Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Behavioral Assays Dissecting NMDA Receptor Function in Zebrafish.

Zebrafish are a powerful system to study brain development and to dissect the activity of complex circuits. One advantage is that they display complex behaviors, including prey capture, learning, responses to photic and acoustic stimuli, and social interaction (Dreosti et al., Front Neural Circuits 9:39, 2015; Bruckner et al., PLoS Biol 20:e3001838, 2022; Zoodsma et al., Mol Autism 13:38, 2022) that can be probed to assess brain function. Many of these behaviors are easily assayed at early larval stages, offering a noninvasive and high-throughput readout of nervous system function. Additionally, larval zebrafish readily uptake small molecules dissolved in water making them ideal for behavioral-based drug screens. Together, larval zebrafish and their behavioral repertoire offer a means to rapidly dissect brain circuitry and can serve as a template for high-throughput small molecule screens.NMDA receptor subunits are highly conserved in zebrafish compared to mammals (Zoodsma et al., Mol Autism 13:38, 2022; Cox et al., Dev Dyn 234:756-766, 2005; Zoodsma et al., J Neurosci 40:3631-3645, 2020). High amino acid and domain structure homology between humans and zebrafish underlie conserved functional similarities. Here we describe a set of behavioral assays that are useful to study the NMDA receptor activity in brain function.

Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.

High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes are high-dimensional, unbiased approaches to detect and visualize the changes in phenotypes are still needed. Here, we suggest that changes in cellular phenotype can be visualized in reduced dimensionality representations of the image feature space. We describe a freely available analysis pipeline to visualize changes in protein localization in feature spaces obtained from deep learning. As an example, we use the pipeline to identify changes in subcellular localization after the yeast GFP collection was treated with hydroxyurea.

High-throughput tagging of endogenous loci for rapid characterization of protein function.

To facilitate the interrogation of protein function at scale, we have developed high-throughput insertion of tags across the genome (HITAG). HITAG enables users to rapidly produce libraries of cells, each with a different protein of interest C-terminally tagged. HITAG is based on a modified strategy for performing Cas9-based targeted insertions, coupled with an improved approach for selecting properly tagged lines. Analysis of the resulting clones generated by HITAG reveals high tagging specificity, with most successful tagging events being indel free. Using HITAG, we fuse mCherry to a set of 167 stress granule-associated proteins and elucidate the features that drive a subset of proteins to strongly accumulate within these transient RNA-protein granules.

Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity.

Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.

A high-throughput differential scanning fluorimetry method for rapid detection of thermal stability and iron saturation in lactoferrin.

Thermal stability and iron saturation of lactoferrin (LF) are of great significance not only for the evaluation of the biological activities of LF but also for the optimization of the isolation and drying process parameters. Differential scanning calorimetry (DSC) is a well-established and efficient method for thermal stability and iron saturation detection in LF. However, multiple DSC measurements are typically performed sequentially, thus time-consuming and low throughput. Herein, we introduced the differential scanning fluorimetry (DSF) approach to overcome such limitations. The DSF can monitor LF thermal unfolding with a commonly available real-time PCR instrument and a fluorescent dye (SYPRO orange or Glomelt), and the measured melting temperature of LF is consistent with that determined by DSC. On the basis of that, a new quantification method was established for determination of iron saturation levels using the linear correlation of the degree of ion saturation of LF with DSF measurements. Such DSF method is simple, inexpensive, rapid (<15 min), and high throughput (>96 samples per experiment), and provides a valuable alternative tool for thermal stability detection of LF and other whey proteins.

The small molecule CBR-5884 inhibits the Candida albicans phosphatidylserine synthase.

