Enzymatic Methods for Salivary Biomarkers Detection: Overview and Current Challenges

Early detection is a key factor in patient fate. Currently, multiple biomolecules have been recognized as biomarkers. Nevertheless, their identification is only the starting line on the way to their implementation in disease diagnosis. Although blood is the biofluid par excellence for the quantification of biomarkers, its extraction is uncomfortable and painful for many patients. In this sense, there is a gap in which saliva emerges as a non-invasive and valuable source of information, as it contains many of the biomarkers found in blood. Recent technological advances have made it possible to detect and quantify biomarkers in saliva samples. However, there are opportunity areas in terms of cost and complexity, which could be solved using simpler methodologies such as those based on enzymes. Many reviews have focused on presenting the state-of-the-art in identifying biomarkers in saliva samples. However, just a few of them provide critical analysis of technical elements for biomarker quantification in enzymatic methods for large-scale clinical applications. Thus, this review proposes enzymatic assays as a cost-effective alternative to overcome the limitations of current methods for the quantification of biomarkers in saliva, highlighting the technical and operational considerations necessary for sampling, method development, optimization, and validation.     

Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry

Fecal metabolome of healthy humans and patients suffering from liver cirrhosis and hepatocellular carcinoma (HCC) were studied using ultra performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF MS). Metabolic features detected by the method were then statistically treated using partial least squares to latent structure-discriminant analysis (PLS-DA) models to discriminate between healthy and diseased states. PLS-DA was also used to discriminate between cirrhosis and HCC stressed fecal metabolomes and to identify potential biomarkers for cirrhosis and HCC that are expressed at significantly different amounts in fecal metabolomes. Score plots of pattern recognition analysis distinguished liver cirrhosis and HCC patients from healthy humans. Based on the variable of importance in the project (VIP) values and S-plots, six metabolites were considered as potential biomarkers with a strong increase in lysophosphatidylcholines and a dramatic decrease in bile acids and bile pigments in patients with liver cirrhosis and HCC in comparison with healthy humans. Results demonstrate the potential of UPLC-MS as an efficient and convenient method that can be applied to screen fecal samples and aid in the early diagnosis of cirrhosis and hepatocellular carcinoma.     

Use of MDLC-DIGE and LC-MS/MS to identify serum biomarkers for complete remission in patients with acute myeloid leukemia

The response criteria for complete remission (CR) in acute myeloid leukemia (AML) are currently based on morphology and blood cell counts. However, these criteria are insufficient to establish a diagnosis in cases with poor quality bone marrow (BM) samples demonstrating a loss of cellular morphology. We investigated whether the sera of patients contained biomarkers that indicate disease response status. First, we performed multidimensional liquid chromatography-differential gel electrophoresis (MDLC-DIGE) to generate protein profiles of two pooled, paired serum samples from patients who had achieved CR; one collected at diagnosis (PreCR) and the other collected after chemotherapy (CR). Then, with the biomarker candidates found, ELISA was carried out for individual PreCR and CR samples, and for other verification sets including nonremission (NR) patients and normal samples. We selected two proteins, complement factor H (CFH) and apolipoprotein H (ApoH), with dye (Cy) ratios showing greater than 2.0-fold differences between the pooled samples. ELISA showed that CFH and ApoH are useful for distinguishing between the recovered (CR and normal) and nonrecovered (PreCR, PreNR, and NR) states in AML (p <0.001). We successfully applied a protein profiling technology of MDLC-DIGE and LC-MS/MS to discover two biomarkers for CR which needs further validation for a clinical setting.     

Study of urinary steroid hormone disorders: difference between hepatocellular carcinoma in early stage and cirrhosis

Hepatocellular carcinoma (HCC) is the fifth most common cancer in the world. Discovery of novel biomarkers for early HCC from other liver diseases such as cirrhosis is of great clinical benefit. In this study, a novel steroid hormone metabolomic method based on liquid chromatography–mass spectrometry combined with logistic regression analysis was applied to study the steroid hormone disorders and to screen potential urinary steroid hormone biomarkers of early HCC. Thirty-six urinary steroid hormones were detected and quantified in healthy controls, cirrhotic patients, and early HCC patients. Heat map analysis and multivariate statistical analysis suggested severe disorders of steroid hormone network and holistically decreased urinary steroid hormone pattern in cirrhotic and early HCC patients. Logistic regression analysis reveals that a panel of two urinary steroid hormones (epitestosterone and allotetrahydrocortisol) displayed excellent diagnostic capability for distinguishing early HCC from cirrhosis with area under the curve (AUC)?=?0.938 of receiver operating characteristic (ROC) analysis. These results help to overcome the disadvantage of lower sensitivity and specificity of alpha-fetoprotein for distinguishing early HCC from cirrhosis. Our work shows that steroid hormone metabolomics is a promising biomarker tool for biomarker study of early HCC.

