Application of combined omics platforms to accelerate biomedical discovery in diabesity

Distinct Lipidomic Signatures in People Living With HIV: Combined Analysis of ACTG 5260s and MACS/WIHS

CONTEXT

Disentangling contributions of HIV from antiretroviral therapy (ART) and understanding the effects of different ART on metabolic complications in persons living with HIV (PLHIV) has been challenging.

OBJECTIVE

We assessed the effect of untreated HIV infection as well as different antiretroviral therapy (ART) on the metabolome/lipidome.

METHODS

Widely targeted plasma metabolomic and lipidomic profiling was performed on HIV-seronegative individuals and people living with HIV (PLHIV) before and after initiating ART (tenofovir/emtricitabine plus atazanavir/ritonavir [ATV/r] or darunavir/ritonavir [DRV/r] or raltegravir [RAL]). Orthogonal partial least squares discriminant analysis was used to assess metabolites/lipid subspecies that discriminated between groups. Graphical lasso estimated group-specific metabolite/lipid subspecies networks associated with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Correlations between inflammatory markers and metabolites/lipid subspecies were visualized using heat maps.

RESULTS

Of 435 participants, 218 were PLHIV. Compared to HIV-seronegative individuals, ART-naive PLHIV exhibited higher levels of saturated triacylglycerols/triglycerides (TAGs) and 3-hydroxy-kynurenine, lower levels of unsaturated TAGs and N-acetyl-tryptophan, and a sparser and less heterogeneous network of metabolites/lipid subspecies associated with HOMA-IR. PLHIV on RAL vs ATV/r or DRV/r had lower saturated and unsaturated TAGs. Positive correlations were found between medium-long chain acylcarnitines (C14-C6 ACs), palmitate, and HOMA-IR for RAL but not ATV/r or DRV/r. Stronger correlations were seen for TAGs with interleukin 6 and high-sensitivity C-reactive protein after RAL vs ATV/r or DRV/r initiation; these correlations were absent in ART-naive PLHIV.

CONCLUSION

Alterations in the metabolome/lipidome suggest increased lipogenesis for ART-naive PLHIV vs HIV-seronegative individuals, increased TAG turnover for RAL vs ATV/r or DRV/r, and increased inflammation associated with this altered metabolome/lipidome after initiating ART. Future studies are needed to understand cardiometabolic consequences of lipogenesis and inflammation in PLHIV.

Obesity Connected Metabolic Changes in Type 2 Diabetic Patients Treated With Metformin

Metformin is widely used in the treatment of Type 2 Diabetes Mellitus (T2DM). However, it is known to have beneficial effects in many other conditions, including obesity and cancer. In this study, we aimed to investigate the metabolic effect of metformin in T2DM and its impact on obesity. A mass spectrometry (MS)-based metabolomics approach was used to analyze samples from two cohorts, including healthy lean and obese control, and lean as well as obese T2DM patients on metformin regimen in the last 6 months. The results show a clear group separation and sample clustering between the study groups due to both T2DM and metformin administration. Seventy-one metabolites were dysregulated in diabetic obese patients (30 up-regulated and 41 down-regulated), and their levels were unchanged with metformin administration. However, 30 metabolites were dysregulated (21 were up-regulated and 9 were down-regulated) and then restored to obese control levels by metformin administration in obese diabetic patients. Furthermore, in obese diabetic patients, the level of 10 metabolites was dysregulated only after metformin administration. Most of these dysregulated metabolites were dipeptides, aliphatic amino acids, nucleic acid derivatives, and urea cycle components. The metabolic pattern of 62 metabolites was persistent, and their levels were affected by neither T2DM nor metformin in obesity. Interestingly, 9 metabolites were significantly dysregulated between lean and obese cohorts due to T2DM and metformin regardless of the obesity status. These include arginine, citrulline, guanidoacetic acid, proline, alanine, taurine, 5-hydroxyindoleacetic acid, and 5-hydroxymethyluracil. Understanding the metabolic alterations taking place upon metformin treatment would shed light on possible molecular targets of metformin, especially in conditions like T2DM and obesity.