Systemic infections by Candida spp. are associated with high mortality rates, partly due to limitations in current antifungals, highlighting the need for novel drugs and drug targets. The fungal phosphatidylserine synthase, Cho1, from Candida albicans is a logical antifungal drug target due to its importance in virulence, absence in the host, and conservation among fungal pathogens. Inhibitors of Cho1 could serve as lead compounds for drug development, so we developed a target-based screen for inhibitors of purified Cho1. This enzyme condenses serine and cytidyldiphosphate-diacylglycerol (CDP-DAG) into phosphatidylserine (PS) and releases cytidylmonophosphate (CMP). Accordingly, we developed an in vitro nucleotidase-coupled malachite-green-based high throughput assay for purified C. albicans Cho1 that monitors CMP production as a proxy for PS synthesis. Over 7,300 molecules curated from repurposing chemical libraries were interrogated in primary and dose-responsivity assays using this platform. The screen had a promising average Z' score of ~0.8, and seven compounds were identified that inhibit Cho1. Three of these, ebselen, LOC14, and CBR-5884, exhibited antifungal effects against C. albicans cells, with fungicidal inhibition by ebselen and fungistatic inhibition by LOC14 and CBR-5884. Only CBR-5884 showed evidence of disrupting in vivo Cho1 function by inducing phenotypes consistent with the cho1∆∆ mutant, including a reduction of cellular PS levels. Kinetics curves and computational docking indicate that CBR-5884 competes with serine for binding to Cho1 with a Ki of 1,550 ± 245.6 nM. Thus, this compound has the potential for development into an antifungal compound. IMPORTANCE: Fungal phosphatidylserine synthase (Cho1) is a logical antifungal target due to its crucial role in the virulence and viability of various fungal pathogens, and since it is absent in humans, drugs targeted at Cho1 are less likely to cause toxicity in patients. Using fungal Cho1 as a model, there have been two unsuccessful attempts to discover inhibitors for Cho1 homologs in whole-cell screens prior to this study. The compounds identified in these attempts do not act directly on the protein, resulting in the absence of known Cho1 inhibitors. The significance of our research is that we developed a high-throughput target-based assay and identified the first Cho1 inhibitor, CBR-5884, which acts both on the purified protein and its function in the cell. This molecule acts as a competitive inhibitor with a Ki value of 1,550 ± 245.6 nM and, thus, has the potential for development into a new class of antifungals targeting PS synthase.

A High-Throughput Approach for Photosynthesis Studies in a Brassicaceae Panel.

The study of natural variations in photosynthesis in the Brassicaceae family offers the possibility of identifying mechanisms to enhance photosynthetic efficiency in crop plants. Indeed, this family, and particularly its tribe Brassiceae, has been shown to harbor species that have a higher-than-expected photosynthetic efficiency, possibly as a result of a complex evolutionary history. Over the past two decades, methods have been developed to measure photosynthetic efficiency based on chlorophyll fluorescence. Chlorophyll fluorescence measurements are performed with special cameras, such as the FluorCams, which can be included in robotic systems to create high-throughput phenotyping platforms. While these platforms have so far demonstrated high efficiency in measuring small model species like Arabidopsis thaliana, they have the drawback of limited adaptability to accommodate different plant sizes. As a result, the range of species that can be analyzed is restricted. This chapter presents our approach to analyze the photosynthetic parameters: ϕPSII and Fv/Fm for a panel of Brassicaceae species, including a high-photosynthesis species, Hirschfeldia incana, and the adaptations to the phenotyping platform that are required to accommodate this varied group of plants.

High-throughput viscoelastic characterization of cells in hyperbolic microchannels.

Extensive research has demonstrated the potential of cell viscoelastic properties as intrinsic indicators of cell state, functionality, and disease. For this, several microfluidic techniques have been developed to measure cell viscoelasticity with high-throughput. However, current microchannel designs introduce complex stress distributions on cells, leading to inaccuracies in determining the stress-strain relationship and, consequently, the viscoelastic properties. Here, we introduce a novel approach using hyperbolic microchannels that enable precise measurements under a constant extensional stress and offer a straightforward stress-strain relationship, while operating at a measurement rate of up to 100 cells per second. We quantified the stresses acting in the channels using mechanical calibration particles made from polyacrylamide (PAAm) and found that the measurement buffer, a solution of methyl cellulose and phosphate buffered saline, shows strain-thickening following a power law up to 200 s-1. By measuring oil droplets with varying viscosities, we successfully detected changes in the relaxation times of the droplets and our approach could be used to get the interfacial tension and viscosity of liquid-liquid droplet systems from the same measurement. We further applied this methodology to PAAm microgel beads, demonstrating the accurate recovery of Young's moduli and the near-ideal elastic behavior of the beads. To explore the influence of altered cell viscoelasticity, we treated HL60 human leukemia cells with latrunculin B and nocodazole, resulting in clear changes in cell stiffness while relaxation times were only minimally affected. In conclusion, our approach offers a streamlined and time-efficient solution for assessing the viscoelastic properties of large cell populations and other microscale soft particles.

Optimization and validation of high throughput methods for the determination of 132 organic contaminants in green and roasted coffee using GC-QqQ-MS/MS and LC-QqQ-MS/MS.

