Blood transcriptomics and metabolomics for personalized medicine

Exploring the allelopathic autotoxicity mechanism of ginsenosides accumulation under ginseng decomposition based on integrated analysis of transcriptomics and metabolomics

Continuous cropping obstacles seriously constrained the sustainable development of the ginseng industry. The allelopathic autotoxicity of ginsenosides is the key "trigger" of continuous cropping obstacles in ginseng. During harvest, the ginseng plants could be broken and remain in the soil. The decomposition of ginseng residue in soil is one of the important release ways of ginsenosides. Therefore, the allelopathic mechanism of ginsenosides through the decomposed release pathway needs an in-depth study. To investigate this allelopathic regulation mechanism, the integrated analysis of transcriptomics and metabolomics was applied. The prototype ginsenosides in ginseng were detected converse to rare ginsenosides during decomposition. The rare ginsenosides caused more serious damage to ginseng hairy root cells and inhibited the growth of ginseng hairy roots more significantly. By high-throughput RNA sequencing gene transcriptomics study, the significantly differential expressed genes (DEGs) were obtained under prototype and rare ginsenoside interventions. These DEGs were mainly enriched in the biosynthesis of secondary metabolites and metabolic pathways, phytohormone signal transduction, and protein processing in endoplasmic reticulum pathways. Based on the functional enrichment of DEGs, the targeted metabolomics analysis based on UPLC-MS/MS determination was applied to screen endogenous differential metabolized phytohormones (DMPs). The influence of prototype and rare ginsenosides on the accumulation of endogenous phytohormones was studied. These were mainly involved in the biosynthesis of diterpenoid, zeatin, and secondary metabolites, phytohormone signal transduction, and metabolic pathways. After integrating the transcriptomics and metabolomics analysis, ginsenosides could regulate the genes in phytohormone signaling pathways to influence the accumulation of JA, ABA, and SA. The conclusion was that the prototype ginsenosides were converted into rare ginsenosides by ginseng decomposition and released into the soil, which aggravated its allelopathic autotoxicity. The allelopathic mechanism was to intervene in the response regulation of genes related to the metabolic accumulation of endogenous phytohormones in ginseng. This result provides a reference for the in-depth study of continuous cropping obstacles of ginseng.

A metabolome-wide case-control study of african american breast cancer patients

BACKGROUND

Breast cancer survivors face long-term sequelae compared to the general population, suggesting altered metabolic profiles after breast cancer. We used metabolomics approaches to investigate the metabolic differences between breast cancer patients and women in the general population, aiming to elaborate metabolic changes among breast cancer patients and identify potential targets for clinical interventions to mitigate long-term sequelae.

METHODS

Serum samples were retrieved from 125 breast cancer cases recruited from the Chicago Multiethnic Epidemiologic Breast Cancer Cohort (ChiMEC), and 125 healthy controls selected from Chicago Multiethnic Prevention and Surveillance Study (COMPASS). We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) combined with fold change to select metabolic features associated with breast cancer. Pathway analyses were conducted using Mummichog to identify differentially enriched metabolic pathways among cancer patients. As potential confounders we included age, marital status, tobacco smoking, alcohol drinking, type 2 diabetes, and area deprivation index in our model. Random effects of residence for intercept was also included in the model. We further conducted subgroup analysis by treatment timing (chemotherapy/radiotherapy/surgery), lymph node status, and cancer stages.

RESULTS

The entire study participants were African American. The average ages were 57.1 for cases and 58.0 for controls. We extracted 15,829 features in total, among which 507 features were eventually selected by our criteria. Pathway enrichment analysis of these 507 features identified three differentially enriched metabolic pathways related to prostaglandin, leukotriene, and glycerophospholipid. The three pathways demonstrated inconsistent patterns. Metabolic features in the prostaglandin and leukotriene pathways exhibited increased abundances among cancer patients. In contrast, metabolic intensity in the glycerolphospholipid pathway was deregulated among cancer patients. Subgroup analysis yielded consistent results. However, changes in these pathways were strengthened when only using cases with positive lymph nodes, and attenuated when only using cases with stage I disease.

CONCLUSION

Breast cancer in African American women is associated with increase in serum metabolites involved in prostaglandin and leukotriene pathways, but with decrease in serum metabolites in glycerolphospholipid pathway. Positive lymph nodes and advanced cancer stage may strengthen changes in these pathways.

Relationship between plasma amino acid and carnitine levels and primary angle-closure glaucoma based on mass spectrometry metabolomics

