Discovery Of Candidate Biomarkers Research Articles

Exploration on OCT biomarker candidate related to macular edema caused by diabetic retinopathy and retinal vein occlusion in SD-OCT images

To improve the understanding of potential pathological mechanisms of macular edema (ME), we try to discover biomarker candidates related to ME caused by diabetic retinopathy (DR) and retinal vein occlusion (RVO) in spectral-domain optical coherence tomography images by means of deep learning (DL). 32 eyes of 26 subjects with non-proliferative DR (NPDR), 77 eyes of 61 subjects with proliferative DR (PDR), 120 eyes of 116 subjects with branch RVO (BRVO), and 17 eyes of 15 subjects with central RVO (CRVO) were collected. A DL model was implemented to guide biomarker candidate discovery. The disorganization of the retinal outer layers (DROL), i.e., the gray value of the retinal tissues between the external limiting membrane (ELM) and retinal pigment epithelium (RPE), the disrupted and obscured rate of the ELM, ellipsoid zone (EZ), and RPE, was measured. In addition, the occurrence, number, volume, and projected area of hyperreflective foci (HRF) were recorded. ELM, EZ, and RPE are more likely to be obscured in RVO group and HRFs are observed more frequently in DR group (all P ≤ 0.001). In conclusion, the features of DROL and HRF can be possible biomarkers related to ME caused by DR and RVO in OCT modality.

Tear Proteins Altered in Patients with Persistent Eye Pain after Refractive Surgery: Biomarker Candidate Discovery.

Some patients develop persistent eye pain after refractive surgery, but factors that cause or sustain pain are unknown. We tested whether tear proteins of patients with pain 3 months after surgery differ from those of patients without pain. Patients undergoing refractive surgery (laser in situ keratomileusis or photorefractive keratectomy ) were recruited from 2 clinics, and tears were collected 3 months after surgery. Participants rated their eye pain using a numerical rating scale (NRS, 0-10; no pain-worst pain) at baseline, 1 day, and 3 months after surgery. Using tandem mass tag proteomic analysis, we examined tears from patients with pain [NRS ≥ 3 at 3 months (n = 16)] and patients with no pain [NRS ≤ 1 at 3 months (n = 32)] after surgery. A subset of proteins (83 of 2748 detected, 3.0%) were associated with pain 3 months after surgery. High-dimensional statistical models showed that the magnitude of differential expression was not the only important factor in classifying tear samples from pain patients. Models utilizing 3 or 4 proteins had better classification performance than single proteins and represented differences in both directions (higher or lower in pain). Thus, patterns of protein differences may serve as biomarkers of postsurgical eye pain as well as potential therapeutic targets.

Sample-Treatment with the Virucidal β-Propiolactone Does Not Preclude Analysis by Large Panel Affinity Proteomics, Including the Discovery of Biomarker Candidates.

Virus inactivation is a prerequisite for safe handling of high-risk infectious samples. β-Propiolactone (BPL) is an established reagent with proven virucidal efficacy. BPL primarily reacts with DNA, RNA, and amino acids. The latter may modify antigenic protein epitopes interfering with binding properties of affinity reagents such as antibodies and aptamers used in affinity proteomic screens. We investigated (i) the impact of BPL treatment on the analysis of protein levels in plasma samples using the aptamer-based affinity proteomic platform SomaScan and (ii) effects on protein detection in conditioned medium samples using the proximity extension assay-based Olink Target platform. In the former setup, BPL-treated and native plasma samples from patients with ovarian cancer (n = 12) and benign diseases (n = 12) were analyzed using the SomaScan platform. In the latter, conditioned media samples collected from cultured T cells with (n = 3) or without (n = 3) anti-CD3 antibody stimulation were analyzed using the Olink Target platform. BPL-related changes in protein detection were evaluated comparing native and BPL-treated states, simulating virus inactivation, and impact on measurable group differences was assessed. While approximately one-third of SomaScan measurements were significantly changed by the BPL treatment, a majority of antigen/aptamer interactions remained unaffected. Interaction effects of BPL treatment and disease state, potentially altering detectability of group differences, were observable for less than one percent of targets (0.6%). BPL effects on protein detection with Olink Target were also limited, affecting 3.6% of detected proteins with no observable interaction effects. Thus, effects of BPL treatment only moderately interfere with affinity proteomic detectability of differential protein expression between different experimental groups. Overall, the results prove high-throughput affinity proteomics well suited for the analysis of high-risk samples inactivated using BPL.

