Sharpening Precision Medicine by a Thorough Interrogation of Metabolic Individuality

Metabolic determinants of leukemia onset variability in genetically homogeneous AKR mice

Leukemia is a complex disease shaped by the intricate interplay of genetic and environmental factors. Given our preliminary data showing different leukemia incidence in genetically homogenous AKR mice harboring the spontaneous leukemia-inducing mutation Rmcfs, we sought to unravel the role of metabolites and gut microbiota in the leukemia penetrance. Our metabolomic analysis revealed distinct serum metabolite profiles between mice that developed leukemia and those that did not. We discovered that linoleic acid (LA), an essential ω-6 polyunsaturated fatty acid, was significantly decreased in the leukemia group, with the lower levels observed starting from 25 weeks before the onset. A predictive model based on LA levels demonstrated high accuracy in predicting leukemia development (area under curve 0.82). In vitro experiment confirmed LA's cytotoxic effects against leukemia cells, and in vivo study showed that a diet enriched with LA prolonged survival in AKR mice. Furthermore, gut microbiome analysis identified specific Lachnospiraceae species, that affect host lipid metabolism, are exclusively present in the leukemia group, suggesting their potential influence on LA metabolism and leukemia development. These findings shed light on the complex relationship between metabolites, gut microbiota, and leukemia development, providing valuable insights into the role of non-genetic factors in leukemia penetrance and potential strategies for leukemia prevention.

How far are we from reliable metabolomics-based biomarkers? The often-overlooked importance of addressing inter-individual variability factors

Metabolomics has proven great potential to unravel the molecular basis of diseases. However, most attempts aimed at identifying reliable metabolomics-based biomarkers for diagnosis, prediction, and prognosis of diseases have repeatedly failed because of inconsistent results and unsatisfactory replication in independent cohorts. This review article explores the possible causes behind this reproducibility crisis, with special focus on the role that inter-individual variability factors play in modulating the susceptibility to disease development. Furthermore, we provide future perspectives on the applicability of metabolomics in biomedical research and its translatability into clinical practice.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease

BACKGROUND

Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD).

METHODS

Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (p_FDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (p_FDR < 0.05) with an imaging outcome (N = 1638).

RESULTS

In the fully adjusted model, three lipid modules were associated with a brain volume measure (p_FDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß =  - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß =  - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (p_FDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction.

CONCLUSIONS

Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Nasopharyngeal airway dual-transcriptome of infants with severe bronchiolitis and risk of childhood asthma: A multicenter prospective study

BACKGROUND

Severe bronchiolitis (ie, bronchiolitis requiring hospitalization) during infancy is a major risk factor for childhood asthma. However, the exact mechanism linking these common conditions remains unclear.

OBJECTIVES

This study sought to examine the integrated role of airway microbiome (both taxonomy and function) and host response in asthma development in this high-risk population.

METHODS

This multicenter prospective cohort study of 244 infants with severe bronchiolitis (median age, 3 months) examined the infants' nasopharyngeal metatranscriptomes (microbiomes) and transcriptomes (hosts), as well as metabolomes at hospitalization. The longitudinal relationships investigated include (1) major bacterial species (Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis), (2) microbial function, and (3) host response with risks of developing asthma by age 6 years.

RESULTS

First, the abundance of S pneumoniae was associated with greater risks of asthma (P = .01), particularly in infants with nonrhinovirus infection (Pinteraction = .04). Second, of 328 microbial functional pathways that are differentially enriched by asthma development, the top pathways (eg, fatty acid and glycolysis pathways; false discovery rate [FDR] < 1 × 10-12) were driven by these 3 major species (eg, positive association of S pneumoniae with glycolysis; FDR < 0.001). These microbial functional pathways were validated with the parallel metabolome data. Third, 104 transcriptome pathways were differentially enriched (FDR < .05)-for example, downregulated interferon-α and -γ and upregulated T-cell activation pathways. S pneumoniae was associated with most differentially expressed transcripts (eg, DAGLB; FDR < 0.05).

CONCLUSIONS

By applying metatranscriptomic, transcriptomic, and metabolomic approaches to a multicenter cohort of infants with bronchiolitis, this study found an interplay between major bacterial species, their function, and host response in the airway, and their longitudinal relationship with asthma development.

Assessing Adherence to Healthy Dietary Habits Through the Urinary Food Metabolome: Results From a European Two-Center Study

Background

Diet is one of the most important modifiable lifestyle factors in human health and in chronic disease prevention. Thus, accurate dietary assessment is essential for reliably evaluating adherence to healthy habits.