World blindness is primarily caused by glaucoma. It has been predicted that by 2040, 118 million individuals will have glaucoma. Among Asians and Africans, primary angle-closure glaucoma (PACG) is the most prevalent type of glaucoma, for which treatment options are currently very limited. At present, lowering intraocular pressure (IOP) is the primary approach for PACG treatment. However, some PACG patients with decreased IOP measurements still advance. Additionally, because of the complicated pathophysiology, there are no biomarkers for diagnosis. Metabolomics is the study of the metabolites produced by all cellular processes in a biological sample, providing a method for identifying biomarkers and early diagnosis. Nevertheless, metabolomics has infrequently been applied to PACG. Previous research conducted by our lab on plasma metabolite fatty acids in PACG patients revealed reduced free fatty acid (FFA) levels, which may be connected to lipid peroxidation. To ascertain the relationship between other metabolites and PACG. We compared levels of amino acids and carnitine in patients with PACG (n = 147) and non-glaucoma (n = 340). Using metabolomics analysis, twenty-one amino acids and twenty-six carnitines (a total of ninety-six indicators) were examined. Odds ratios (OR) and 95% confidence intervals (CI) for these metabolites in relation to PACG were calculated. The relationship between ocular measures and metabolites was assessed by Spearman's rank correlation. Predictive performance was evaluated using the receiver operating characteristic (ROC). The C8/C2 level was comparable across patients with PACG and individuals without glaucoma based on the Wilcoxon rank-sum test. The PACG group had lower levels of Arginine (Arg), Ornithine (Orn), Arg/Orn, Orn/Cit, and C26/C20 than the nonglaucoma group, whereas Cit/Arg and C4/C2 ratios were greater. Both univariate and multivariate models showed a negative correlation between Orn and Orn/Cit and PACG. In the univariate model, palmitoylcarnitine (C16) had a negative correlation with PACG. According to our findings, metabolic profiles of plasma amino acids and carnitine between PACG patients and controls are different. The combination of amino acids and carnitine increased the predictive value of PACG. The Orn and Arg were negatively correlated with the local ocular neurodegenerative pathology. We speculate lipid peroxidation may explain the reduction in C16, and the decrease in Orn may be associated with hyperammonia neurotoxicity.

The effect of infection risk on female blood transcriptomics

Defenses against pathogens can take on many forms. For instance, behavioral avoidance of diseased conspecifics is widely documented. Interactions with these infectious conspecifics can also, however, lead to physiological changes in uninfected animals, an effect that is much less well understood. These changes in behavior and physiology are particularly important to study in a reproductive context, where they can impact reproductive decisions and offspring quality. Here, we studied how an acute (3 h) exposure to an immune-challenged male affected female blood transcriptomics and behavior. We predicted that females paired with immune-challenged males would reduce eating and drinking behaviors (as avoidance behaviors) and that their blood would show activation of immune and stress responses. We used female Japanese quail as a study system because they have been shown to respond to male traits, in terms of their own physiology and egg investment. Only two genes showed significant differential expression due to treatment, including an increase in the threonine dehydrogenase (TDH) transcript, an enzyme important for threonine breakdown. However, hundreds of genes in pathways related to activation of immune responses showed coordinated up-regulation in females exposed to immune-challenged males. Suppressed pathways revealed potential changes to metabolism and reduced responsiveness to glucocorticoids. Contrary to our prediction, we found that females paired with immune-challenged males increased food consumption. Water consumption was not changed by treatment. These findings suggest that even short exposure to diseased conspecifics can trigger both behavioral and physiological responses in healthy animals.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

A training curriculum for retrieving, structuring, and aggregating information derived from the biomedical literature and large-scale data repositories

Background: Biomedical research over the past two decades has become data and information rich. This trend has been in large part driven by the development of systems-scale molecular profiling capabilities and by the increasingly large volume of publications contributed by the biomedical research community. It has therefore become important for early career researchers to learn to leverage this wealth of information in their own research. Methods: Here we describe in detail a training curriculum focusing on the development of foundational skills necessary to retrieve, structure, and aggregate information available from vast stores of publicly available information. It is provided along with supporting material and an illustrative use case. The stepwise workflow encompasses; 1) Selecting a candidate gene; 2) Retrieving background information about the gene; 3) Profiling its literature; 4) Identifying in the literature instances where its transcript abundance changes in blood of patients; 5) Retrieving transcriptional profiling data from public blood transcriptome and reference datasets; and 6) Drafting a manuscript, submitting it for peer-review, and publication. Results: This resource may be leveraged by instructors who wish to organize hands-on workshops. It can also be used by independent trainees as a self-study toolkit. The workflow presented as proof-of-concept was designed to establish a resource for assessing a candidate gene’s potential utility as a blood transcriptional biomarker. Trainees will learn to retrieve literature and public transcriptional profiling data associated with a specific gene of interest. They will also learn to extract, structure, and aggregate this information to support downstream interpretation efforts as well as the preparation of a manuscript. Conclusions: This resource should support early career researchers in their efforts to acquire skills that will permit them to leverage the vast amounts of publicly available large-scale profiling data.

Toxoplasma gondii infection and peripheral-blood gene expression profiling of older people reveals dysregulation of cytokines and identifies hub genes as potential therapeutic targets

Infections of humans with the protozoan parasite Toxoplasma gondii (T. gondii) can lead to the disease's development, even in an asymptomatic status. However, the mechanisms that result in these clinical outcomes after infection are poorly understood. This study aimed to explore the molecular pathogenesis of toxoplasmosis-related inflammation through next-generation sequencing, to assess RNA expression profiles in peripheral blood from 5 female patients with chronic toxoplasmosis and 5 healthy female controls. All plasma samples were analyzed for anti-Toxoplasma IgG and IgM antibody titers by using electrochemiluminescence. Detection of acute and chronic toxoplasmosis was carried out using the ELISA IgG avidity. We evaluated the levels of INF-γ, IL-2, IL-12, TNF-α, IL-10, and IL-1β in culture supernatants of Peripheral Blood Mononuclear Cells infected with Toxoplasma lysate antigen (TLA) prepared with tachyzoites of strain T. gondii RH. Differential expression analysis was performed using DESeq2, pathway and enrichment analysis of DEGs was done on WEB-based Gene SeT AnaLysis Toolkit (WebGestalt) and Protein-protein interaction was carried out using NetworkAnalyst with STRING. In older people with chronic asymptomatic infection, a significant difference in the levels of inflammatory cytokines INF-γ and IL-2 was observed compared to seronegative individuals. Our results revealed differences in the regulation of critical biological processes involved in host responses to chronic T. gondii infection. Gene ontology analysis revealed several biologically relevant inflammatory and immune-related pathways.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

Toward Rapid, Widely Available Autologous CAR-T Cell Therapy – Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital

CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.

