All the four flours showed positive modulations of the microbiota composition and metabolic activity. Furthermore, none of the flours influenced the growth-modulatory potential of the metabolites toward HT29 cells. CONCLUSION Our findings support the utilization of the tested ingredients in the development of a variety of potentially prebiotic food products aimed at improving gastrointestinal health.

Innovation in metabolomics to improve personalized healthcare

Metabolomics is the systemic study of all small molecules (metabolites) and their concentration as affected by pathological and physiological alterations or environmental or other factors. Metabolic alterations represent a “window” on the complex interactions between genetic expression, enzyme activity, and metabolic reactions. Techniques, including nuclear magnetic resonance spectroscopy, mass spectrometry, Fourier‐transform infrared, and Raman spectroscopy, have led to significant advances in metabolomics. The field is shifting from feasibility studies to biological and clinical applications. Fields of application range from cancer biology to stem cell research and assessment of xenobiotics and drugs in tissues and single cells. Cross‐validation across high‐throughput platforms has allowed findings from expression profiling to be confirmed with metabolomics. Specific genetic alterations appear to drive unique metabolic programs. These, in turn, can be used as biomarkers of genetic subtypes of prostate cancer or as discovery tools for therapeutic targeting of metabolic enzymes. Thus, metabolites in blood may serve as biomarkers of tumor state, including inferring driving oncogenes. Novel applications such as these suggest that metabolic profiling may be utilized in refining personalized medicine.

Unbiased Metabolomic Investigation of Alzheimer’s Disease Brain Points to Dysregulation of Mitochondrial Aspartate Metabolism

Alzheimers disease (AD) is the most common cause of adult dementia. Yet the complete set of molecular changes accompanying this inexorable, neurodegenerative disease remains elusive. Here we adopted an unbiased lipidomics and metabolomics approach to surveying frozen frontal cortex samples from clinically characterized AD patients (n = 21) and age-matched controls (n = 19), revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, we incorporated the novel molecular information into the known biochemical pathways and compared it with the results of a metabolomics meta-analysis of previously published AD research. We found six metabolic pathways of the central metabolism as well as glycerophospholipid metabolism predominantly altered in AD brains. Using targeted metabolomics approaches and MS imaging, we confirmed a marked dysregulation of mitochondrial aspartate metabolism. The altered metabolic pathways were further integrated with clinical data, showing various degrees of correlation with parameters of dementia and AD pathology. Our study highlights specific, altered biochemical pathways in the brains of individuals with AD compared with those of control subjects, emphasizing dysregulation of mitochondrial aspartate metabolism and supporting future venues of investigation.

Relationship between vaginal microbial dysbiosis, inflammation, and pregnancy outcomes in cervical cerclage

Cervical cerclage using braided suture material disrupts vaginal microbial stability and increases inflammation. A (monofilament) stitch in time Cervical cerclage, a procedure that uses suture to reinforce the cervical opening, is frequently used to reduce the risk of preterm delivery in women with a history of previous preterm birth or short cervical length. Either monofilament or braided suture can be used for cerclage, but braided is more commonly selected because of its mechanical strength and easier application. A large clinical study by Kindinger et al. now shows that braided cerclage increases the risk of preterm birth and intrauterine death compared to monofilament suture. The authors also found that the braided suture is more conducive to bacterial colonization and increases the risk of vaginal dysbiosis and inflammation, helping to explain the clinical findings. Preterm birth, the leading cause of death in children under 5 years, may be caused by inflammation triggered by ascending vaginal infection. About 2 million cervical cerclages are performed annually to prevent preterm birth. The procedure is thought to provide structural support and maintain the endocervical mucus plug as a barrier to ascending infection. Two types of suture material are used for cerclage: monofilament or multifilament braided. Braided sutures are most frequently used, although no evidence exists to favor them over monofilament sutures. We assessed birth outcomes in a retrospective cohort of 678 women receiving cervical cerclage in five UK university hospitals and showed that braided cerclage was associated with increased intrauterine death (15% versus 5%; P = 0.0001) and preterm birth (28% versus 17%; P = 0.0006) compared to monofilament suture. To understand the potential underlying mechanism, we performed a prospective, longitudinal study of the vaginal microbiome in women at risk of preterm birth because of short cervical length (≤25 mm) who received braided (n = 25) or monofilament (n = 24) cerclage under comparable circumstances. Braided suture induced a persistent shift toward vaginal microbiome dysbiosis characterized by reduced Lactobacillus spp. and enrichment of pathobionts. Vaginal dysbiosis was associated with inflammatory cytokine and interstitial collagenase excretion into cervicovaginal fluid and premature cervical remodeling. Monofilament suture had comparatively minimal impact upon the vaginal microbiome and its interactions with the host. These data provide in vivo evidence that a dynamic shift of the human vaginal microbiome toward dysbiosis correlates with preterm birth.