Objectives

The aim of this study was to identify urinary metabolites that could serve as robust biomarkers of diet quality, as assessed through the Alternative Healthy Eating Index (AHEI-2010).

Design

We set up two-center samples of 160 healthy volunteers, aged between 25 and 50, living as a couple or family, with repeated urine sampling and dietary assessment at baseline, and 6 and 12 months over a year. Urine samples were subjected to large-scale metabolomics analysis for comprehensive quantitative characterization of the food-related metabolome. Then, lasso regularized regression analysis and limma univariate analysis were applied to identify those metabolites associated with the AHEI-2010, and to investigate the reproducibility of these associations over time.

Results

Several polyphenol microbial metabolites were found to be positively associated with the AHEI-2010 score; urinary enterolactone glucuronide showed a reproducible association at the three study time points [false discovery rate (FDR): 0.016, 0.014, 0.016]. Furthermore, other associations were found between the AHEI-2010 and various metabolites related to the intake of coffee, red meat and fish, whereas other polyphenol phase II metabolites were associated with higher AHEI-2010 scores at one of the three time points investigated (FDR < 0.05 or β ≠ 0).

Conclusion

We have demonstrated that urinary metabolites, and particularly microbiota-derived metabolites, could serve as reliable indicators of adherence to healthy dietary habits.

Clinical Trail Registration

www.ClinicalTrials.gov, Identifier: NCT03169088.

Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort

Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography-mass spectrometry (age 60-64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed 'hub' metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = -0.10, 95% confidence interval = -0.15 to -0.052, P = 5.99 × 10-5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P < 0.05. Two modules demonstrated associations that were partly or largely attenuated by life course factors: one enriched in modified nucleosides and amino acids (overall attenuation = 39.2-55.5%), and another in vitamin A and C metabolites (overall attenuation = 68.6-92.6%). Our other findings, including a module enriched in sphingolipid pathways, were entirely explained by life course factors, particularly childhood cognition and education. Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms associated with cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.

Food and Microbiota Metabolites Associate with Cognitive Decline in Older Subjects: A 12-Year Prospective Study

SCOPE

Diet is considered an important modulator of cognitive decline and dementia, but the available evidence is, however, still fragmented and often inconsistent.

METHODS AND RESULTS

The article studies the long-term prospective Three-City Cohort, which consists of two separate nested case-control sample sets from different geographic regions (Bordeaux, n = 418; Dijon, n = 424). Cognitive decline is evaluated through five neuropsychological tests (Mini-Mental State Examination, Benton Visual Retention Test, Isaac's Set Test, Trail-Making Test part A, and Trail-Making Test part B). The food-related and microbiota-derived circulating metabolome is studied in participants free of dementia at baseline, by subjecting serum samples to large-scale quantitative metabolomics analysis. A protective association is found between metabolites derived from cocoa, coffee, mushrooms, red wine, the microbial metabolism of polyphenol-rich foods, and cognitive decline, as well as a negative association with metabolites related to unhealthy dietary components, such as artificial sweeteners and alcohol.

CONCLUSION

These results provide insight into the early metabolic events that are associated with the later risk to develop cognitive decline within the crosstalk between diet, gut microbiota and the endogenous metabolism, which can help identify potential targets for preventive and therapeutic strategies to preserve cognitive health.

A Clinical Trial for the Identification of Metabolic Biomarkers in Hashimoto's Thyroiditis and in Psoriasis: Study Protocol

Hashimoto's thyroiditis and psoriasis are inflammatory disorders that significantly impact patients' quality of life, stressing the need for novel biomarkers of early diagnosis. This randomized clinical trial (NCT04693936) aims to identify Hashimoto's thyroiditis' and psoriasis' metabolic biomarkers and to investigate the effect of environmental factors on the disease-related metabolic imprint and quality of life. Patients with Hashimoto's thyroiditis, patients with psoriasis, and healthy individuals aged 18-60 will be recruited, enrolled according to eligibility criteria (medical history, clinical thyroid markers and the PASI score) and randomized to two groups. The intervention group will receive a combination of nutraceuticals for 6 months as part of a Mediterranean diet, and the control group will follow their usual diet. Data will be collected at baseline and the end of the study, including metabolite levels, lifestyle and anthropometric measurements, adherence to the Mediterranean diet (through the Mediterranean Diet Score) and disease-specific quality of life (through the Thyroid Patient Report Outcome for Hashimoto's group, and the Dermatology Life Quality Index for the psoriasis group). This study will investigate metabolic biomarkers and related changes in Hashimoto's thyroiditis and psoriasis and evaluate the association of metabolic changes with dietary factors and quality of life.