Recently some major safety concerns have been raised on organic contaminants in widely consumed plants such as coffee. Hence, this study aimed to develop specifically optimized methods for determining organic contaminants, such as pesticides and polychlorinated biphenyls (PCBs), in coffee using GC-MS/MS and LC-MS/MS. QuEChERS method was used as a base extraction method, and 27 experiments were studied using design of experiments with categorical variables (extraction buffers, cleanup sorbents, and coffee roasting degree) to find the optimum method for each matrix type. The optimum method for green coffee was acetate buffer and chitosan for clean-up, while no-buffer extraction and the PSA + C18 method were ideal for light and dark-roasted coffee. The optimized methods were validated in accordance with SANTE/11312/2021. Furthermore, ten real samples (4 green, and 6 roasted) from the markets were analysed; ortho-phenylphenol was found in all the roasted coffee samples, and carbendazim was found in one green coffee sample.

AI is a viable alternative to high throughput screening: a 318-target study.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

A new high-throughput screening methodology for the discovery of cancer-testis antigen using multi-omics data.

BACKGROUND: Cancer/testis antigens (CTAs), also known as tumor-specific antigens (TSAs) are specifically expressed in cancer cells and exhibit high immunogenicity, making them promising targets for immunotherapy and cancer vaccines. METHODS: A new integrated high-throughput screening methodology for CTAs was proposed in this study through combining DNA methylation and RNA sequencing data. Briefly, the genes with increased transcript level and decreased DNA methylation were identified by multi-omics analysis. RNA sequencing studies in cell lines exposed to DNA methyltransferase (DNMT) inhibitors were performed to validate the inherent causal relationship between DNA hypomethylation and gene expression upregulation. RESULTS: We proposed a new integrated high-throughput screening methodology for identification of CTAs using multi-omics analysis. In addition, we tested the feasibility of this method using gastric cancer (GC) as an example. In GC, we identified over 2000 primary candidate CTAs and ultimately identified 20 CTAs with significant tissue-specificity, including a testis-specific serine protease TESSP1/PRSS41. Integrated analysis confirmed that PRSS41 expression was reactivated in gastrointestinal cancers by promoter DNA hypomethylation at the CpG site (cg08104780). Additionally, DNA hypomethylation of PRSS41 predicted a poor prognosis in GC. CONCLUSION: We propose a new high-throughput screening method for the identification of CTAs in cancer and validate its effectiveness. Our work emphasizes that serine protease PRSS41 is a novel TSA that is reactivated in GC due to promoter DNA hypomethylation.

The Discovery of New Inhibitors of Insulin-Regulated Aminopeptidase by a High-Throughput Screening of 400,000 Drug-like Compounds.

With the ambition to identify novel chemical starting points that can be further optimized into small drug-like inhibitors of insulin-regulated aminopeptidase (IRAP) and serve as potential future cognitive enhancers in the clinic, we conducted an ultra-high-throughput screening campaign of a chemically diverse compound library of approximately 400,000 drug-like small molecules. Three biochemical and one biophysical assays were developed to enable large-scale screening and hit triaging. The screening funnel, designed to be compatible with high-density microplates, was established with two enzyme inhibition assays employing either fluorescent or absorbance readouts. As IRAP is a zinc-dependent enzyme, the remaining active compounds were further evaluated in the primary assay, albeit with the addition of zinc ions. Rescreening with zinc confirmed the inhibitory activity for most compounds, emphasizing a zinc-independent mechanism of action. Additionally, target engagement was confirmed using a complementary biophysical thermal shift assay where compounds causing positive/negative thermal shifts were considered genuine binders. Triaging based on biochemical activity, target engagement, and drug-likeness resulted in the selection of 50 qualified hits, of which the IC50 of 32 compounds was below 3.5 µM. Despite hydroxamic acid dominance, diverse chemotypes with biochemical activity and target engagement were discovered, including non-hydroxamic acid compounds. The most potent compound (QHL1) was resynthesized with a confirmed inhibitory IC50 of 320 nM. Amongst these compounds, 20 new compound structure classes were identified, providing many new starting points for the development of unique IRAP inhibitors. Detailed characterization and optimization of lead compounds, considering both hydroxamic acids and other diverse structures, are in progress for further exploration.

High-Throughput µPAD with Cascade Signal Amplification through Dual Enzymes for arsM in Paddy Soil.

