Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and d...
Contexts in source publication
Context 1
... short, regarding the management of patients with infections, it is relevant to define biomarkers that effectively will help physicians to diagnose the infection, to predict the causative agent, the disease severity and progression and to monitor the antimicrobial therapy (Figure 1). These biomarkers should be especially applicable in patients in risk of developing severe diseases, critically ill patients, and those presenting confound symptoms that may result in under and over diagnosis of infection. ...
Context 2
... biomarkers should be especially applicable in patients in risk of developing severe diseases, critically ill patients, and those presenting confound symptoms that may result in under and over diagnosis of infection. More specific and sensitive biomarkers of infection have great potential to decrease severe outcomes, minimizing hospitalization times and significantly improve patient survival ( Figure 1). This section may be divided by subheadings. ...
Similar publications
Objective
To compare the predictive performance of residents, senior intensive care unit physicians and surrogates early during intensive care unit stays and to evaluate whether different presentations of prognostic data (probability of survival versus probability of death) influenced their performance.
Methods
We questioned surrogates and physici...
Citations
... The comprehensive data generated by omics technologies have been instrumental in the discovery of biomarkers for disease diagnosis, prognosis, and therapeutic targeting, including, e.g., in studies of cancer [15], genotoxicity [16], organ rejection [17,18], and infections [13,19]. By integrating data across different omics layers, researchers can construct a more complete picture of biological processes, thereby enhancing our understanding of health and disease [20,21]. ...
Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.
... Prior metabolomic studies have pinpointed specific metabolic changes associated with the urea cycle and oxidative stress, which correlate with inflammatory markers in healthy individuals (Pietzner et al., 2017) and rheumatoid arthritis patients (Jutley et al., 2021). Importantly, metabolomics was also widely applied to identify infection biomarkers (Araújo et al., 2022). Moreover, microbiota balance has a powerful regulatory effect on the human immune and metabolic systems (Samuelson et al., 2015). ...
Background
Inflammation serves as a key pathologic mediator in the progression of infections and various diseases, involving significant alterations in the gut microbiome and metabolism. This study aims to probe into the potential causal relationships between gut microbial taxa and human blood metabolites with various serum inflammatory markers (CRP, SAA1, IL-6, TNF-α, WBC, and GlycA) and the risks of seven common infections (gastrointestinal infections, dysentery, pneumonia, bacterial pneumonia, bronchopneumonia and lung abscess, pneumococcal pneumonia, and urinary tract infections).
Methods
Two-sample Mendelian randomization (MR) analysis was performed using inverse variance weighted (IVW), maximum likelihood, MR-Egger, weighted median, and MR-PRESSO.
Results
After adding other MR models and sensitivity analyses, genus Roseburia was simultaneously associated adversely with CRP (Beta IVW = −0.040) and SAA1 (Beta IVW = −0.280), and family Bifidobacteriaceae was negatively associated with both CRP (Beta IVW = −0.034) and pneumonia risk (Beta IVW = −0.391). After correction by FDR, only glutaroyl carnitine remained significantly associated with elevated CRP levels (Beta IVW = 0.112). Additionally, threonine (Beta IVW = 0.200) and 1-heptadecanoylglycerophosphocholine (Beta IVW = −0.246) were found to be significantly associated with WBC levels. Three metabolites showed similar causal effects on different inflammatory markers or infectious phenotypes, stearidonate (18:4n3) was negatively related to SAA1 and urinary tract infections, and 5-oxoproline contributed to elevated IL-6 and SAA1 levels. In addition, 7-methylguanine showed a positive correlation with dysentery and bacterial pneumonia.
Conclusion
This study provides novel evidence confirming the causal effects of the gut microbiome and the plasma metabolite profile on inflammation and the risk of infection. These potential molecular alterations may aid in the development of new targets for the intervention and management of disorders associated with inflammation and infections.
... The need for a well-understood pathophysiological mechanism of schistosomiasis, together with accurate disease diagnosis, are crucial elements for the successful control and elimination of the disease. In this regard, there has been an increase in concerted efforts aimed at identifying infection biomarkers to be utilised in the development of diagnostic tools and identification of therapeutic targets (6,7). ...