Integrated multi-omics uncovers reliable potential biomarkers and adverse effects of zinc deficiency

BACKGROUND

Zinc deficiency is a worldwide public health problem. Currently, there are no established biomarkers available for the accurate diagnosis of zinc-deficiency in individuals. Additionally, a comprehensive view of the adverse effects of zinc deficiency is lacking. Our aim was to identify superior biomarkers of zinc deficiency and uncover the adverse effects of zinc deficiency.

METHODS

We performed multi-omics analysis using serum proteomics-metabolomics and liver proteomics on zinc-deficient rats to identify candidate biomarkers and reveal the associated adverse effects of zinc deficiency. Secondly, the candidate biomarkers were validated in two zinc-deficient populations and an RCT zinc supplementation trial on a zinc-deficient population.

RESULTS

Our integrated multi-omics approach revealed numerous biomarkers (>2000) and glutathione metabolism as the most important changed pathway in zinc deficiency. Three candidate biomarkers from glutathione metabolism were validated in repeated zinc-deficient rats by quantitative analysis. Only glutathione sulfotransferase omega-1 (GSTO1) (among 3 candidate biomarkers) was validated in the two zinc-deficient populations and zinc-supplemented population. Compared with serum zinc, serum GSTO1 yielded a better response to zinc supplementation and a higher correlation coefficient with zinc intake and the AUC value and has the potential for diagnosing zinc deficiency. By integrated multi-omics, we identified both established and novel adverse effects of zinc deficiency.

CONCLUSIONS

Our integrated multi-omics analysis revealed more complete information about zinc deficiency; GSTO1 was found to be a reliable potential biomarker for diagnosis of zinc deficiency. This trial is registered at http://www.chictr.org.cn/registry.aspx as ChiCTR1900028162.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Metabolite Genome-Wide Association Study (mGWAS) and Gene-Metabolite Interaction Network Analysis Reveal Potential Biomarkers for Feed Efficiency in Pigs

Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake (RFI)), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs' feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (p-value < 1.06 × 10-6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4, and CCNC consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine, and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.

Mapping Metabolite and ICD-10 Associations

The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers’ performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.

Decoding the Metabolome and Lipidome of Child Malnutrition by Mass Spectrometric Techniques: Present Status and Future Perspectives

Child malnutrition (CM) is a global public health problem. It contributes to poor health in one in four children under five years worldwide and causes serious health problems in children, including stunted, wasted, and overweight growth. These serious public health issues lead to a higher chance of living in poverty in adulthood. Malnutrition is related with reduced economic productivity and increases the serious national and international burden. Currently, there is no meaningful therapeutic intervention of CM, and the use of different therapeutic foods has shown poor outcomes among supplemented malnourished children. The role of metabolites and lipids has been extensively recognized as early determinants of child health, but their contribution in CM and its pathobiology are poorly understood. This perspective provides a most recent update on these aspects. After briefly introducing the disciplines of metabolomics and lipidomics, we describe a mass spectrometry-based metabolic workflow for analysis of both metabolites and lipids and summarize several recent applications of metabolomics and lipidomics in CM. Finally, we discuss the future directions of the field toward the development of meaningful interventions for CM through metabolomics and lipidomics advances.

Plasticiser-Free 3D Printed Hydrophilic Matrices: Quantitative 3D Surface Texture, Mechanical, Swelling, Erosion, Drug Release and Pharmacokinetic Studies

Hydroxypropyl methyl cellulose, HPMC, a hydrophilic polymer, is widely used for the development of extended release hydrophilic matrices and it is also considered as a good contender for the fabrication of 3D printing of matrix tablets. It is often combined with plasticisers to enable extrusion. The aim of the current project was to develop plasticizer-free 3D printed hydrophilic matrices using drug loaded filaments prepared via HME to achieve an in vitro (swelling, erosion and drug release) and in vivo (drug absorption) performance which is analogous to hydrophilic matrix tablets developed through conventional approaches. Additionally, the morphology of the printed tablets was studied using quantitative 3D surface texture studies and the porosity calculated. Filaments were produced successfully and used to produce matrix tablets with acceptable drug loading (95-105%), mechanical and surface texture properties regardless of the employed HPMC grade. The viscosity of HPMC had a discernible impact on the swelling, erosion, HPMC dissolution, drug release and pharmacokinetic findings. The highest viscosity grade (K100M) results in higher degree of swelling, decreased HPMC dissolution, low matrix erosion, decreased drug release and extended drug absorption profile. Overall, this study demonstrated that the drug loaded (glipizide) filaments and matrix tablets of medium to high viscosity grades of HPMC, without the aid of plasticisers, can be successfully prepared. Furthermore, the in vitro and in vivo studies have revealed the successful fabrication of extended release matrices.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

The Epigenetic Basis of Individuality

This commentary reviews the concept of experience-dependent epigenetic modifications in the CNS as a core mechanism underlying individuality and individuation at the behavioral level. I use the term individuation to refer to the underlying neurobiological processes that result in individuality, with the discussion focusing on individuality of cognitive, emotional, and behavioral repertoire. The review describes recent work supporting the concept of neuroepigenetic mechanisms underlying individuation, possible roles of transgenerational effects, and implications for precision medicine.