The protein folding network indicates that the ultrafast folding mutant of villin headpiece subdomain has a deeper folding funnel

Protein folding is a dynamic process with continuous transitions among different conformations. In this work, the dynamics in the protein folding network of villin headpiece subdomain (HP35) has been investigated based on multiple reversible folding trajectories of HP35 and its ultrafast folding mutant where sub-angstrom folding was achieved. The four folding states were clearly separated on the network, validating the classification of the states. Examination of the eight conformers with different formation of the individual helices revealed high plasticity of the three helices in all the four states. A consistent feature between the wild type and mutant protein is the dominant conformer 111 (all three helices formed) in the folded state and conformers 111 and 011 (helices II and III formed) in the major intermediate state, indicating the critical role of helices II and III in the folding mechanism. When compared to the wild type, the folding landscape of the ultrafast folding mutant exhibited a deeper folding funnel towards the folded state. The very beginning of the folding (0-10 ns) was very similar for both protein variants but it soon diverged and displayed different folding pathways. Although going through the major intermediate state is the dominant pathway for both, it was also observed that some folding went through the minor intermediate state for the mutant. The intriguing difference resulting from the mutation at two residues in helix III has been carefully analyzed and discussed in details.     

High-throughput biomarker discovery and identification by mass spectrometry

Native peptides and proteins are of increasing interest in biomedical research because they hold promise to represent a large number of useful diagnostic and therapeutic biomarkers. Discovery attempts from patient samples have to deal with the complexity of biology from a disease perspective as well as with a high individual variability. High throughput screening of samples is therefore the strategy of choice to detect relevant peptidic biomarkers, and requires a high order of automation particularly in the detection process. In this contribution, a novel technical approach employing a fully automated MALDI-TOF/TOF mass spectrometer is described. This approach combines high throughput biomarker discovery with the identification of corresponding endogenous peptides in one instrument and from the same set of samples. The degree of automation allows the analysis of thousands of chromatographic fractions corresponding to up to one hundred patient samples per day. The applied relative quantification via Differential Peptide Display((R)) is performed in a label-free way and shows a dynamic range of up to four orders of magnitude in the accessible peptide concentrations. The typical limit of detection is in the mid- to low-picomolar range for body fluids such as blood plasma, urine and cerebrospinal fluid. Sequence assignment via MALDI-TOF/TOF mass spectrometry is carried out either in an overview approach, characterizing rapidly the peptide composition e.g. of a novel sample, or in a directed approach, analyzing a list of biomarker candidates deduced from statistically significant abundance differences from the biomarker discovery process.     

Blood-based Alzheimer's disease diagnosis using fluorescent peptide nanoparticle arrays

There is a critical need to diagnose and monitor the progression of Alzheimer’s disease(AD) using bloodbased biomarkers. At present, it is believed that tau biomarkers can be utilized to reliably detect AD.Multimodal techniques are highly sought after for AD diagnosis and progression monitoring. For this purpose, we developed a fluorescent peptide nanoparticles(f-PNPs) arrays that is capable of detecting multiple signals simultaneously. The concentration, aggregation stages, and Young’s modulus ...     

Applications of aptamers in cancer cell biology

Identifying cells associated with specific disease states is critically important for the early detection and diagnosis of cancer. To facilitate this task, molecular probes, which bind biomarkers that are either specifically or differentially expressed in diseased cells relative to healthy cells, provide a simple and effective method. This review focuses on the use of DNA aptamers as molecular probes for cancer cells. These aptamers are created by means of the cell-based Systematic Evolution of Ligands by EXponential enrichment (SELEX) process, which uses whole disease cells as targets. We describe at length the steps of the cell-SELEX process and discuss several applications for the aptamers, including profiling leukemia patient samples and discovering cell-surface cancer biomarkers. We conclude with a discussion of an aptamer-conjugated nanoparticle enrichment and detection scheme.     

Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes

Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.     