Let's Get Back to Normal? COVID-19 and the Logic of Cure

The COVID-19 pandemic has inversed certainties of absolutes of cure in everyday life but paradoxically this has occurred during a time when novel scientific advancements seem to herald a new frontier of cures for rare diseases, chronic conditions, disabilities and viruses that were previously incurable. In this paper, I illustrate the development of a logic of cure by first of all noting a lacuna in the medical sociological and anthropological literature, where although a lot of empirical research and theoretical work to understand cure has been undertaken, there has been no sociology or anthropology of cure. Using three case studies, I examine what they reveal about the logic of cure. Firstly, I argue that there is a development of a bioethics of cure in reactions of disability community and disabled people to care as cure during the COVID-19 pandemic. The second case-study focuses on understanding limitations of vaccines and how people react against such indeterminancies of loss of absolutes of cure. Lastly, the final case study describes how while there are cures, for example, for rare genetic conditions, they are often initially curated with long-term cost-benefit analysis for the Global North. In conclusion, it is found that many of the developments within sociology and anthropology are missing from a logic of cure and that a new theory of cure has to develop.

Maternal plasma lipids are involved in the pathogenesis of preterm birth

BACKGROUND

Preterm birth is defined by the onset of labor at a gestational age shorter than 37 weeks, and it can lead to premature birth and impose a threat to newborns' health. The Puerto Rico PROTECT cohort is a well-characterized prospective birth cohort that was designed to investigate environmental and social contributors to preterm birth in Puerto Rico, where preterm birth rates have been elevated in recent decades. To elucidate possible relationships between metabolites and preterm birth in this cohort, we conducted a nested case-control study to conduct untargeted metabolomic characterization of maternal plasma of 31 women who experienced preterm birth and 69 controls who underwent full-term labor at 24-28 gestational weeks.

RESULTS

A total of 333 metabolites were identified and annotated with liquid chromatography/mass spectrometry. Subsequent weighted gene correlation network analysis shows that the fatty acid and carene-enriched module has a significant positive association (P = 8e-04, FDR = 0.006) with preterm birth. After controlling for potential clinical confounders, a total of 38 metabolites demonstrated significant changes uniquely associated with preterm birth, where 17 of them were preterm biomarkers. Among 7 machine-learning classifiers, the application of random forest achieved a highly accurate and specific prediction (AUC = 0.92) for preterm birth in testing data, demonstrating their strong potential as biomarkers for preterm births. The 17 preterm biomarkers are involved in cell signaling, lipid metabolism, and lipid peroxidation functions. Additional modeling using only the 19 spontaneous preterm births (sPTB) and controls identifies 16 sPTB markers, with an AUC of 0.89 in testing data. Half of the sPTB overlap with those markers for preterm births. Further causality analysis infers that suberic acid upregulates several fatty acids to promote preterm birth.

CONCLUSIONS

Altogether, this study demonstrates the involvement of lipids, particularly fatty acids, in the pathogenesis of preterm birth.

Integrated Metabolomics and Transcriptomics Analyses Reveal Histidine Metabolism Plays an Important Role in Imiquimod-Induced Psoriasis-like Skin Inflammation

Psoriasis is a chronic inflammatory skin disease characterized by massive keratinocyte proliferation and immune cell infiltration into the epidermis. However, the specific mechanisms underlying the development of psoriasis remain unclear. Untargeted metabolomics and transcriptomics have been used separately to profile biomarkers and risk genes in the serum of psoriasis patients. However, the integration of metabolomics and transcriptomics to identify dysregulated metabolites and genes in the psoriatic skin is lacking. In this study, we performed an untargeted metabolomics analysis of imiquimod (IMQ)-induced psoriasis-like mice and healthy controls, and found that levels of a total of 4,188 metabolites differed in IMQ-induced psoriasis-like mice compared with those in control mice. Metabolomic data analysis using MetaboAnalyst showed that the metabolic pathways of primary metabolites, such as folate biosynthesis and galactose metabolism, were significantly altered in the skin of mice after treatment with IMQ. Furthermore, IMQ treatment also significantly altered metabolic pathways of secondary metabolites, including histidine metabolism, in mouse skin tissues. The metabolomic results were verified by transcriptomics analysis. RNA-seq results showed that histamine decarboxylase (HDC) mRNA levels were significantly upregulated after IMQ treatment. Targeted inhibition of histamine biosynthesis process using HDC-specific inhibitor, pinocembrin (PINO), significantly alleviated epidermal thickness, downregulated the expression of interleukin (IL)-17A and IL-23, and inhibited the infiltration of immune cells during IMQ-induced psoriasis-like skin inflammation. In conclusion, our study offers a validated and comprehensive understanding of metabolism during the development of psoriasis and demonstrated that PINO could protect against IMQ-induced psoriasis-like skin inflammation.

Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming

BACKGROUND AND OBJECTIVE

Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes.

METHODS

While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion.

RESULTS

With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes.

CONCLUSIONS

A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.