Photocurable poly(ethylene glycol) as a bioink for the inkjet 3D pharming of hydrophobic drugs

Binder jetting and material extrusion are the two most common additive manufacturing techniques used to create pharmaceutical tablets. However, their versatility is limited since the powder component is present throughout the dosage forms fabricated by binder jet 3D printing and material extrusion 3D printing requires high operating temperatures. Conversely, material jetting allows for compositional control at a voxel level and can dispense material at room temperature. Unfortunately, there are a limited number of materials that are both printable and biocompatible. Therefore, the aim of this study was to engineer photocurable bioinks that are suitable for hydrophobic active pharmaceutical ingredients and have rapid gelation times upon visible light exposure. The resulting bioinks were comprised of poly(ethylene glycol) diacrylate (250 Da) as the crosslinkable monomer, Eosin Y as the photoinitiator, and methoxide-poly(ethylene glycol)-amine as the coinitiator. Additionally, poly(ethylene glycol) (200 Da) was added as a plasticizer to modulate the drug release profiles, and Naproxen was used as the model drug due to its high hydrophobicity. Various bioink formulations were dispensed into the bottom half of blank preform tablets - made via direct compression - using a piezoelectric nozzle, photopolymerized, and capped with the top half of the preform tablet to complete the pharmaceutical dosage form. Results from the release studies showed that drug release can be modulated by both the percent of poly(ethylene glycol) diacrylate in the formulation and the light exposure time used to cure the bioinks. These bioinks have the potential to expand the library of materials available for creating pharmaceutical tablets via inkjet printing with personalized drug dosages.

A Review of Pulmonary Toxicity of Electronic Cigarettes in the Context of Smoking: A Focus on Inflammation

The use of electronic cigarettes (e-cigs) is increasing rapidly, but their effects on lung toxicity are largely unknown. Smoking is a well-established cause of lung cancer and respiratory disease, in part through inflammation. It is plausible that e-cig use might affect similar inflammatory pathways. E-cigs are used by some smokers as an aid for quitting or smoking reduction, and by never smokers (e.g., adolescents and young adults). The relative effects for impacting disease risk may differ for these groups. Cell culture and experimental animal data indicate that e-cigs have the potential for inducing inflammation, albeit much less than smoking. Human studies show that e-cig use in smokers is associated with substantial reductions in blood or urinary biomarkers of tobacco toxicants when completely switching and somewhat for dual use. However, the extent to which these biomarkers are surrogates for potential lung toxicity remains unclear. The FDA now has regulatory authority over e-cigs and can regulate product and e-liquid design features, such as nicotine content and delivery, voltage, e-liquid formulations, and flavors. All of these factors may impact pulmonary toxicity. This review summarizes current data on pulmonary inflammation related to both smoking and e-cig use, with a focus on human lung biomarkers. Cancer Epidemiol Biomarkers Prev; 26(8); 1175-91. ©2017 AACR.

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Clinical Metabolomics: The New Metabolic Window for Inborn Errors of Metabolism Investigations in the Post-Genomic Era

Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era.

Metabolomics: Bridging the Gap between Pharmaceutical Development and Population Health

Metabolomics has emerged as an essential tool for studying metabolic processes, stratification of patients, as well as illuminating the fundamental metabolic alterations in disease onset, progression, or response to therapeutic intervention. Metabolomics materialized within the pharmaceutical industry as a standalone assay in toxicology and disease pathology and eventually evolved towards aiding in drug discovery and pre-clinical studies via supporting pharmacokinetic and pharmacodynamic characterization of a drug or a candidate. Recent progress in the field is illustrated by coining of the new term—Pharmacometabolomics. Integration of data from metabolomics with large-scale omics along with clinical, molecular, environmental and behavioral analysis has demonstrated the enhanced utility of deconstructing the complexity of health, disease, and pharmaceutical intervention(s), which further highlight it as an essential component of systems medicine. This review presents the current state and trend of metabolomics applications in pharmaceutical development, and highlights the importance and potential of clinical metabolomics as an essential part of multi-omics protocols that are directed towards shaping precision medicine and population health.