From Chemistry to Translational Medicine: The Application of Proteomics to Cancer Biomarker Discovery and Diagnosis

The study of complex protein mixtures and their interactions in cells and tissues has been difficult due to the tedious process involved in their characterization and analysis. The recent emergence of fast‐evolving and state‐of‐the‐art proteomics methodologies has provided a rapid and scalable platform for understanding the comprehensive proteome profiles from complex whole tissues or cells of various biological sources. Therefore, proteomics has been increasingly valuable to examine real‐time changes in protein expression of various tissues or body fluids from patients with various diseases, especially cancer, resulting in the identification of clinically useful biomarkers for diagnosis, prognosis and disease staging. In this review, we focus on potential biomarkers for (1) Helicobacter pylori‐associated gastric cancer, (2) hepatocellular carcinoma (HCC), and (3) renal cell carcinoma (RCC). In addition to the conventional gel‐based proteomics (1‐D or 2‐D gels), we have utilized a more advanced proteomic approach by incorporating stable isotope dimethyl labelling and shotgun proteomics strategy in combination with nanoliquid chromatography and tandem mass spectrometry (nanoLC‐MS/MS) to better characterize the biomarkers in several cancer tissues. By establishing a high‐throughput proteomics platform based on multiple reaction monitoring (MRM), we have successfully detected and analyzed potential protein markers at low concentrations in various normal and tumor tissues. This platform not only highlights the utility of proteomics for biomarker discovery but also can be uniquely applied to disease‐oriented translational medicine for diagnosis of diverse types of cancers and other diseases.     

Microfluidic systems for the analysis of blood-derived molecular biomarkers

Biomarkers are relevant indicators of the physiological state of an individual. Although biomarkers can be found in diseased tissue and different biofluids, sampling from blood plasma is relatively easy and less invasive. Among the molecular biomarkers that can be found circulating in plasma are proteins, metabolites, nucleic acids, and exosomes. Some of these plasma-circulating biomarkers are now employed for patient stratification in a broad range of diseases with high sensitivity and specificity and are useful in early diagnosis, initial risk assessment, and therapy selection. However, there is a pressing need to develop novel approaches for biomarker analysis that can be translated into clinical or other settings without complex methodologies or instrumentation. Microfluidics has been touted as a promising technology to carry out this task because it offers high-throughput, automation, multiplexed detection, and portability, possibly overcoming the bottleneck that prevent the translation of novel biomarkers to the point-of-care (POC). Here, we provide a review of the microfluidic systems that have been engineered to detect circulating molecular biomarkers in blood plasma. We also review the different microfluidic approaches for plasma enrichment, which are now being integrated with microfluidic-based biomarker analyzers. Such integration should lead to cost-effective solutions in in vitro diagnostics, with special relevance to POC platforms.     

Simultaneous Quantification of Multiple Cancer Biomarkers in Blood Samples through DNA‐Assisted Nanopore Sensing

Protein biomarkers in blood have been widely used in the early diagnosis of disease. However, simultaneous detection of many biomarkers in a single sample remains challenging. Herein, we show that the combination of a sandwich assay and DNA‐assisted nanopore sensing could unambiguously identify and quantify several antigens in a mixture. We use five barcode DNAs to label different gold nanoparticles that can selectively bind specific antigens. After the completion of the sandwich assay, barcode DNAs are released and subject to nanopore translocation tests. The distinct current signatures generated by each barcode DNA allow simultaneous quantification of biomarkers at picomolar level in clinical samples. This approach would be very useful for accurate and multiplexed quantification of cancer‐associated biomarkers within a very small sample volume, which is critical for non‐invasive early diagnosis of cancer.     

Genetically Encoded Ratiometric RNA‐Based Sensors for Quantitative Imaging of Small Molecules in Living Cells

Precisely determining the intracellular concentrations of metabolites and signaling molecules is critical in studying cell biology. Fluorogenic RNA‐based sensors have emerged to detect various targets in living cells. However, it is still challenging to apply these genetically encoded sensors to quantify the cellular concentrations and distributions of targets. Herein, using a pair of orthogonal fluorogenic RNA aptamers, DNB and Broccoli, we engineered a modular sensor system to apply the DNB‐to‐Broccoli fluorescence ratio to quantify the cell‐to‐cell variations of target concentrations. These ratiometric sensors can be broadly applied for live‐cell imaging and quantification of metabolites, signaling molecules, and other synthetic compounds.     

New method for prefractionation of plasma for proteomic analysis

The depth of proteome analysis is severely limited in complex samples with a wide dynamic range of protein abundance such as plasma. Removal of high‐abundance proteins should reveal the signal of lower abundance plasma proteins. However, smaller proteins may be part of larger protein complexes and hence the removal of proteins involved in complexes with high‐abundance proteins such as albumin may inhibit the search for disease biomarkers. Prefractionation of a sample divides it into fractions of reduced complexity, allowing improved detection of lower abundance proteins. Using a prefractionation device, which employs Gradiflow? technology, we were able to separate small volume plasma samples into multiple fractions based on the molecular weight and/or charge. The resulting samples of reduced complexity were directly compatible with 2‐DE. The use of this prefractionation machine may therefore be useful in the hunt for disease biomarkers.     