Knowledge discovery by accuracy maximization

Significance We propose an innovative method to extract new knowledge from noisy and high-dimensional data. Our approach differs from previous methods in that it has an integrated procedure of validation of the results through maximization of cross-validated accuracy. In many cases, this method performs better than existing feature extraction methods and offers a general framework for analyzing any kind of complex data in a broad range of sciences. Examples ranging from genomics and metabolomics to astronomy and linguistics show the versatility of the method. Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold’s topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan’s presidency and not from its beginning.

The effect of gestational age and cervical length measurements in the prediction of spontaneous preterm birth in twin pregnancies: an individual patient level meta-analysis.

To assess the effect of gestational age (GA) and cervical length (CL) measurements at transvaginal ultrasound (TVUS) in the prediction of preterm birth in twin pregnancy.

Metabolic Profiling in Formalin-Fixed and Paraffin Embedded Prostate Cancer Tissues

Metabolite profiling has significantly contributed to a deeper understanding of the biochemical metabolic networks and pathways in cancer cells. Metabolomics-based biomarker discovery would greatly benefit from the ability to interrogate retrospective annotated clinical specimens archived as formalin-fixed, paraffin-embedded (FFPE) material. Mass spectrometry–based metabolomic analysis was performed in matched frozen and FFPE human prostate cancers as well as isogenic prostate cancer cell lines. A total of 352 and 460 metabolites were profiled in human tissues and cell lines, respectively. Classes and physical–chemical characteristics of the metabolites preserved in FFPE material were characterized and related to their preservation or loss following fixation and embedding. Metabolite classes were differentially preserved in archival FFPE tissues, regardless of the age of the block, compared with matched frozen specimen, ranging from maximal preservation of fatty acids (78%) to loss of the majority of peptides and steroids. Generally, FFPE samples showed a decrease of metabolites with functional groups, such as carboxamide. As an adjunct technique, metabolic profiles were also obtained in situ from FFPE tissue sections where metabolites were extracted in a manner that preserves tissue architecture. Despite the fact that selected metabolites were not retained after processing, global metabolic profiles obtained from FFPE can be used to predict biologic states and study biologic pathways. These results pave the way for metabolomics-based biomarker discovery/validation utilizing retrospective and clinically annotated FFPE collections. Implications: Metabolic profiles can be performed in archival tissue and may be used to complement other profiling methods such as gene expression for biomarker discovery or pathway analysis in the assessment of biologic states. Mol Cancer Res; 15(4); 439–47. ©2017 AACR.

Modeling hormonal and inflammatory contributions to preterm and term labor using uterine temporal transcriptomics

BackgroundPreterm birth is now recognized as the primary cause of infant mortality worldwide. Interplay between hormonal and inflammatory signaling in the uterus modulates the onset of contractions; however, the relative contribution of each remains unclear. In this study we aimed to characterize temporal transcriptome changes in the uterus preceding term labor and preterm labor (PTL) induced by progesterone withdrawal or inflammation in the mouse and compare these findings with human data.MethodsMyometrium was collected at multiple time points during gestation and labor from three murine models of parturition: (1) term gestation; (2) PTL induced by RU486; and (3) PTL induced by lipopolysaccharide (LPS). RNA was extracted and cDNA libraries were prepared and sequenced using the Illumina HiSeq 2000 system. Resulting RNA-Seq data were analyzed using multivariate modeling approaches as well as pathway and causal network analyses and compared against human myometrial transcriptome data.ResultsWe identified a core set of temporal myometrial gene changes associated with term labor and PTL in the mouse induced by either inflammation or progesterone withdrawal. Progesterone withdrawal initiated labor without inflammatory gene activation, yet LPS activation of uterine inflammation was sufficient to override the repressive effects of progesterone and induce a laboring phenotype. Comparison of human and mouse uterine transcriptomic datasets revealed that human labor more closely resembles inflammation-induced PTL in the mouse.ConclusionsLabor in the mouse can be achieved through inflammatory gene activation yet these changes are not a requisite for labor itself. Human labor more closely resembles LPS-induced PTL in the mouse, supporting an essential role for inflammatory mediators in human “functional progesterone withdrawal.” This improved understanding of inflammatory and progesterone influence on the uterine transcriptome has important implications for the development of PTL prevention strategies.