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

Tracer-based lipidomics enables the discovery of disease-specific candidate biomarkers in mitochondrial β-oxidation disorders.

Carnitine derivatives of disease-specific acyl-CoAs are the diagnostic hallmark for long-chain fatty acid β-oxidation disorders (lcFAOD), including carnitine shuttle deficiencies, very-long-chain acyl-CoA dehydrogenase deficiency (VLCADD), long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency (LCHADD) and mitochondrial trifunctional protein deficiency (MPTD). The exact consequence of accumulating lcFAO-intermediates and their influence on cellular lipid homeostasis is, however, still unknown. To investigate the fate and cellular effects of the accumulating lcFAO-intermediates and to explore the presence of disease-specific markers, we used tracer-based lipidomics with deuterium-labeled oleic acid (D9-C18:1) in lcFAOD patient-derived fibroblasts. In line with previous studies, we observed a trend towards neutral lipid accumulation in lcFAOD. In addition, we detected a direct connection between the chain length and patterns of (un)saturation of accumulating acylcarnitines and the various enzyme deficiencies. Our results also identified two disease-specific candidate biomarkers. Lysophosphatidylcholine(14:1) (LPC(14:1)) was specifically increased in severe VLCADD compared to mild VLCADD and control samples. This was confirmed in plasma samples showing an inverse correlation with enzyme activity, which was better than the classic diagnostic marker C14:1-carnitine. The second candidate biomarker was an unknown lipid class, which we identified as S-(3-hydroxyacyl)cysteamines. We hypothesized that these were degradation products of the CoA moiety of accumulating 3-hydroxyacyl-CoAs. S-(3-hydroxyacyl)cysteamines were significantly increased in LCHADD compared to controls and other lcFAOD, including MTPD. Our findings suggest extensive alternative lipid metabolism in lcFAOD and confirm that lcFAOD accumulate neutral lipid species. In addition, we present two disease-specific candidate biomarkers for VLCADD and LCHADD, that may have significant relevance for disease diagnosis, prognosis, and monitoring.

Introducing Pain-Related Molecular Pathways in Painful Diabetic Neuropathy Via Protein Interaction Networks

Background: Painful diabetic neuropathy (PDN) is one of the most drastic complications of diabetes. Patients with PDN always reveal spontaneous and stimulus‑evoked pain. However, the pathogenesis mechanisms of PDN are not entirely distinct. Objectives: In the present study protein-protein interaction (PPI) network for PDN was constructed and analyzed to identify key proteins as potential biomarker candidates. Methods: The transcriptomic (genes) and proteomic (proteins) data in articles that focused on PDN with differential expressions were collected. Protein networks were constructed and analyzed using STRING and Cytoscape software, respectively. Further PPI network analysis, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis were performed using MCODE and DAVID tools. Results: A total of 147 differentially regulated proteins/genes were identified in painful diabetic neuropathy, including 91 up-regulated and 56 down-regulated proteins/genes. A network analysis of genes/proteins related to PDN identified COX4I1, NDUFS8, UQCRC1, COX7C, and some other NADH dehydrogenases, including NDUFB7, NDUFS7, NDUFS3, NDUFB5, NDUFA2, and NDUFB4 as hub-bottleneck proteins. With functional enrichment analysis of network clustering, COX7C, HP, RPS12, KCNIP2, and CoL4A1 were established as distinct seed proteins in the obtained modules, which could lead to the discovery of biomarker candidates. Conclusions: These results could provide new insights into pathology and molecular mechanisms, as well as the identification of pathways and proteins/genes involved in causing PDN in diabetic patients. COX7C, HP, RPS12, KCNIP2, and CoL4A1 are the top 5 seed nodes (hub proteins) and can serve as potential biomarker candidates and targets for PDN management. However, further investigations are needed to evaluate these proteins in detail.