The arsM gene is a critical biomarker for the potential risk of arsenic exposure in paddy soil. However, on-site screening of arsM is limited by the lack of high-throughput point-of-use (POU) methods. Here, a multiplex CRISPR/Cas12a microfluidic paper-based analytical device (µPAD) was constructed for the high-throughput POU analysis of arsM, with cascade amplification driven by coupling crRNA-enhanced Cas12a and horseradish peroxidase (HRP)-modified probes. First, seven crRNAs were designed to recognize arsM, and their LODs and background signal intensities were evaluated. Next, a step-by-step iterative approach was utilized to develop and optimize coupling systems, which improved the sensitivity 32 times and eliminated background signal interference. Then, ssDNA reporters modified with HRP were introduced to further lower the LOD to 16 fM, and the assay results were visible to the naked eye. A multiplex channel microfluidic paper-based chip was developed for the reaction integration and simultaneous detection of 32 samples and generated a recovery rate between 87.70 and 114.05%, simplifying the pretreatment procedures and achieving high-throughput POU analysis. Finally, arsM in Wanshan paddy soil was screened on site, and the arsM abundance ranged from 1.05 × 106 to 6.49 × 107 copies/g; this result was not affected by the environmental indicators detected in the study. Thus, a coupling crRNA-based cascade amplification method for analyzing arsM was constructed, and a microfluidic device was developed that contains many more channels than previous paper chips, greatly improving the analytical performance in paddy soil samples and providing a promising tool for the on-site screening of arsM at large scales.

Using Infrared Thermography for High-Throughput Plant Phenotyping.

Infrared thermography offers a rapid, noninvasive method for measuring plant temperature, which provides a proxy for stomatal conductance and plant water status and can therefore be used as an index for plant stress. Thermal imaging can provide an efficient method for high-throughput screening of large numbers of plants. This chapter provides guidelines for using thermal imaging equipment and illustrative methodologies, coupled with essential considerations, to access plant physiological processes.

Protocol for kinetic mode potassium channel assays on common plate readers and microscopes.

Fluorescence-based potassium channel assays are typically run on expensive, hard to obtain, fluorescence imaging kinetic plate readers that are uncommon in most laboratories. Here we describe the use of the Brilliant Thallium Snapshot assay to conduct an endpoint potassium channel assay, so that it can be used across multiple plate reader platforms that are more common in many labs. These methods will allow users to identify modulators of potassium channels. For this work, we have taken a kinetic mode Molecular Devices FLIPR based protocol and adapted it to be utilized on endpoint plate readers, such as the BMG Labtech PHERAstar, to identify activators of GIRK channels in CHO cells. We demonstrate that both plate readers are functionally competent at generating excellent Z' values which makes them ideally suited to finding corollary hits from the Sigma LOPAC 1,280 screening collection. Importantly, this assay has also been validated using a high content reader, demonstrating the possibility of spatially resolving signals from individual cells within a mixed cell population. The compendium of these results shows the flexibility, accessibility and functionality of endpoint-compatible potassium channel assay readouts on more common plate readers.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

Droplet-based microfluidic platform for detecting agonistic peptides that are self-secreted by yeast expressing a G-protein-coupled receptor.

BACKGROUND: Single-cell droplet microfluidics is an important platform for high-throughput analyses and screening because it provides an independent and compartmentalized microenvironment for reaction or cultivation by coencapsulating individual cells with various molecules in monodisperse microdroplets. In combination with microbial biosensors, this technology becomes a potent tool for the screening of mutant strains. In this study, we demonstrated that a genetically engineered yeast strain that can fluorescently sense agonist ligands via the heterologous expression of a human G-protein-coupled receptor (GPCR) and concurrently secrete candidate peptides is highly compatible with single-cell droplet microfluidic technology for the high-throughput screening of new agonistically active peptides. RESULTS: The water-in-oil microdroplets were generated using a flow-focusing microfluidic chip to encapsulate engineered yeast cells coexpressing a human GPCR [i.e., angiotensin II receptor type 1 (AGTR1)] and a secretory agonistic peptide [i.e., angiotensin II (Ang II)]. The single yeast cells cultured in the droplets were then observed under a microscope and analyzed using image processing incorporating machine learning techniques. The AGTR1-mediated signal transduction elicited by the self-secreted Ang II peptide was successfully detected via the expression of a fluorescent reporter in single-cell yeast droplet cultures. The system could also distinguish Ang II analog peptides with different agonistic activities. Notably, we further demonstrated that the microenvironment of the single-cell droplet culture enabled the detection of rarely existing positive (Ang II-secreting) yeast cells in the model mixed cell library, whereas the conventional batch-culture environment using a shake flask failed to do so. Thus, our approach provided compartmentalized microculture environments, which can prevent the diffusion, dilution, and cross-contamination of peptides secreted from individual single yeast cells for the easy identification of GPCR agonists. CONCLUSIONS: We established a droplet-based microfluidic platform that integrated an engineered yeast biosensor strain that concurrently expressed GPCR and self-secreted the agonistic peptides. This offers individually isolated microenvironments that allow the culture of single yeast cells secreting these peptides and gaging their signaling activities, for the high-throughput screening of agonistic peptides. Our platform base on yeast GPCR biosensors and droplet microfluidics will be widely applicable to metabolic engineering, environmental engineering, and drug discovery.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry.

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.