Systemic differences in serum metabolome: a cross sectional comparison of women with localised and widespread pain and controls

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.

Thermal Pain Thresholds Are Significantly Associated with Plasma Proteins of the Immune System in Chronic Widespread Pain-An Exploratory Pilot Study Using Multivariate and Network Analyses

Chronic widespread pain (CWP), including fibromyalgia (FM), is characterized by generalized musculoskeletal pain. An important clinical feature is widespread increased pain sensitivity such as lowered pain thresholds for different stimuli such as heat (HPT) and cold (CPT). There is a growing interest in investigating the activated neurobiological mechanisms in CWP. This explorative proteomic study investigates the multivariate correlation pattern between plasma and muscle proteins and thermal pain thresholds in CWP and in healthy controls (CON). In addition, we analysed whether the important proteins and their networks for CPT and HPT differed between CWP and CON. We used a proteomic approach and analysed plasma and muscle proteins from women with CWP (n = 15) and CON (n = 23). The associations between the proteins and CPT/HPT were analysed using orthogonal partial least square (OPLS). The protein-protein association networks for the important proteins for the two thermal pain thresholds were analysed using STRING database. CWP had lowered pain thresholds for thermal stimulus. These levels were generally not related to the included clinical variables except in CWP for HPT. Highly interacting proteins mainly from plasma showed strong significant associations with CPT and HPT both in CWP and in CON. Marked differences in the important proteins for the two thermal pain thresholds were noted between CWP and CON; more complex patterns emerged in CWP. The important proteins were part of the immune system (acute phase proteins, complement factors, and immunoglobulin factors) or known to interact with the immune system. As expected, CWP had lowered pain thresholds for thermal stimulus. Although different proteins were important in the two groups, there were similarities. For example, proteins related to the host defence/immunity such as acute phase proteins, complement factors, immunoglobulin factors, and cytokines/chemokines (although not in CON for CPT) were important habitual/tonic factors for thermal pain thresholds. The fact that peripheral proteins contribute to thermal pain thresholds does not exclude that central factors also contribute and that complex interactions between peripheral and central factors determine the registered pain thresholds in CWP.

Sphingomyelin is involved in multisite musculoskeletal pain: evidence from metabolomic analysis in 2 independent cohorts

ABSTRACT

Metabolic dysfunction has been suggested to be involved in musculoskeletal pain; however, few studies have identified metabolic markers associated with multisite musculoskeletal pain (MSMP). This study sought to identify metabolic marker(s) for MSMP by metabolomic analysis. The Tasmanian Older Adult Cohort Study (TASOAC) provided the discovery cohort with the Newfoundland Osteoarthritis Study (NFOAS) providing the replication cohort. Multisite musculoskeletal pain was assessed by a self-reported pain questionnaire and defined as painful sites ≥4 in both the TASOAC and the NFOAS. Furthermore, MSMP was also defined as painful sites ≥7, whereas non-MSMP was defined as either painful sites <7 or ≤1 in the NFOAS. Serum samples of the TASOAC received metabolic profiling using The Metabolomics Innovation Centre Prime Metabolomics Profiling Assay. The data on the identified metabolites were retrieved from NFOAS metabolomic database for the purpose of replication. A total of 409 participants were included in the TASOAC, 38% of them had MSMP. Among the 143 metabolites assessed, 129 passed quality control and were included in the analysis. Sphingomyelin (SM) C18:1 was significantly associated with MSMP (odds ratio [OR] per log µM increase = 3.96, 95% confidence interval, 1.95-8.22; P = 0.0002). The significance remained in multivariable analysis (OR per log µM increase = 2.70, 95% confidence interval, 1.25-5.95). A total of 610 participants were included in the NFOAS, and the association with SM C18:1 was successfully replicated with 3 MSMP definitions (OR ranging from 1.89 to 2.82; all P < 0.03). Our findings suggest that sphingomyelin metabolism is involved in the pathogenesis of MSMP, and the circulating level of SM C18:1 could serve as a potential marker in the management of MSMP.

Metabolomic analysis coupled with extreme phenotype sampling identified that lysophosphatidylcholines are associated with multisite musculoskeletal pain