... The feasibility of metabolomics in the discovery of biomarkers falls on the premise that metabolites are the key elements in biological pathways and in the presence of a disease, notable changes will occur in these metabolic pathways (22). Consequently, parallel assessment of multiple metabolites during infection enables identification of potent biomarkers that can be used in the development of cost-effective and highly sensitive diagnostic tools (7,23). ...
Background
Metabolomics approaches are indispensable tools in infection biomarker discovery efforts as they shed light on the underlying pathophysiological mechanisms of disease. In this study, we analysed plasma metabolites that can be used as biomarkers of urogenital schistosomiasis in pre-school aged children below the age of five.
Methods
A case-control study was conducted involving 82 pre-school aged children that were age- and sex-matched. Urine samples were collected for three consecutive days to detect S. haematobium infection using urine filtration. Blood samples were also collected and processed to obtain plasma. Beckman Coulter AU480 chemistry analyser and commercial metabolite kits were used for profiling biomarkers in plasma samples. Descriptive statistics and MetaboAnalyst tool, were used for metabolite analysis. For the determination of diagnostic efficiency of plasma biomarkers, the area under the curve (AUC) was calculated from receiver operating characteristic curves at 95% CI.
Results
Succinic acid, glucose-6-phosphate, phosphatidylcholine, alanine and creatinine levels in plasma were significantly associated with urogenital schistosomiasis (p<0.005) at the population level. Significant increase in concentration at 1.5-fold change (FC) threshold was highest for glucose-6-phosphate with FC value of 2.02 followed by creatinine, albumin and phosphatidylcholine. Creatinine was significantly downregulated with a FC value of 1.98. Of the six dysregulated metabolic pathways, glucose and sucrose metabolism were predominantly affected. Glucose-6-phosphate had the highest AUC (0.81), sensitivity (88.85%) and specificity (90.37%). Phosphatidylcholine and succinic acid also had AUC values greater than 0.7.
Conclusion
Urogenital schistosomiasis affects the energy-related metabolic pathways in pre-school aged children. Glucose-6-phosphate was identified as a potential indicator of infection at the population level. Furthermore, we recommend intensive validation of schistosome metabolite biomarkers.
... Using tandem mass spectrometry, amino acids and fatty acids have been finely detected in samples such as blood, urine, and amniotic fluid [12,13]. Tandem mass spectrometry and metabolomics are widely used for the screening, diagnosis, evaluation, and monitoring of multiple genetic metabolic diseases with rapid detection, high sensitivity, and specificity [13][14][15]. They present a huge advantage in prenatal diagnosis, enabling rapid and accurate assessment of fetal health or the diagnosis of diseases [16,17]. ...
Unlabelled:
This study aimed to compare the blood metabolic status of neonates with idiopathic polyhydramnios (IPH) and those with normal amniotic fluid, and to explore the relationship between IPH and fetal health. Blood metabolites of 32 patients with IPH and 32 normal controls admitted to the Sixth Affiliated Hospital of Sun Yat-sen University between January 2017 and December 2022 were analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Orthogonal partial least squares discriminant analysis (OPLS-DA) and metabolite enrichment analyses were performed to identify the differential metabolites and metabolic pathways. There was a significant difference in the blood metabolism between newborns with IPH and those with normal amniotic fluid. Six discriminant metabolites were identified: glutamate, serine, asparagine, aspartic acid, homocysteine, and phenylalanine. Differential metabolites were mainly enriched in two pathways: aminoacyl-tRNA biosynthesis, and alanine, aspartate, and glutamate metabolism.
Conclusions:
This study is the first to investigate metabolomic profiles in newborns with IPH and examine the correlation between IPH and fetal health. Differential metabolites and pathways may affect amino acid synthesis and the nervous system. Continuous attention to the development of the nervous system in children with IPH is necessary.
What is known:
• There is an increased risk of adverse pregnancy outcomes with IPH, such as perinatal death, neonatal asphyxia, neonatal intensive care admission, cesarean section rates, and postpartum hemorrhage. • Children with a history of IPH have a higher proportion of defects than the general population, particularly central nervous system problems, neuromuscular disorders, and other malformations.
What is new:
• In neonates with IPH, six differential metabolites were identified with significant differences and good AUC values using LC-MS/MS analysis: glutamic acid, serine, asparagine, aspartic acid, homocysteine, and phenylalanine, which were mainly enriched in two metabolic pathways: aminoacyl-tRNA biosynthesis and alanine, aspartate, and glutamate metabolism. • These differential metabolites and pathways may affect amino acid synthesis and development of the nervous system in neonates with IPH.