A plasma metabolomics study suggests alteration of multiple metabolic pathways in patients with bipolar disorder

Previous omics studies have greatly contributed to our knowledge of bipolar disorder. Metabolomics is a relatively new field of omics science that can provide complementary insight into data obtained from genomics, transcriptomics or proteomics analyses. In this study, we aimed to identify metabolic pathways associated with bipolar disorder. We performed a liquid chromatography-mass spectrometry-based study to identify plasma metabolic profiles in patients with bipolar disorder (N = 91) and healthy controls (N = 92). Multivariate features selection by sparse partial least square-discriminant analysis combined with metabolite set enrichment analysis were used to identify metabolites and biological pathways that discriminate patients with bipolar disorder from healthy controls. The results showed that eighty metabolites in the plasma were identified to discriminate patients with bipolar disorder from healthy controls, and nine metabolic pathways, i.e., (1) glycine and serine metabolism, (2) glutamate metabolism, (3) arginine and proline metabolism, (4) tyrosine metabolism, (5) catecholamine biosynthesis, (6) purine metabolism, (7) amino sugar metabolism, (8) ammonia recycling, and (9) carnitine synthesis, were identified to be altered in bipolar disorder compared to healthy controls. We conclude that the 80 metabolites and nine metabolic pathways identified might serve as biomarkers to distinguish bipolar disorder patients from healthy controls.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Review of nanotheranostics for molecular mechanisms underlying psychiatric disorders and commensurate nanotherapeutics for neuropsychiatry: The mind knockout

Bio-neuronal led psychiatric abnormalities transpired by the loss of neuronal structure and function (neurodegeneration), pro-inflammatory cytokines, microglial dysfunction, altered neurotransmission, toxicants, serotonin deficiency, kynurenine pathway, and excessively produced neurotoxic substances. These uncontrolled happenings in the etiology of psychiatric disorders initiate further changes in neurotransmitter metabolism, pathologic microglial, cell activation, and impaired neuroplasticity. Inflammatory cytokines, the outcome of dysfunctional mitochondria, dysregulation of the immune system, and under stress functions of the brain are leading biochemical factors for depression and anxiety. Nanoscale drug delivery platforms, inexpensive diagnostics using nanomaterials, nano-scale imaging technologies, and ligand-conjugated nanocrystals used for elucidating the molecular mechanisms and foremost cellular communications liable for such disorders are highly capable features to study for efficient diagnosis and therapy of the mental illness. These theranostic tools made up of multifunctional nanomaterials have the potential for effective and accurate diagnosis, imaging of psychiatric disorders, and are at the forefront of leading technologies in nanotheranostics openings field as they can collectively and efficiently target the stimulated territories of the cerebellum (cells and tissues) through molecular-scale interactions with higher bioavailability, and bio-accessibility. Specifically, the nanoplatforms based neurological changes are playing a significant role in the diagnosis of psychiatric disorders and portraying the routes of functional restoration of mental disorders by newer imaging tools at nano-level in all directions. Because of these nanotherapeutic platforms, the molecules of nanomedicine can penetrate the Blood-Brain Barrier with an increased half-life of drug molecules. The discoveries in nanotheranostics and nanotherapeutics inbuilt unique multi-functionalities are providing the best multiplicities of novel nanotherapeutic potentialities with no toxicity concerns at the level of nano range.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.

Transcriptomic and Metabolic Responses to a Live-Attenuated Francisella tularensis Vaccine

The immune response to live-attenuated Francisella tularensis vaccine and its host evasion mechanisms are incompletely understood. Using RNA-Seq and LC–MS on samples collected pre-vaccination and at days 1, 2, 7, and 14 post-vaccination, we identified differentially expressed genes in PBMCs, metabolites in serum, enriched pathways, and metabolites that correlated with T cell and B cell responses, or gene expression modules. While an early activation of interferon α/β signaling was observed, several innate immune signaling pathways including TLR, TNF, NF-κB, and NOD-like receptor signaling and key inflammatory cytokines such as Il-1α, Il-1β, and TNF typically activated following infection were suppressed. The NF-κB pathway was the most impacted and the likely route of attack. Plasma cells, immunoglobulin, and B cell signatures were evident by day 7. MHC I antigen presentation was more actively up-regulated first followed by MHC II which coincided with the emergence of humoral immune signatures. Metabolomics analysis showed that glycolysis and TCA cycle-related metabolites were perturbed including a decline in pyruvate. Correlation networks that provide hypotheses on the interplay between changes in innate immune, T cell, and B cell gene expression signatures and metabolites are provided. Results demonstrate the utility of transcriptomics and metabolomics for better understanding molecular mechanisms of vaccine response and potential host–pathogen interactions.

Virus-induced genetics revealed by multidimensional precision medicine transcriptional workflow applicable to COVID-19

Precision medicine requires the translation of basic biological understanding to medical insights, mainly applied to characterization of each unique patient. In many clinical settings, this requires tools that can be broadly used to identify pathology and risks. Patients often present to the intensive care unit with broad phenotypes, including multiple organ dysfunction syndrome (MODS) resulting from infection, trauma, or other disease processes. Etiology and outcomes are unique to individuals, making it difficult to cohort patients with MODS, but presenting a prime target for testing/developing tools for precision medicine. Using multitime point whole blood (cellular/acellular) total transcriptomics in 27 patients, we highlight the promise of simultaneously mapping viral/bacterial load, cell composition, tissue damage biomarkers, balance between syndromic biology versus environmental response, and unique biological insights in each patient using a single platform measurement. Integration of a transcriptome workflow yielded unexpected insights into the complex interplay between host genetics and viral/bacterial specific mechanisms, highlighted by a unique case of virally induced genetics (VIG) within one of these 27 patients. The power of RNA-Seq to study unique patient biology while investigating environmental contributions can be a critical tool moving forward for translational sciences applied to precision medicine.

Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation

BACKGROUND

Sepsis is a leading cause of death in intensive care units (ICUs), but outcomes of individual patients are difficult to predict. The recently developed clinical metabolomics has been recognized as a promising tool in the clinical practice of critical illness. The objective of this study was to identify the unique metabolic biomarkers and their pathways in the blood of sepsis nonsurvivors and to assess the prognostic value of these pathways.

METHODS

We searched PubMed, EMBASE, Cochrane, Web of Science, CNKI, Wangfang Data, and CQVIP from inception until July 2019. Eligible studies included the metabolomic analysis of blood samples from sepsis patients with the outcome. The metabolic pathway was assigned to each metabolite biomarker. The meta-analysis was performed using the pooled fold changes, area under the receiver operating characteristic curve (AUROC), and vote-counting of metabolic pathways. We also conducted a prospective cohort metabolomic study to validate the findings of our meta-analysis.

RESULTS

The meta-analysis included 21 cohorts reported in 16 studies with 2509 metabolite comparisons in the blood of 1287 individuals. We found highly limited overlap of the reported metabolite biomarkers across studies. However, these metabolites were enriched in several death-related metabolic pathways (DRMPs) including amino acids, mitochondrial metabolism, eicosanoids, and lysophospholipids. Prediction of sepsis death using DRMPs yielded a pooled AUROC of 0.81 (95% CI 0.76-0.87), which was similar to the combined metabolite biomarkers with a merged AUROC of 0.82 (95% CI 0.78-0.86) (P > 0.05). A prospective metabolomic analysis of 188 sepsis patients (134 survivors and 54 nonsurvivors) using the metabolites from DRMPs produced an AUROC of 0.88 (95% CI 0.78-0.97). The sensitivity and specificity for the prediction of sepsis death were 80.4% (95% CI 66.9-89.4%) and 78.8% (95% CI 62.3-89.3%), respectively.

CONCLUSIONS

DRMP analysis minimizes the discrepancies of results obtained from different metabolomic methods and is more practical than blood metabolite biomarkers for sepsis mortality prediction.

TRIAL REGISTRATION

The meta-analysis was registered on OSF Registries, and the prospective cohort study was registered on the Chinese Clinical Trial Registry (ChiCTR1800015321).

Network-Based Approaches for Multi-omics Integration

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.

Dysregulated Fc gamma receptor-mediated phagocytosis pathway in Alzheimer's disease: network-based gene expression analysis

Transcriptomics has become an important tool for identification of biological pathways dysregulated in Alzheimer's disease (AD). We performed a network-based gene expression analysis of blood-based microarray gene expression profiles using 2 independent cohorts, Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 661) and AddNeuroMed (N = 674). Weighted gene coexpression network analysis identified 17 modules from ADNI and 13 from AddNeuroMed. Four of the modules derived in ADNI were significantly related to AD; 5 modules in AddNeuroMed were significant. Gene-set enrichment analysis of the AD-related modules identified and replicated 3 biological pathways including the Fc gamma receptor-mediated phagocytosis pathway. Module-based association analysis showed the AD-related module, which has the 3 pathways, to be associated with cognitive function and neuroimaging biomarkers. Gene-based association analysis identified PRKCD in the Fc gamma receptor-mediated phagocytosis pathway as being significantly associated with cognitive function and cerebrospinal fluid biomarkers. The identification of the Fc gamma receptor-mediated phagocytosis pathway implicates the peripheral innate immune system in the pathophysiology of AD. PRKCD is known to be related to neurodegeneration induced by amyloid-β.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

n-Butylamine for Improving the Efficiency of Untargeted Mass Spectrometry Analysis of Plasma Metabolite Composition

A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(-)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities.

The utility of reptile blood transcriptomes in molecular ecology

Reptiles and other nonmammalian vertebrates have transcriptionally active nucleated red blood cells. If blood transcriptomes can provide quantitative data to address questions relevant to molecular ecology, this could circumvent the need to euthanize animals to assay tissues. This would allow longitudinal sampling of animals' responses to treatments, as well as sampling of protected taxa. We developed and annotated blood transcriptomes from six reptile species and found on average 25,000 proteins are being transcribed in the blood, and there is a CORE group of 9,282 orthogroups that are found in at least four of six species. In comparison to liver transcriptomes from the same taxa, approximately two-thirds of the orthogroups were found in both blood and liver; and a similar percentage of ecologically relevant gene groups (insulin and insulin-like signalling, electron transport chain, oxidative stress, glucocorticoid receptors) were found transcribed in both blood and liver. As a resource, we provide a user-friendly database of gene ids identified in each blood transcriptome. Although on average 37% of reads mapped to haemoglobin, importantly, the majority of nonhaemoglobin transcripts had sufficient depth (e.g., 97% at ≥10 reads) to be included in differential gene expression analysis. Thus, we demonstrate that RNAseq blood transcriptomes from a very small blood sample (<10 μl) is a minimally invasive option in nonmammalian vertebrates for quantifying expression of a large number of ecologically relevant genes that would allow longitudinal sampling and sampling of protected populations.

Maternal serum metabolome and traffic-related air pollution exposure in pregnancy

BACKGROUND

Maternal exposure to traffic-related air pollution during pregnancy has been shown to increase the risk of adverse birth outcomes and neurodevelopmental disorders. By utilizing high-resolution metabolomics (HRM), we investigated perturbations of the maternal serum metabolome in response to traffic-related air pollution to identify biological mechanisms.