ABSTRACT

Musculoskeletal pain often occurs simultaneously at multiple anatomical sites. The aim of the study was to identify metabolic biomarkers for multisite musculoskeletal pain (MSMP) by metabolomics with an extreme phenotype sampling strategy. The study participants (n = 610) were derived from the Newfoundland Osteoarthritis Study. Musculoskeletal pain was assessed using a self-reported pain questionnaire where painful sites were circled on a manikin by participants and the total number of painful sites were calculated. Targeted metabolomic profiling on fasting plasma samples was performed using the Biocrates AbsoluteIDQ p180 kit. Plasma cytokine concentrations including tumor necrosis factor-α, interleukin-6, interleukin-1β, and macrophage migration inhibitory factor were assessed by enzyme-linked immunosorbent assay. Data on blood cholesterol profiles were retrieved from participants' medical records. Demographic, anthropological, and clinical information was self-reported. The number of reported painful sites ranged between 0 and 21. Two hundred and five participants were included in the analysis comprising 83 who had ≥7 painful sites and 122 who had ≤1 painful site. Women and younger people were more likely to have MSMP (P ≤ 0.02). Multisite musculoskeletal pain was associated with a higher risk of having incontinence, worse functional status and longer period of pain, and higher levels of low-density lipoprotein and non-high-density lipoprotein cholesterol (all P ≤ 0.03). Among the 186 metabolites measured, 2 lysophosphatidylcholines, 1 with 26 carbons with no double bond and 1 with 28 carbons with 1 double bond, were significantly and positively associated with MSMP after adjusting for multiple testing with the Bonferroni method (P ≤ 0.0001) and could be considered as novel metabolic markers for MSMP.

Metabolomics in chronic pain research

BACKGROUND AND OBJECTIVE

Metabolomics deals with the identification and quantification of small molecules (metabolites) in biological samples. As metabolite levels can reflect normal or altered metabolic pathways, their measurement provides information to improve the understanding, diagnosis and management of diseases. Despite its immense potential, metabolomics applications to pain research have been sparse. This paper describes current metabolomics techniques, reviews published human metabolomics pain research and compares successful metabolomics research in other areas of medicine with the goal of highlighting opportunities offered by metabolomics to advance pain medicine.

DATABASES AND DATA TREATMENT

Non-systematic review.

RESULTS

Our search identified 19 studies that adopted a metabolomics approach in: fibromyalgia (7), chronic widespread pain (4), other musculoskeletal pain conditions (5), neuropathic pain (1), complex regional pain syndrome (1) and pelvic pain (1). The studies used either mass spectrometry or nuclear magnetic resonance. Most are characterized by small sample sizes. Some consistency has been found for alterations in glutamate and testosterone metabolism, and metabolic imbalances caused by the gut microbiome.

CONCLUSIONS

Metabolomics research in chronic pain is in its infancy. Most studies are at the pilot stage. Metabolomics research has been successful in other areas of medicine. These achievements should motivate investigators to expand metabolomics research to improve the understanding of the basic mechanisms of human pain, as well as provide tools to diagnose, predict and monitor chronic pain conditions. Metabolomics research can lead to the identification of biomarkers to support the development and testing of treatments, thereby facilitating personalized pain medicine.

Plasma protein patterns are strongly correlated with pressure pain thresholds in women with chronic widespread pain and in healthy controls-an exploratory case-control study

Chronic widespread pain (CWP) is a complex pain condition characterized by generalized musculoskeletal pain and often associated with other symptoms. An important clinical feature is widespread increased pain sensitivity such as lowered pain thresholds for mechanical stimuli (pressure pain thresholds [PPT]). There is a growing interest in investigating the activated neurobiological mechanisms in CWP, which includes fibromyalgia. In CWP, strong significant correlations have been found between muscle protein patterns and PPT. This explorative proteomic study investigates the multivariate correlation pattern between plasma proteins and PPT in CWP and in healthy controls (CON). In addition, this study analyses whether the important proteins for PPT differ between the 2 groups.Using 2-dimensional gel electrophoresis, we analyzed the plasma proteome of the CWP (n = 15) and the CON (n = 23) and proteins were identified using mass spectrometry. For both the CWP and the CON, the associations between the identified proteins and PPT were analyzed using orthogonal partial least square in 2 steps.Significant associations between certain plasma proteins and PPT existed both in CWP (R = 0.95; P = .006) and in CON (R = 0.89; P < .001). For both groups of subjects, we found several proteins involved in PPT that reflect different biological processes. The plasma proteins as well as the biological processes involved in PPT differed markedly between the 2 groups of subjects.This study suggests that plasma protein patterns are associated with pain thresholds in CWP. Using the plasma proteome profile of CWP to study potential biomarker candidates could provide a snapshot of ongoing systemic mechanisms in CWP.

Peripheral nitric oxide signaling directly blocks inflammatory pain

Pain is a classical sign of inflammation, and sensitization of primary sensory neurons (PSN) is the most important mediating mechanism. This mechanism involves direct action of inflammatory mediators such as prostaglandins and sympathetic amines. Pharmacologic control of inflammatory pain is based on two principal strategies: (i) non-steroidal anti-inflammatory drugs targeting inhibition of prostaglandin production by cyclooxygenases and preventing nociceptor sensitization in humans and animals; (ii) opioids and dipyrone that directly block nociceptor sensitization via activation of the NO signaling pathway. This review summarizes basic concepts of inflammatory pain that are necessary to understand the mechanisms of peripheral NO signaling that promote peripheral analgesia; we also discuss therapeutic perspectives based on the modulation of the NO pathway.