Plasma sphingomyelins increase in pre-diabetic Korean men with abdominal obesity

Abdominal or visceral obesity is a well-known risk factor for metabolic diseases. However, whether abdominal obesity significantly affects plasma lipid profile during the development of type 2 diabetes has not been fully elucidated. We investigated the differences in plasma lipid concentrations in 63 participants categorized into six groups (middle-aged Korean men); Normal, Pre-diabetes (pre-DM), and Diabetes mellitus (DM) with or without abdominal obesity (AO or lean). The lipidomic profiles were determined by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Sphingomyelin (SM) levels in plasma were significantly higher in the pre-DM with AO than in pre-DM with lean (p = 0.021). SM concentrations correlated positively with waist-to-hip ratio (WHR) (r = 0.256, p = 0.044), cholesteryl ester (CE) (r = 0.483, p < 0.0001), ceramide (r = 0.489, p < 0.0001) and plasmanyl phosphatidylcholine (PC) (r = 0.446, p < 0.0001). The present study found that pre-diabetic patients with AO were characterized by increased plasma concentrations of SM. Plasma SM levels in individuals with AO may be an early prognostic biomarker to better predict the progression toward type 2 diabetes and metabolic syndrome.

Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes

Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different ¹³C-enriched carbon sources (randomized 95% ¹²C and 95% ¹³C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Culturomics: A New Kid on the Block of OMICS to Enable Personalized Medicine

This innovation analysis highlights the underestimated and versatile potential of the new field of culturomics and examines its relation to other OMICS system sciences such as infectiomics, metabolomics, phenomics, and pharmacomicrobiomics. The advent of molecular biology, followed by the emergence of various disciplines of the genomics, and most importantly metagenomics, brought about the sharp decline of conventional microbiology methods. Emergence of culturomics has a natural synergy with therapeutic and clinical genomic approaches so as to realize personalized medicine. Notably, the concept of culturomics expands on that of phenomics and allows a reintroduction of the culture-based phenotypic characterization into the 21st century research repertoire, bolstered by robust technology for automated and massive execution, but its potential is largely unappreciated at present; the few available references show unenthusiastic pursuit and in narrow applications. This has not to be so: depending on the specific brand of culturomics, the scope of applications may extend to medicine, agriculture, environmental sciences, pharmacomicrobiomics, and biotechnology innovation. Moreover, culturomics may produce Big Data. This calls for a new generation of data scientists and innovative ways of harnessing and valorizing Big Data beyond classical genomics. Much more detailed and objective classification and identification of microbiota may soon be at hand through culturomics, thus enabling precision diagnosis toward truly personalized medicine. Culturomics may both widen the scope of microbiology and improve its contributions to diagnostics and personalized medicine, characterizing microbes and determining their associations with health and disease dynamics.

Veterinary Medicine and Omics (Veterinomics): Metabolic Transition of Milk Triacylglycerol Synthesis in Sows from Late Pregnancy to Lactation

Mammalian milk is a key source of lipids, providing not only important calories but also essential fatty acids. Veterinary medicine and omics systems sciences intersection, termed as "veterinomics" here, has received little attention to date but stands to offer much promise for building bridges between human and animal health. We determined the changes in porcine mammary genes and proteomics expression associated with milk triacylglycerol (TAG) synthesis and secretion from late pregnancy to lactation. TAG content and fatty acid (FA) composition were determined in porcine colostrum (the 1st day of lactation) and milk (the 17th day of lactation). The mammary transcriptome for 70 genes and 13 proteins involved in TAG synthesis and secretion from six sows, each at d -17(late pregnancy), d 1(early lactation), and d 17 (peak lactation) relative to parturition were analyzed using quantitative real-time PCR and Western blot analyses. The TAG content and the concentrations of de novo synthesized FAs, saturated FAs, and monounsaturated FAs were higher in milk than in colostrum (p<0.05). Robust upregulation with high relative mRNA abundance was evident during lactation for genes associated with FA uptake (VLDLR, LPL, CD36), FA activation (ACSS2, ACSL3), and intracellar transport (FABP3), de novo FA synthesis (ACACA, FASN), FA elongation (ELOVL1), FA desaturation (SCD, FADS1), TAG synthesis (GPAM, AGPAT1, LPIN1, DGAT1), lipid droplet formation (BTN2A1, XDH, PLIN2), and transcription factors and nuclear receptors (SREBP1, SCAP, INSIG1/2). In conclusion, a wide variety of lipogenic genes and proteins regulate the channeling of FAs towards milk TAG synthesis and secretion in porcine mammary gland tissue. These findings inform future omics strategies to increase milk fat production and lipid profile and attest to the rise of both veterinomics and lipidomics in postgenomics life sciences.