Discovery of candidate biomarkers from plasma-derived extracellular vesicles of patients with cirrhosis and hepatocellular carcinoma: an exploratory proteomic study.

Extracellular vesicles (EVs) represent an attractive source of biomarkers due to their biomolecular cargo. The aim of this study was to identify candidate protein biomarkers from plasma-derived EVs of patients with liver cirrhosis (LC) and hepatocellular carcinoma (HCC). Plasma-derived EVs from healthy participants (HP), LC, and HCC patients (eight samples each) were subjected to label-free quantitative proteomic analysis using LC-MS/MS. A total of 248 proteins were identified, and differentially expressed proteins (DEPs) were obtained after pairwise comparison. We found that DEPs mainly involve complement cascade activation, coagulation pathways, cholesterol metabolism, and extracellular matrix components. By choosing a panel of up- and down-regulated proteins involved in cirrhotic and carcinogenesis processes, TGFBI, LGALS3BP, C7, SERPIND1, and APOC3 were found to be relevant for LC patients, while LRG1, TUBA1C, TUBB2B, ACTG1, C9, HP, FGA, FGG, FN1, PLG, APOB and ITIH2 were associated with HCC patients, which could discriminate both diseases. In addition, we identified the top shared proteins in both diseases, which included LCAT, SERPINF2, A2M, CRP, and VWF. Thus, our exploratory proteomic study revealed that these proteins might play an important role in the disease progression and represent a panel of candidate biomarkers for the prognosis and diagnosis of LC and HCC.

Discovery of candidate biomarkers to discriminate between Korean and Japanese red seabream (Pagrus major) using metabolomics

Red seabream (Pagrus major) is widely consumed in East Asia. As nuclear wastewater is discharged into Japanese waterbodies, the country of origin of marine products must be accurately labeled. Here, we aimed to discover candidate metabolite biomarkers to discriminate between Korean and Japanese red seabream using LC-Orbitrap mass spectrometry. In total, 95 and 138 putative metabolites were detected via chromatographic separation of fish sampled in the warm and cold seasons, respectively. The spectrometric and chromatographic data were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. We identified 12 and 19 influential metabolites to discriminate between each origin fish in the warm and cold seasons, respectively, using variable importance in projection scores and p values. Anserine was further selected as a candidate biomarker based on receiver operating characteristic curve analysis. This study provides a basis for using anserine to determine the geographic origin of red seabream.

Combined targeted and untargeted high-resolution mass spectrometry analyses to investigate metabolic alterations in pompe disease

Pompe disease is a rare, lysosomal disorder, characterized by intra-lysosomal glycogen accumulation due to an impaired function of α-glucosidase enzyme. The laboratory testing for Pompe is usually performed by enzyme activity, genetic test, or urine glucose tetrasaccharide (Glc4) screening by HPLC. Despite being a good preliminary marker, the Glc4 is not specific for Pompe. The purpose of the present study was to develop a simple methodology using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for targeted quantitative analysis of Glc4 combined with untargeted metabolic profiling in a single analytical run to search for complementary biomarkers in Pompe disease. We collected 21 urine specimens from 13 Pompe disease patients and compared their metabolic signatures with 21 control specimens. Multivariate statistical analyses on the untargeted profiling data revealed Glc4, creatine, sorbitol/mannitol, L-phenylalanine, N-acetyl-4-aminobutanal, N-acetyl-L-aspartic acid, and 2-aminobenzoic acid as significantly altered in Pompe disease. This panel of metabolites increased sample class prediction (Pompe disease versus control) compared with a single biomarker. This study has demonstrated the potential of combined acquisition methods in LC-HRMS for Pompe disease investigation, allowing for routine determination of an established biomarker and discovery of complementary candidate biomarkers that may increase diagnostic accuracy, or improve the risk stratification of patients with disparate clinical phenotypes.