A laboratory approach for characterizing chronic fatigue: what does metabolomics tell us?

INTRODUCTION

Manifestations of fatigue range from chronic fatigue up to a severe syndrome and myalgic encephalomyelitis. Fatigue grossly affects the functional status and quality of life of affected individuals, prompting the World Health Organization to recognize it as a chronic non-communicable condition.

OBJECTIVES

Here, we explore the potential of urinary metabolite information to complement clinical criteria of fatigue, providing an avenue towards an objective measure of fatigue in patients presenting with the full spectrum of fatigue levels.

METHODS

The experimental group consisted of 578 chronic fatigue female patients. The measurement design was composed of (1) existing clinical fatigue scales, (2) a hepatic detoxification challenge test, and (3) untargeted proton nuclear magnetic resonance (¹H-NMR) procedure to generate metabolomics data. Data analysed via an in-house Matlab script that combines functions from a Statistics and a PLS Toolbox.

RESULTS

Multivariate analysis of the original 459 profiled ¹H-NMR bins for the low (control) and high (patient) fatigue groups indicated complete separation following the detoxification experimental challenge. Important bins identified from the ¹H-NMR spectra provided quantitative metabolite information on the detoxification challenge for the fatigue groups.

CONCLUSIONS

Untargeted ¹H-NMR metabolomics proved its applicability as a global profiling tool to reveal the impact of toxicological interventions in chronic fatigue patients. No clear potential biomarker emerged from this study, but the quantitative profile of the phase II biotransformation products provide a practical visible effect directing to up-regulation of crucial phase II enzyme systems in the high fatigue group in response to a high xenobiotic-load.

Gut microbiome and serum metabolome analyses identify molecular biomarkers and altered glutamate metabolism in fibromyalgia

BACKGROUND

Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition.

METHODS

We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers.

RESULTS

We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia.

CONCLUSIONS

Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology

Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.

Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome

Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.

Plasma Protein Pattern Correlates With Pain Intensity and Psychological Distress in Women With Chronic Widespread Pain

Objectives: Although generalized muscle pain, tiredness, anxiety, and depression are commonly present among chronic widespread pain (CWP) patients, the molecular mechanisms behind CWP are not fully elucidated. Moreover, the lack of biomarkers often makes diagnosis and treatment problematic. In this study, we investigated the correlation between pain intensity, psychological distress, and plasma proteins among CWP patients and controls (CON). Methods: The plasma proteome of CWP (n = 15) and CON (n = 23) was analyzed using two-dimensional gel electrophoresis. Orthogonal Partial Least Square analysis (OPLS) was used to determine proteins associated with pain intensity (numeric rating scale) in CWP and psychological distress (Hospital and Depression Scale, HADS) in CWP and CON. Significant proteins were identified by MALDI-TOF and tandem MS. Results: In CWP, pain intensity was associated with plasma proteins mostly involved in metabolic and immunity processes (e.g., kininogen-1, fibrinogen gamma chain, and ceruloplasmin), and psychological distress was associated with plasma proteins related to immunity response, iron ion, and lipid metabolism (e.g., complement factor B, complement C1r subcomponent, hemopexin, and clusterin). Discussion: This study suggests that different plasma protein patterns are associated with different pain intensity and psychological distress in CWP. Proteins belonging to the coagulation cascade and immunity processes showed strong associations to each clinical outcome. Using the plasma proteome profile of CWP to study potential biomarker candidates provides a snapshot of ongoing systemic mechanisms in CWP.

Systemic alterations in plasma proteins from women with chronic widespread pain compared to healthy controls: a proteomic study

Chronic widespread pain (CWP) is a complex pain condition that is difficult to treat. The prevalence of CWP approximates ~10% of the general population, with higher prevalence in women. Lack of understanding of molecular mechanisms has been a challenge for diagnosis and treatment of chronic pain. The aim of this study was to explore the systemic protein changes in CWP compared to those in healthy controls (CON). By applying 2-dimensional gel electrophoresis, we analyzed the protein pattern of plasma samples from women with CWP (n=16) and healthy women (n=23). The proteomic data were analyzed using multivariate statistical models, and altered proteins were identified using mass spectrometry. The proteome analysis was further validated by gel-free Western blot. Multivariate statistical data analysis of quantified proteins revealed 22 altered proteins in women with CWP, compared to CON group. Many of the identified proteins are previously known to be involved in different parts of the complement system and metabolic and inflammatory processes, e.g., complement factor B, vitamin D-binding protein, ceruloplasmin, transthyretin and alpha-2-HS-glycoprotein. These results indicate that important systemic protein differences exist between women with CWP and healthy women. Further, this study illustrates the potential use of proteomics to detect biomarkers that may provide new insights into the molecular mechanism(s) of chronic pain. However, further larger investigations are required in order to confirm these findings before it will be possible to identify proteins as potential pain biomarkers for clinical use.