... [ [49][50][51] Epigenomics Studies heritable changes not coded in DNA sequence. ...
Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics’ current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.
... There are diverse and complementary analytical techniques to retrieve the system's metabolome, 10 one of the most common approaches being based on ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) due to the wide metabolite coverage offered by the wide-ranging selectivity in LC separation and great sensitivity in MS/MS detection. The dynamic range of these analytical setups enables the acquisition of data over 3−5 orders of magnitude and the detection of metabolite concentrations less than 0.01% from the highest metabolite intensity. ...
Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.
... From the metabolic inflammation perspective, IL10 is significantly decreased in obesity, insulin resistance and inflammatory bowel disease (Charles et al. 2011;Leon-Cabrera et al. 2015;Meng, Liang, and Guo 2019), being a relevant candidate to be included as an indicator of inflammatory status in different metabolic conditions due to its master regulatory role. However, it is important to remember that infectious processes or acute injury can increase the levels of inflammation biomarkers many times more than the increases observed due to metabolic derangements, with the CRP response as prime example (Araújo et al. 2022;P C Calder et al. 2013). ...
Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.
... The detection of S. parauberis mainly depends on the identification of clinical symptoms [3], conventional culture-based biochemical tests [4], and molecular tests such as polymerase chain reaction (PCR) based on the 23S rRNA region [2]. Although PCR is an established technique for the diagnosis of S. parauberis, the high sensitivity of the test may exceed clinical significance leading to false positives and targeting inappropriate specimen types, or suboptimal volumes of the specimen may result in false negatives [5]. ...
... Metabolomics studies, such as liquid chromatography-tandem mass spectrometry (LC-MS) [7], optical spectroscopy [8,9], and nuclear magnetic resonance ( 1 H-NMR) spectroscopy, have identified novel biomarkers for diagnosing bacterial infections in fishes. Although these techniques offer intrinsic advantages, such as high resolution and accuracy, the miniaturization of the equipment for point-of-care devices and their application remain a challenge [5]. Notably, electrochemical techniques are both highly selective and sensitive for target detection [10]. ...
Bacterial infections in marine fishes are linked to mass mortality issues; hence, rapid detection of an infection can contribute to achieving a faster diagnosis using point-of-care testing. There has been substantial interest in identifying diagnostic biomarkers that can be detected in major organs to predict bacterial infections. Aspartate was identified as an important biomarker for bacterial infection diagnosis in olive flounder (Paralichthys olivaceus) fish. To determine aspartate levels, an amperometric biosensor was designed based on bi-enzymes, namely, glutamate oxidase (GluOx) and aspartate transaminase (AST), which were physisorbed on copolymer reduced graphene oxide (P-rGO), referred to as enzyme nanosheets (GluOx-ASTENs). The GluOx-ASTENs were drop casted onto a Prussian blue electrodeposited screen-printed carbon electrode (PB/SPCE). The proposed biosensor was optimized by operating variables including the enzyme loading amount, coreactant (α-ketoglutarate) concentration, and pH. Under optimal conditions, the biosensor displayed the maximum current responses within 10 s at the low applied potential of −0.10 V vs. the internal Ag/AgCl reference. The biosensor exhibited a linear response from 1.0 to 2.0 mM of aspartate concentrations with a sensitivity of 0.8 µA mM−1 cm−2 and a lower detection limit of approximately 500 µM. Moreover, the biosensor possessed high reproducibility, good selectivity, and efficient storage stability.
... In theory, the omics sciences present advantages in the discovery of biomarkers in the diagnosis or prognosis of a defined physiological state due to the high complexity and interrelationships of the biological processes, as in this case, e.g., associated with the rejection mechanisms, immunotherapies, and the high diversity of the patient's physiological states [40]. Since the proteome reflects different gene expressions, alterations occurring at the transcriptional level (e.g., alternative splicing), protein post-translational modification, and protein degradation or release portray with higher accuracy the system phenotype in a specific environment and, therefore, presents advantages over genomics and transcriptomics. ...
Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume , proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Prote-omics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclin-ical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.
Introduction:
The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.
Areas covered:
This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.
Expert opinion:
The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.