METHODS

We retrieved stored mid-pregnancy serum samples from 160 mothers who lived in the Central Valley of California known for high air particulate levels. We estimated prenatal traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during first-trimester using the California Line Source Dispersion Model, version 4 (CALINE4) based on residential addresses recorded at birth. We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) to select metabolic features associated with air pollution exposure. Pathway analyses were employed to identify biologic pathways related to air pollution exposure. As potential confounders we included maternal age, maternal race/ethnicity, and maternal education.

RESULTS

In total we extracted 4038 and 4957 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for confounding factors, PLS-DA (Variable Importance in Projection (VIP) ≥2) yielded 181 and 251 metabolic features (HILIC and C18, respectively) that discriminated between the high (n = 98) and low exposed (n = 62). Pathway enrichment analysis for discriminatory features associated with air pollution indicated that in maternal serum oxidative stress and inflammation related pathways were altered, including linoleate, leukotriene, and prostaglandin pathways.

CONCLUSION

The metabolomic features and pathways we found to be associated with air pollution exposure suggest that maternal exposure during pregnancy induces oxidative stress and inflammation pathways previously implicated in pregnancy complications and adverse outcomes.

Tear Metabolomics in Dry Eye Disease: A Review

Dry eye disease (DED) is a multifactorial syndrome that can be caused by alteration in the quality or quantity of the precorneal tear film. It is considered one of the most common ocular conditions leading patients to seek eye care. The current method for diagnostic evaluations and follow-up examinations of DED is a combination of clinical signs and symptoms determined by clinical tests and questionnaires, respectively. The application of powerful omics technologies has opened new avenues toward analysis of subjects in health and disease. Metabolomics is a new emerging and complementary research discipline to all modern omics in the comprehensive analysis of biological systems. The identification of distinct metabolites and integrated metabolic profiles in patients can potentially inform clinicians at an early stage or during monitoring of disease progression, enhancing diagnosis, prognosis, and the choice of therapy. In ophthalmology, metabolomics has gained considerable attention over the past decade but very limited such studies have been reported on DED. This paper aims to review the application of tear metabolomics in DED.

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

Toward multiomics-based next-generation diagnostics for precision medicine

Our healthcare system is experiencing a paradigm shift to precision medicine, aiming at an early prediction of individual disease risks and targeted interventions. Whole-genome sequencing is currently gaining momentum, as it has the potential to capture all classes of genetic variation, thus providing a more complete picture of the individual's genetic makeup, which could be utilized in genetic testing; however, this will also lead to difficulties in interpreting the test results, necessitating careful integration of genomic data with other layers of information, both molecular multiomics measurements of epigenome, transcriptome, proteome, metabolome and even microbiome, as well as comprehensive information on diet, lifestyle and environment. Overall, the translation of patient-specific data into actionable diagnostic tools will be a challenging task, requiring expertise from multiple disciplines, secure data sharing in large reference databases and a strong computational infrastructure.

Metabolite changes in blood predict the onset of tuberculosis

New biomarkers of tuberculosis (TB) risk and disease are critical for the urgently needed control of the ongoing TB pandemic. In a prospective multisite study across Subsaharan Africa, we analyzed metabolic profiles in serum and plasma from HIV-negative, TB-exposed individuals who either progressed to TB 3–24 months post-exposure (progressors) or remained healthy (controls). We generated a trans-African metabolic biosignature for TB, which identifies future progressors both on blinded test samples and in external data sets and shows a performance of 69% sensitivity at 75% specificity in samples within 5 months of diagnosis. These prognostic metabolic signatures are consistent with development of subclinical disease prior to manifestation of active TB. Metabolic changes associated with pre-symptomatic disease are observed as early as 12 months prior to TB diagnosis, thus enabling timely interventions to prevent disease progression and transmission.

The tuberculosis pandemic requires new methods for diagnosing and containing infections prior to active disease. Here, the authors performed a multi-site observational study within sub-Saharan Africa and present serum and plasma metabolic signatures that can predict the onset of active TB with a high degree of sensitivity and specificity.

The growing role of precision and personalized medicine for cancer treatment

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.

Chromatin modifications in metabolic disease: Potential mediators of long-term disease risk

Metabolic diseases such as obesity and diabetes are complex diseases resulting from multiple genetic and environmental factors, such as diet and activity levels. These factors are well known contributors to the development of metabolic diseases. One manner by which environmental factors can influence metabolic disease progression is through modifications to chromatin. These modifications can lead to altered gene regulatory programs, which alters disease risk. Furthermore, there is evidence that parents exposed to environmental factors can influence the metabolic health of offspring, especially if exposures are during intrauterine growth periods. In this review, we outline the evidence that chromatin modifications are associated with metabolic diseases, including diabetes and obesity. We also consider evidence that these chromatin modifications can lead to long-term disease risk and contribute to disease risk for future generations. This article is categorized under: Biological Mechanisms > Metabolism Developmental Biology > Developmental Processes in Health and Disease Physiology > Organismal Responses to Environment.

Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling

Almost invariably, humans become ill during primary infections with malaria parasites which is a pathology associated with oxidative stress and perturbations in metabolism. Importantly, repetitive exposure to Plasmodium results in asymptomatic infections, which is a condition defined as clinical tolerance. Integration of transcriptomics and metabolomics data provides a powerful way to investigate complex disease processes involving oxidative stress, energy metabolism and immune cell activation. We used metabolomics and transcriptomics to investigate the different clinical outcomes in a P. vivax controlled human malaria infection trial. At baseline, the naïve and semi-immune subjects differed in the expression of interferon related genes, neutrophil and B cell signatures that progressed with distinct kinetics after infection. Metabolomics data indicated differences in amino acid pathways and lipid metabolism between the two groups. Top pathways during the course of infection included methionine and cysteine metabolism, fatty acid metabolism and urea cycle. There is also evidence for the activation of lipoxygenase, cyclooxygenase and non-specific lipid peroxidation products in the semi-immune group. The integration of transcriptomics and metabolomics revealed concerted molecular events triggered by the infection, notably involving platelet activation, innate immunity and T cell signaling. Additional experiment confirmed that the metabolites associated with platelet activation genes were indeed enriched in the platelet metabolome.