Advanced mass spectrometry-based multi-omics technologies for exploring the pathogenesis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the primary hepatic malignancies and is the third most common cause of cancer related death worldwide. Although a wealth of knowledge has been gained concerning the initiation and progression of HCC over the last half century, efforts to improve our understanding of its pathogenesis at a molecular level are still greatly needed, to enable clinicians to enhance the standards of the current diagnosis and treatment of HCC. In the post-genome era, advanced mass spectrometry driven multi-omics technologies (e.g., profiling of DNA damage adducts, RNA modification profiling, proteomics, and metabolomics) stand at the interface between chemistry and biology, and have yielded valuable outcomes from the study of a diversity of complicated diseases. Particularly, these technologies are being broadly used to dissect various biological aspects of HCC with the purpose of biomarker discovery, interrogating pathogenesis as well as for therapeutic discovery. This proof of knowledge-based critical review aims at exploring the selected applications of those defined omics technologies in the HCC niche with an emphasis on translational applications driven by advanced mass spectrometry, toward the specific clinical use for HCC patients. This approach will enable the biomedical community, through both basic research and the clinical sciences, to enhance the applicability of mass spectrometry-based omics technologies in dissecting the pathogenesis of HCC and could lead to novel therapeutic discoveries for HCC.

Isotopic Ratio Outlier Analysis of the S. cerevisiae Metabolome Using Accurate Mass Gas Chromatography/Time-of-Flight Mass Spectrometry: A New Method for Discovery

Isotopic ratio outlier analysis (IROA) is a (13)C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% (13)C, or 5%(13)C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%(13)C extracts, or light isotopologues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the (12)C monoisotopic and the (13)C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study.

The "Sapienza University Mortality and Morbidity Event Rate (SUMMER) study in diabetes": Study protocol

BACKGROUND AND AIMS

The rate of mortality in diabetic patients, especially of cardiovascular origin, is about twice as much that of nondiabetic individuals. Thus, the pathogenic factors shaping the risk of mortality in such patients must be unraveled in order to target intensive prevention and treatment strategies. The "Sapienza University Mortality and Morbidity Event Rate (SUMMER) study in diabetes" is aimed at identifying new molecular promoters of mortality and major vascular events in patients with type 2 diabetes mellitus (T2DM).

METHODS/DESIGN

The "SUMMER study in diabetes" is an observational, prospective, and collaborative study conducted on at least 5000 consecutive patients with T2DM, recruited from several diabetes clinics of Central-Southern Italy and followed up for a minimum of 5 years. The primary outcome is all-cause mortality; the secondary outcomes are cardiovascular mortality, acute myocardial infarction, stroke, and dialysis. A biobank will be created for genomic, transcriptomic, and metabolomic analysis, in order to unravel new molecular predictors of mortality and vascular morbidity.

DISCUSSION

The "SUMMER study in diabetes" is aimed at identifying new molecular promoters of mortality and major vascular events in patients with T2DM. These novel pathogenic factors will most likely be instrumental in unraveling new pathways underlying such dramatic events. In addition, they will also be used as additional markers to increase the performance of the already existing risk-scoring models for predicting the above-mentioned outcomes in T2DM, as well as for setting up new preventive and treatment strategies, possibly tailored to specific pathogenic backgrounds.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02311244; URL https://clinicaltrials.gov/ct2/show/NCT02311244?term=SUMMER&rank=5.

Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes

Gut microbiota has recently been proposed as a crucial environmental factor in the development of metabolic diseases such as obesity and type 2 diabetes, mainly due to its contribution in the modulation of several processes including host energy metabolism, gut epithelial permeability, gut peptide hormone secretion, and host inflammatory state. Since the symbiotic interaction between the gut microbiota and the host is essentially reflected in specific metabolic signatures, much expectation is placed on the application of metabolomic approaches to unveil the key mechanisms linking the gut microbiota composition and activity with disease development. The present review aims to summarize the gut microbial-host co-metabolites identified so far by targeted and untargeted metabolomic studies in humans, in association with impaired glucose homeostasis and/or obesity. An alteration of the co-metabolism of bile acids, branched fatty acids, choline, vitamins (i.e., niacin), purines, and phenolic compounds has been associated so far with the obese or diabese phenotype, in respect to healthy controls. Furthermore, anti-diabetic treatments such as metformin and sulfonylurea have been observed to modulate the gut microbiota or at least their metabolic profiles, thereby potentially affecting insulin resistance through indirect mechanisms still unknown. Despite the scarcity of the metabolomic studies currently available on the microbial-host crosstalk, the data-driven results largely confirmed findings independently obtained from in vitro and animal model studies, putting forward the mechanisms underlying the implication of a dysfunctional gut microbiota in the development of metabolic disorders.

Lower Preprandial Insulin and Altered Fuel Use in HIV/Antiretroviral-Exposed Infants in Cameroon

CONTEXT

Intrauterine HIV/antiretroviral (ARV) and postnatal ARVs are known to perturb energy metabolism and could have permanent effects on future metabolic health. Such maladaptive effects could be mediated by changes in mitochondrial function and intermediary metabolism due to fetal and early-life ARV exposure in HIV/ARV-exposed uninfected (HEU) infants.

OBJECTIVE

The objective of the study was to understand the relationship(s) between mitochondrial fuel use (assessed via acylcarnitines and branched chain amino acids) and preprandial insulin in infants exposed to in utero HIV/ARV plus postnatal zidovudine or nevirapine compared with HIV/ARV-unexposed uninfected (HUU) infants.

DESIGN

This was a prospective cohort study with the following three groups: 1) intrauterine HIV/ARV/postnatal zidovudine-exposed (HEU-A), 2) intrauterine HIV/ARV/postnatal nevirapine-exposed (HEU-N), and 3) HUU infants. Principal component analysis and linear regression modeling were performed to assess the association between in utero HIV/ARV exposure and infant insulin.

SETTING

The study was conducted at Cameroonian urban antenatal centers.

PARTICIPANTS

HIV-infected and -uninfected pregnant woman/infant dyads participated in the study.

MAIN OUTCOME

Preprandial insulin was the main outcome measured.

RESULTS

Of 366 infants, 38 were HEU-A, 118 HEU-N. Forty intermediary metabolites were consolidated into seven principal components. In a multivariate analysis, both HEU-A (β = -.116, P= .012) and HEU-N (β = -.070, P= .022) demonstrated lower insulin compared with HUU infants. However, at high levels of plasma metabolites, HEU-A (β = .027, P= .050) exhibited higher insulin levels than HEU-N or HUU infants. A unique array of short-chain acylcarnitines (β = .044, P= .001) and branched-chain amino acids (β = .033, P= .012) was associated with insulin.

CONCLUSION

HEU-A and HEU-N infants have lower preprandial insulin levels at 6 weeks of age and appear to use metabolic fuel substrates differently than HUU infants. Future studies are warranted to determine whether observed differences have lasting metabolic implications, such as later insulin resistance.

Genetics, genomics and personalized medicine in Type 2 diabetes: a perspective on the Arab region

Type 2 diabetes (T2D) is a wide-spread, chronic metabolic disorder, affecting millions of people worldwide. The epidemic of diabetes has placed a huge strain on public health, longevity and economy. T2D occurs as a result of both genetic and environmental factors and is heterogeneous in its presentation across individuals. This review gives an overview of the genetic variations identified by genome-wide association studies which predispose individuals to T2D and those which are responsible for variable drug response across patients, and the necessity to adopt a personalized approach to diabetes management. We also include a perspective on diabetes in Arabs, given the high incidence of T2D and consanguineous marriages, and the need to understand associated genetic components in this vulnerable population.