Exploring the Trans‐Cleavage Activity of CRISPR‐Cas12a (cpf1) for the Development of a Universal Electrochemical Biosensor

An accurate, rapid, and cost‐effective biosensor for the quantification of disease biomarkers is vital for the development of early‐diagnostic point‐of‐care systems. The recent discovery of the trans‐cleavage property of CRISPR type V effectors makes CRISPR a potential high‐accuracy bio‐recognition tool. Herein, a CRISPR‐Cas12a (cpf1) based electrochemical biosensor (E‐CRISPR) is reported, which is more cost‐effective and portable than optical‐transduction‐based biosensors. Through optimizing the in vitro trans‐cleavage activity of Cas12a, E‐CRIPSR was used to detect viral nucleic acids, including human papillomavirus 16 (HPV‐16) and parvovirus B19 (PB‐19), with a picomolar sensitivity. An aptamer‐based E‐CRISPR cascade was further designed for the detection of transforming growth factor β1 (TGF‐β1) protein in clinical samples. As demonstrated, E‐CRISPR could enable the development of portable, accurate, and cost‐effective point‐of‐care diagnostic systems.     

Serum Proteomics in Biomedical Research: A Systematic Review

Proteins that are important indicators of physiological or pathological states may contribute to the early diagnosis of disease, which may provide a basis for identifying the underlying mechanism of disease development. Serum, contains an abundance of proteins, offers an easy and inexpensive approach for disease detection and possesses a high potential to revolutionize the diagnostics. These differentially expressed proteins in serum have become an important role to monitoring the state for disease. Availability of emerging proteomic techniques gives optimism that serum can eventually be placed as a biomedium for clinical diagnostics. Advancements have benefited biomarker research to the point where serum is now recognized as an excellent diagnostic medium for the detection of disease. Comprehensive proteome of human serum fluid with high accuracy and availability has the potential to open new doors for disease biomarker discovery and for disease diagnostics, providing insights useful for future study. Thus, this review presents an overview of the value of serum as a credible diagnostic tool, and we aim to summarize the proteomic technologies currently used for global analysis of serum proteins and to elaborate on the application of serum proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives for this emerging field.     

An Off‐Pathway Folding Intermediate of an Acyl Carrier Protein Domain Coexists with the Folded and Unfolded States under Native Conditions

A protein can exist in multiple states under native conditions and those states with low populations are often critical to biological function and self‐assembly. To investigate the role of the minor states of an acyl carrier protein, NMR techniques were applied to determine the number of minor states and characterize their structures and kinetics. The acyl carrier protein from Micromonospora echinospora was found to exist in one major folded state (95.2 %), one unfolded state (4.1 %), and one intermediate state (0.7 %) under native conditions. The three states are in dynamic equilibrium and the intermediate state very likely adopts a native‐like structure and is an off‐pathway folding product. The intermediate state may mediate the formation of oligomers in vitro and play an important role in the recognition of partner enzymes in vivo.     

基于质谱检测的相关色谱技术在阿尔茨海默病诊断中的应用

随着世界老年人口的急速增长,阿尔茨海默病发病人数也逐年增多,已成为继心脑血管疾病和恶性肿瘤之后威胁人类健康的“第三大杀手”。疾病的诊断和治疗同等重要,阿尔茨海默病诊断通常依靠典型的临床特征、神经影像技术以及检测疾病相关的生物标志物等。近些年来蛋白质组学和质谱技术迅速发展,可以利用这些技术寻找到与疾病相关的特异性的蛋白质分子作为早期诊断的生物标志物。本文就此进行了综述,主要包括基于蛋白质组学的诊断标志物的筛选和基于质谱检测的色谱技术在阿尔茨海默病诊断中的应用,引用文献34篇。     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

Serum metabolomics as a novel diagnostic approach for disease: a systematic review

Metabolomics is a promising "omics" field in systems biology; its objective is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could result in earlier intervention and provide valuable insights into the mechanisms of diseases. Because of the possible discovery of clinically relevant biomarkers, metabolomics has potential advantages that routine approaches to clinical diagnosis do not. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of a disease. Serum is a readily accessible and informative biofluid, making it ideal for early detection of a wide range of diseases, and analysis of serum has several advantages over analysis of other biofluids. Metabolite profiles of serum can be regarded as important indicators of physiological and pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level, and provide information enabling identification of early and differential metabolic markers of disease. Analysis of these crucial metabolites in serum has become important in monitoring the state of biological organisms and is widely used for diagnosis of disease. Emerging metabolomics will drive serum analysis, facilitate and improve the development of disease treatments, and provide great benefits for public health in the long-term.