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Plasmodium vivax infection induces significant change in blood metabolomics.

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Naïve and semi-immune subjects exhibit different molecular profiles.

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Network integration of metabolites/genes hinges on innate activation, chemokines and T cell signaling.

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Involvement of platelet activation is confirmed by platelet metabolomics.

Missing data in open-data era – a barrier to multiomics integration

The exploration of complex interactions in biological systems is one of the main aims in nature science nowadays. Progress in this area is possible because of high-throughput omics technologies and the computational surge. The development of analytical methods “is trying to keep pace” with the development of molecular biology methods that provide increasingly large amounts of data – omics data. Specialized databases consist of ever-larger collections of experiments that are usually conducted by one next-generation sequencing technique (e.g. RNA-seq). Other databases integrate data by defining qualitative relationships between individual objects in the form of ontologies, interactions, and pathways (e.g. GO, KEGG, and String). However, there are no open-source complementary quantitative data sets for the biological processes studied, including information from many levels of the organism organization, which would allow the development of multidimensional data analysis methods (multiscale and insightful overviews of biological processes). In the paper, the lack of omics complementary quantitative data set, which would help integrate the defined qualitative biological relationships of individual biomolecules with statistical, computational methods, is discussed.

Arachidonic Acid Metabolism Pathway Is Not Only Dominant in Metabolic Modulation but Associated With Phenotypic Variation After Acute Hypoxia Exposure

Background: The modulation of arachidonic acid (AA) metabolism pathway is identified in metabolic alterations after hypoxia exposure, but its biological function is controversial. We aimed at integrating plasma metabolomic and transcriptomic approaches to systematically explore the roles of the AA metabolism pathway in response to acute hypoxia using an acute mountain sickness (AMS) model. Methods: Blood samples were obtained from 53 enrolled subjects before and after exposure to high altitude. Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry and RNA sequencing were separately performed for metabolomic and transcriptomic profiling, respectively. Influential modules comprising essential metabolites and genes were identified by weighted gene co-expression network analysis (WGCNA) after integrating metabolic information with phenotypic and transcriptomic datasets, respectively. Results: Enrolled subjects exhibited diverse response manners to hypoxia. Combined with obviously altered heart rate, oxygen saturation, hemoglobin, and Lake Louise Score (LLS), metabolomic profiling detected that 36 metabolites were highly related to clinical features in hypoxia responses, out of which 27 were upregulated and nine were downregulated, and could be mapped to AA metabolism pathway significantly. Integrated analysis of metabolomic and transcriptomic data revealed that these dominant molecules showed remarkable association with genes in gas transport incapacitation and disorders of hemoglobin metabolism pathways, such as ALAS2, HEMGN. After detailed description of AA metabolism pathway, we found that the molecules of 15-d-PGJ2, PGA2, PGE2, 12-O-3-OH-LTB4, LTD4, LTE4 were significantly up-regulated after hypoxia stimuli, and increased in those with poor response manner to hypoxia particularly. Further analysis in another cohort showed that genes in AA metabolism pathway such as PTGES, PTGS1, GGT1, TBAS1 et al. were excessively elevated in subjects in maladaptation to hypoxia. Conclusion: This is the first study to construct the map of AA metabolism pathway in response to hypoxia and reveal the crosstalk between phenotypic variation under hypoxia and the AA metabolism pathway. These findings may improve our understanding of the advanced pathophysiological mechanisms in acute hypoxic diseases and provide new insights into critical roles of the AA metabolism pathway in the development and prevention of these diseases.

Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4⁺ cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4⁺ cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness.

Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria

BACKGROUND

Plasmodium vivax is one of the leading causes of malaria worldwide. Infections with this parasite cause diverse clinical manifestations, and recent studies revealed that infections with P. vivax can result in severe and fatal disease. Despite these facts, biological traits of the host response and parasite metabolism during P. vivax malaria are still largely underexplored. Parasitemia is clearly related to progression and severity of malaria caused by P. falciparum, however the effects of parasitemia during infections with P. vivax are not well understood.

RESULTS

We conducted an exploratory study using a high-resolution metabolomics platform that uncovered significant associations between parasitemia levels and plasma metabolites from 150 patients with P. vivax malaria. Most plasma metabolites were inversely associated with higher levels of parasitemia. Top predicted metabolites are implicated into pathways of heme and lipid metabolism, which include biliverdin, bilirubin, palmitoylcarnitine, stearoylcarnitine, phosphocholine, glycerophosphocholine, oleic acid and omega-carboxy-trinor-leukotriene B4.

CONCLUSIONS

The abundance of several plasma metabolites varies according to the levels of parasitemia in patients with P. vivax malaria. Moreover, our data suggest that the host response and/or parasite survival might be affected by metabolites involved in the degradation of heme and metabolism of several lipids. Importantly, these data highlight metabolic pathways that may serve as targets for the development of new antimalarial compounds.