The promise of metabolic phenotyping in gastroenterology and hepatology

Disease risk and treatment response are determined, at the individual level, by a complex history of genetic and environmental interactions, including those with our endogenous microbiomes. Personalized health care requires a deep understanding of patient biology that can now be measured using a range of '-omics' technologies. Patient stratification involves the identification of genetic and/or phenotypic disease subclasses that require different therapeutic strategies. Stratified medicine approaches to disease diagnosis, prognosis and therapeutic response monitoring herald a new dimension in patient care. Here, we explore the potential value of metabolic profiling as applied to unmet clinical needs in gastroenterology and hepatology. We describe potential applications in a number of diseases, with emphasis on large-scale population studies as well as metabolic profiling on the individual level, using spectrometric and imaging technologies that will leverage the discovery of mechanistic information and deliver novel health care solutions to improve clinical pathway management.

Integrative Metabolic Signatures for Hepatic Radiation Injury

BACKGROUND

Radiation-induced liver disease (RILD) is a dose-limiting factor in curative radiation therapy (RT) for liver cancers, making early detection of radiation-associated liver injury absolutely essential for medical intervention. A metabolomic approach was used to determine metabolic signatures that could serve as biomarkers for early detection of RILD in mice.

METHODS

Anesthetized C57BL/6 mice received 0, 10 or 50 Gy Whole Liver Irradiation (WLI) and were contrasted to mice, which received 10 Gy whole body irradiation (WBI). Liver and plasma samples were collected at 24 hours after irradiation. The samples were processed using Gas Chromatography/Mass Spectrometry and Liquid Chromatography/Mass Spectrometry.

RESULTS

Twenty four hours after WLI, 407 metabolites were detected in liver samples while 347 metabolites were detected in plasma. Plasma metabolites associated with 50 Gy WLI included several amino acids, purine and pyrimidine metabolites, microbial metabolites, and most prominently bradykinin and 3-indoxyl-sulfate. Liver metabolites associated with 50 Gy WLI included pentose phosphate, purine, and pyrimidine metabolites in liver. Plasma biomarkers in common between WLI and WBI were enriched in microbial metabolites such as 3 indoxyl sulfate, indole-3-lactic acid, phenyllactic acid, pipecolic acid, hippuric acid, and markers of DNA damage such as 2-deoxyuridine. Metabolites associated with tryptophan and indoles may reflect radiation-induced gut microbiome effects. Predominant liver biomarkers in common between WBI and WLI were amino acids, sugars, TCA metabolites (fumarate), fatty acids (lineolate, n-hexadecanoic acid) and DNA damage markers (uridine).

CONCLUSIONS

We identified a set of metabolomic markers that may prove useful as plasma biomarkers of RILD and WBI. Pathway analysis also suggested that the unique metabolic changes observed after liver irradiation was an integrative response of the intestine, liver and kidney.

Biomarkers of insulin sensitivity and insulin resistance: Past, present and future

Insulin resistance in insulin target tissues including liver, skeletal muscle and adipose tissue is an early step in the progression towards type 2 diabetes. Accurate diagnostic parameters reflective of insulin resistance are essential. Longstanding tests for fasting blood glucose and HbA1c are useful and although the hyperinsulinemic euglycemic clamp remains a "gold standard" for accurately determining insulin resistance, it cannot be implemented on a routine basis. The study of adipokines, and more recently myokines and hepatokines, as potential biomarkers for insulin sensitivity is now an attractive and relatively straightforward approach. This review discusses potential biomarkers including adiponectin, RBP4, chemerin, A-FABP, FGF21, fetuin-A, myostatin, IL-6, and irisin, all of which may play significant roles in determining insulin sensitivity. We also review potential future directions of new biological markers for measuring insulin resistance, including metabolomics and gut microbiome. Collectively, these approaches will provide clinicians with the tools for more accurate, and perhaps personalized, diagnosis of insulin resistance.