Group-shrinkage feature selection with a spatial network for mining DNA methylation data

Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.

Proteomic analysis of iTRAQ in melatonin-treated sheep epididymal epithelial cells.

During maturation, spermatozoa acquire motility and fertilizing capacity as they transit through the epididymis. Melatonin is a lipophilic hormone with multiple functions in regulating the fertility. Previous studies have shown that melatonin affected the capacitation or maturation of sperm in the epididymis. The aim of this study was to investigate the effects of melatonin on epididymal caput epithelial cells in sheep. In the study, we used iTRAQ labelling coupled with LC-MS/MS for quantitative identification of differentially expressed proteins in melatonin-treated sheep epididymal caput epithelial cells. We identified 69 differentially expressed protein; 41 were upregulated and 28 were downregulated in samples from sheep in melatonin treated. We validated the differential expression of a subset of these proteins using qPCR and Western blot. Gene ontology annotation identified that the differentially expressed proteins function in cellular processes and metabolic processes. Notably, five of the differentially expressed proteins as SOD1, COL1A1, PRM1, NQO2, and FN1 are involved in sperm migration and sperm maturation. KEGG enrichment analysis demonstrated significant enrichment in several cardiac-related pathways, such as "PI3K-Akt signaling pathway", "AGE-RAGE signaling pathway in diabetic complications", "ECM-receptor interaction", and "Ribosome". Our results suggest that candidate biomarker (SOD1, COL1A1, PRM1, NQO2, and FN1) discovery can aid in understanding sperm development and maturation in sheep. These results provide insights into the potential mechanisms of melatonin regulation of sperm maturation in epididymal caput epithelial cells.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma.

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R2 > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Alterations in Self-Aggregating Neuropeptides in Cerebrospinal Fluid of Patients with Parkinsonian Disorders.

Background:Parkinson’s disease (PD), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA) present with similar movement disorder symptoms but distinct protein aggregates upon pathological examination.Objective:Discovery and validation of candidate biomarkers in parkinsonian disorders for differential diagnosis of subgroup molecular etiologies.Methods:Untargeted liquid chromatography (LC)-mass spectrometry (MS) proteomics was used for discovery profiling in cerebral spinal fluid (CSF) followed by LC-MS/MS based multiple reaction monitoring for validation of candidates. We compared clinical variation within the parkinsonian cohort including PD subgroups exhibiting tremor dominance (TD) or postural instability gait disturbance and those with detectable leukocytes in CSF.Results:We have identified candidate peptide biomarkers and validated related proteins with targeted quantitative multiplexed assays. Dopamine-drug naïve patients at first diagnosis exhibit reduced levels of signaling neuropeptides, chaperones, and processing proteases for packaging of self-aggregating peptides into dense core vesicles. Distinct patterns of biomarkers were detected in the parkinsonian disorders but were not robust enough to offer a differential diagnosis. Different biomarker changes were detected in male and female patients with PD. Subgroup specific candidate biomarkers were identified for TD PD and PD patients with leukocytes detected in CSF.Conclusion:PD, MSA, and PSP exhibit overlapping as well as distinct protein biomarkers that suggest specific molecular etiologies. This indicates common sensitivity of certain populations of selectively vulnerable neurons in the brain, and distinct therapeutic targets for PD subgroups. Our report validates a decrease in CSF levels of self-aggregating neuropeptides in parkinsonian disorders and supports the role of native amyloidogenic proteins in etiologies of neurodegenerative diseases.