Urinary metabolomics of complete Freund's adjuvant-induced hyperalgesia in rats

The aim of this study was to demonstrate the differences of metabolomics changes in a hyperalgesia model and find potent biomarkers of hyperalgesia. Seven rats were placed in metabolic cages. An emulsion containing 500 μg of Complete Freund's adjuvant (CFA) was used to induce hyperalgesia. Urine samples were collected prior to the injection of CFA and on post-injection days 1, 3 and 7. Ultraperformance liquid chromatography, coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), was used for a quantitative analysis of urinary metabolic changes in the CFA-induced hyperalgesia model. Differences between the metabolic profiles of the rats in the four groups were analyzed using partial least squares discriminant analysis. Thirty-four potential urine metabolite biomarkers were identified, which changed in a trend similar to the pain threshold. These potential biomarkers were involved in 11 metabolic pathways, as follows: alanine, aspartate, and glutamate metabolism; ascorbate and aldarate metabolism; glycerolipid metabolism; glycerophospholipid metabolism; histidine metabolism; phenylalanine metabolism; sphingolipid metabolism; tryptophan metabolism; tyrosine metabolism; valine, leucine and isoleucine biosynthesis; and vitamin B6 metabolism. These results may improve our understanding of hyperalgesia and provide a basis for the clinical diagnosis of hyperalgesia.

Trading direct for indirect defense? Phytochrome B inactivation in tomato attenuates direct anti-herbivore defenses whilst enhancing volatile-mediated attraction of predators

Under conditions of competition for light, which lead to the inactivation of the photoreceptor phytochrome B (phyB), the growth of shade-intolerant plants is promoted and the accumulation of direct anti-herbivore defenses is down-regulated. Little is known about the effects of phyB on emissions of volatile organic compounds (VOCs), which play a major role as informational cues in indirect defense. We investigated the effects of phyB on direct and indirect defenses in tomato (Solanum lycopersicum) using two complementary approaches to inactivate phyB: illumination with a low red to far-red ratio, simulating competition, and mutation of the two PHYB genes present in the tomato genome. Inactivation of phyB resulted in low levels of constitutive defenses and down-regulation of direct defenses induced by methyl jasmonate (MeJA). Interestingly, phyB inactivation also had large effects on the blends of VOCs induced by MeJA. Moreover, in two-choice bioassays using MeJA-induced plants, the predatory mirid bug Macrolophus pygmaeus preferred VOCs from plants in which phyB was inactivated over VOCs from control plants. These results suggest that, in addition to repressing direct defense, phyB inactivation has consequences for VOC-mediated tritrophic interactions in canopies, presumably attracting predators to less defended plants, where they are likely to find more abundant prey.

Background, design and conceptual model of the cluster randomized multiple-component workplace study: FRamed Intervention to Decrease Occupational Muscle pain - "FRIDOM"

Background

Several RCT studies have aimed to reduce either musculoskeletal disorders, sickness presenteeism, sickness absenteeism or a combination of these among females with high physical work demands. These studies have provided evidence that workplace health promotion (WHP) interventions are effective, but long-term effects are still uncertain. These studies either lack to succeed in maintaining intervention effects or lack to document if effects are maintained past a one-year period. This paper describes the background, design and conceptual model of the FRIDOM (FRamed Intervention to Decrease Occupational Muscle pain) WHP program among health care workers. A job group characterized by having high physical work demands, musculoskeletal disorders, high sickness presenteeism - and absenteeism.

Methods

FRIDOM aimed to reduce neck and shoulder pain. Secondary aims were to decrease sickness presenteeism, sickness absenteeism and lifestyle-diseases such as other musculoskeletal disorders as well as metabolic-, and cardiovascular disorders – and to maintain participation to regular physical exercise training, after a one year intervention period. The entire concept was tailored to a population of female health care workers. This was done through a multi-component intervention including 1) intelligent physical exercise training (IPET), dietary advice and weight loss (DAW) and cognitive behavioural training (CBT).

Discussion

The FRIDOM program has the potential to provide evidence-based knowledge of the pain reducing effect of a multi component WHP among a female group of employees with a high prevalence of musculoskeletal disorders and in a long term perspective evaluate the effects on sickness presenteeism and absenteeism as well as risk of life-style diseases.

Trial registration

NCT02843269, 06.27.2016 - retrospectively registered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-016-3758-6) contains supplementary material, which is available to authorized users.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.