Metabolomics in the developmental origins of obesity and its cardiometabolic consequences

In this review, we discuss the potential role of metabolomics to enhance understanding of obesity-related developmental origins of health and disease (DOHaD). We first provide an overview of common techniques and analytical approaches to help interested investigators dive into this relatively novel field. Next, we describe how metabolomics may capture exposures that are notoriously difficult to quantify, and help to further refine phenotypes associated with excess adiposity and related metabolic sequelae over the life course. Together, these data can ultimately help to elucidate mechanisms that underlie fetal metabolic programming. Finally, we review current gaps in knowledge and identify areas where the field of metabolomics is likely to provide insights into mechanisms linked to DOHaD in human populations.

[Bardet-Biedl syndrome: cilia and obesity - from genes to integrative approaches]

The primary cilium is a specialized organelle, present at the surface of most eukaryotic cells, whose main function is to detect, integrate and transmit intra- and extra-cellular signals. Its dysfunction usually results in a group of severe clinical manifestations nowadays termed ciliopathies. The latter can be of syndromic nature with multi-organ dysfunctions and can also be associated with a morbid obese phenotype, like it is the case in the iconic ciliopathy, the Bardet Biedl syndrome (BBS). This review will discuss the contribution of the unique context offered by the emblematic BBS for understanding the mechanisms leading to obesity via the involvement of the primary cilium together with identification of novel molecular players and signaling pathways it has helped to highlight. In the current context of translational medicine and system biology, this article will also discuss the potential benefits and challenges posed by these techniques via multi-level approaches to better dissect the underlying mechanisms leading to the complex condition of obesity.

MicroRNA-205 targets tight junction-related proteins during urothelial cellular differentiation

The mammalian bladder urothelium classified as basal, intermediate, and terminally differentiated umbrella cells offers one of the most effective permeability barrier functions known to exist in nature because of the formation of apical uroplakin plaques and tight junctions. To improve our understanding of urothelial differentiation, we analyzed the microRNA (miRNA) expression profiles of mouse urinary tissues and by TaqMan miRNA analysis of microdissected urothelial layers and in situ miRNA-specific hybridization to determine the dependence of these miRNAs on the differentiation stage. Our in situ hybridization studies revealed that miR-205 was enriched in the undifferentiated basal and intermediate cell layers. We then used a quantitative proteomics approach to identify miR-205 target genes in primary cultured urothelial cells subjected to antagomir-mediated knockdown of specific miRNAs. Twenty-four genes were reproducibly regulated by miR-205; eleven of them were annotated as cell junction- and tight junction-related molecules. Western blot analysis demonstrated that antagomir-induced silencing of miR-205 in primary cultured urothelial cells elevated the expression levels of Tjp1, Cgnl1, and Cdc42. Ectopic expression of miR-205 in MDCK cells inhibited the expression of tight junction proteins and the formation of tight junctions. miR-205- knockdown urothelial cells showed alterations in keratin synthesis and increases of uroplakin Ia and Ib, which are the urothelial differentiation products. These results suggest that miR-205 may contribute a role in regulation of urothelial differentiation by modulating the expression of tight junction-related molecules.

Relieving the cardiometabolic disease burden: a perspective on phytometabolite functional and chemical annotation for diabetes management

Type 2 diabetes (T2D) is both a complex, multifactorial disease state and an unsolved, intensifying public-health problem. To help reduce disease burden, some T2D patients have embraced plant-derived substances for use with - if not in place of - prescription medicines, a trend based mainly upon historical precedent and anecdotal observations of human health benefit. Preclinical research has emphasized phytometabolite interactions with purported T2D pathogenic targets and the effects of botanical preparations on experimental T2D symptomology as induced in laboratory animals. More holistic, systems-oriented profiling of phytochemicals with functional-biology, omics, and chemical-fingerprinting tools now appears necessary to increase our appreciation of phytometabolite actions potentially beneficial to the T2D patient. The resultant, multidimensional view of phytometabolite pharmacology should help provide a more rational basis for evaluating the potential of natural plant products as T2D pharmacotherapy. Such information may also help substantiate and legitimize (pre)clinical demonstrations of phytochemical health benefits, advance our understanding of T2D pathogenesis, and offer scope for better T2D medicines. Public-private partnerships are invoked for conducting this research with the ultimate aim of improving the global cardiometabolic profile.

Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.