Periodic Artifact Removal With Applications to Deep Brain Stimulation

Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson’s disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time. Code for our proposed algorithm is publicly available on Github.

Prognostic/predictive markers in systemic therapy resistance and metastasis in breast cancer.

Breast cancer is a highly heterogeneous group of diseases posing a significant challenge in biomarker-driven research and the development of effective targeted therapies. Especially the treatment of metastatic breast cancer poses even more challenges, as we still lose more than 42,000 women and men each year in the United States alone. New biological insight helps to improve breast cancer treatment through early detection, adaptation to chemotherapy resistance, and tailoring to find the right size of care. This review focuses on existing and new areas of predictive biomarkers under development to tailor the management of breast cancer and the application of integrative approaches that have resulted in the promising candidate biomarker discovery. Furthermore, we review new methods to detect metastatic progression using imaging, and blood-based assays. We hope to increase the attention and awareness of a new generation of therapeutic development strategies in metastatic breast cancer.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Defining NASH from a Multi-Omics Systems Biology Perspective.

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

The Influence of Tonality on Auditory Sensitivity in Different Types of Musical Texture

Bovine tuberculosis (bTB), caused by Mycobacterium bovis (M. bovis) infection, disrupts conservation programs of threatened species such as the white rhinoceros (Ceratotherium simum). Interferon gamma release assays have been developed for the diagnosis of M. bovis infection in rhinoceros, however, the discovery of additional diagnostic biomarkers might improve the accuracy of case detection. The aim of this pilot study was therefore to evaluate a novel unbiased approach to candidate biomarker discovery and preliminary validation. Whole blood samples from twelve white rhinoceros were incubated in Nil and TB antigen tubes of the QuantiFERON® TB Gold (In-Tube) system after which RNA was extracted and reverse transcribed. Using the equine RT2 profiler PCR array, relative gene expression analysis of samples from two immune sensitized rhinoceros identified CCL4, CCL8, IL23A, LTA, NODAL, TNF, CSF3, CXCL10 and GPI as upregulated in response to antigen stimulation. Novel gene expression assays (GEAs) were designed for selected candidates, i.e. CCL4, CXCL10 and IFNG, and analysis of QFT-processed samples showed the CXCL10 GEA could distinguish between five M. bovis-infected and five uninfected rhinoceros. These findings confirm the value of the equine RT2 profiler PCR array as a useful tool for screening biomarkers for the diagnosis of M. bovis infection in rhinoceros.

A Systematic Review of Metabolomic Biomarkers for the Intake of Sugar-Sweetened and Low-Calorie Sweetened Beverages

Intake of added sugars (AS) is challenging to assess compared with total dietary sugar because of the lack of reliable assessment methods. The reliance on self-reported dietary data in observational studies is often cited as biased, with evidence of AS intake in relation to health outcomes rated as low to moderate quality. Sugar-sweetened beverages (SSBs) are a major source of AS. A regular and high intake of SSBs is associated with an overall poor diet, weight gain, and cardiometabolic risks. An elevated intake of low-calorie sweetened beverages (LCSBs), often regarded as healthier alternatives to SSBs, is also increasingly associated with increased risk for metabolic dysfunction. In this review, we systematically collate evidence and provide perspectives on the use of metabolomics for the discovery of candidate biomarkers associated with the intake of SSBs and LCSBs. We searched the Medline, Embase, Scopus, and Web of Science databases until the end of December 2020. Seventeen articles fulfilled our inclusion criteria. We evaluated specificity and validity of the identified biomarkers following Guidelines for Biomarker of Food Intake Reviews (BFIRev). We report that the 13C:12C carbon isotope ratio (δ13C), particularly, the δ13C of alanine is the most robust, sensitive, and specific biomarker of SSBs intake. Acesulfame-K, saccharin, sucralose, cyclamate, and steviol glucuronide showed moderate validity for predicting the short-term intake of LCSBs. More evidence is required to evaluate the validity of other panels of metabolites associated with the intake of SSBs.