Advanced bioanalytics for precision medicine

Precision medicine is a new paradigm that combines diagnostic, imaging, and analytical tools to produce accurate diagnoses and therapeutic interventions tailored to the individual patient. This approach stands in contrast to the traditional "one size fits all" concept, according to which researchers develop disease treatments and preventions for an "average" patient without considering individual differences. The "one size fits all" concept has led to many ineffective or inappropriate treatments, especially for pathologies such as Alzheimer's disease and cancer. Now, precision medicine is receiving massive funding in many countries, thanks to its social and economic potential in terms of improved disease prevention, diagnosis, and therapy. Bioanalytical chemistry is critical to precision medicine. This is because identifying an appropriate tailored therapy requires researchers to collect and analyze information on each patient's specific molecular biomarkers (e.g., proteins, nucleic acids, and metabolites). In other words, precision diagnostics is not possible without precise bioanalytical chemistry. This Trend article highlights some of the most recent advances, including massive analysis of multilayer omics, and new imaging technique applications suitable for implementing precision medicine. Graphical abstract Precision medicine combines bioanalytical chemistry, molecular diagnostics, and imaging tools for performing accurate diagnoses and selecting optimal therapies for each patient.

Characterization of rapid extraction protocols for high-throughput metabolomics

RATIONALE

In the last five years, high-throughput metabolomics has significantly advanced scientific research and holds the potential to promote strides in the fields of clinical metabolomics and personalized medicine. While innovations in the field of flow-injection mass spectrometry and three-minute metabolomics methods now allow investigators to process hundreds to thousands of samples per day, time-sensitive clinical applications, particularly in the emergency department, are limited by a lack of rapid extraction methods.

METHODS

Here we characterized the efficacy of fast liquid-liquid extractions for characterization of hydrophilic compounds through ultra-high-pressure liquid chromatography/mass spectrometry. Internal stable-isotope-labeled standards were used to quantitatively characterize markers of energy and oxidative metabolism in human whole blood, plasma and red blood cells - three common matrices of clinical relevance.

RESULTS

For all the tested matrices, vortexing time (4-60 min) did not significantly affect extraction yields for the tested hydrophilic metabolites. Coefficients of variations <<20% for all tested compounds, except for the redox-sensitive metabolite cystine (accumulating over time). Internal standards and second extractions confirmed recoveries >80% for all tested metabolites, except for basic amino acids and polyamines, which showed reproducible yields ranging from 50 to 75%. Global profiling and absolute quantitation of 24 metabolites revealed similarities between the plasma and red blood cell metabolomes.

CONCLUSIONS

Rapid extraction (~4 min) of hydrophilic compounds is a viable and potentially automatable strategy to perform quantitative analysis of whole blood, plasma and red blood cells for research or clinical applications.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Application of metabolomics: Focus on the quantification of organic acids in healthy adults

Metabolomics, a 'budding' discipline, may accurately reflect a specific phenotype which is sensitive to genetic and epigenetic interactions. This rapidly evolving field in science has been proposed as a tool for the evaluation of the effects of epigenetic factors, such as nutrition, environment, drug and lifestyle on phenotype. Urine, being sterile, is easy to obtain and as it contains metabolized or non-metabolized products, is a favored study material in the field of metabolomics. Urine organic acids (OAs) reflect the activity of main metabolic pathways and have been used to assess health status, nutritional status, vitamin deficiencies and response to xenobiotics. To date, a limited number of studies have been performed which actually define reference OA values in a healthy population and as reference range for epigenetic influences, and not as a reference to congenital metabolic diseases. The aim of the present study was thus the determination of reference values (RVs) for urine OA in a healthy adult population. Targeted metabolomics analysis of 22 OAs in the urine of 122 healthy adults by gas chromatography-mass spectrometry, was conducted. Percentile distributions of the OA concentrations in urine, as a base for determining the RVs in the respective population sample, were used. No significant differences were detected between female and male individuals. These findings can facilitate the more sensitive determination of OAs in pathological conditions. Therefore, the findings of this study may contribute or add to the information already available on urine metabolite databases, and may thus promote the use of targeted metabolomics for the evaluation of OAs in a clinical setting and for pathophysiological evaluation. However, further studies with well-defined patients groups exhibiting specific symptoms or diseases are warranted in order to discern between normal and pathological values.

Toward biotechnology in space: High-throughput instruments for in situ biological research beyond Earth

Space biotechnology is a nascent field aimed at applying tools of modern biology to advance our goals in space exploration. These advances rely on our ability to exploit in situ high throughput techniques for amplification and sequencing DNA, and measuring levels of RNA transcripts, proteins and metabolites in a cell. These techniques, collectively known as "omics" techniques have already revolutionized terrestrial biology. A number of on-going efforts are aimed at developing instruments to carry out "omics" research in space, in particular on board the International Space Station and small satellites. For space applications these instruments require substantial and creative reengineering that includes automation, miniaturization and ensuring that the device is resistant to conditions in space and works independently of the direction of the gravity vector. Different paths taken to meet these requirements for different "omics" instruments are the subjects of this review. The advantages and disadvantages of these instruments and technological solutions and their level of readiness for deployment in space are discussed. Considering that effects of space environments on terrestrial organisms appear to be global, it is argued that high throughput instruments are essential to advance (1) biomedical and physiological studies to control and reduce space-related stressors on living systems, (2) application of biology to life support and in situ resource utilization, (3) planetary protection, and (4) basic research about the limits on life in space. It is also argued that carrying out measurements in situ provides considerable advantages over the traditional space biology paradigm that relies on post-flight data analysis.