Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites.

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis.

Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS.

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log KOW.

Preparation, Structural Characterization of Anti-Cancer Drugs-Mediated Self-Assembly from the Pluronic Copolymers through Synchrotron SAXS Investigation.

Chemotherapy drugs are mainly administered via intravenous injection or oral administration in a very a high dosage. If there is a targeted drug vehicle which can be deployed on the tumor, the medical treatment is specific and precise. Binary mixing of biocompatible Pluronic® F127 and Pluronic® L121 was used in this study for a drug carrier of pluronic biomedical hydrogels (PBHs). Based on the same PBH ingredients, the addition of fluorouracil (5-FU) was separated in three ways when it was incorporated with pluronics: F127-L121-(5-FU), F127-(5-FU), and L121-(5-FU). Small angle X-ray scattering experiments were performed to uncover the self-assembled structures of the PBHs. Meanwhile, the expected micelle and lamellar structural changes affected by the distribution of 5-FU were discussed with respect to the corresponding drug release monitoring. PBH-all with the mixing method of F127-L121-(5-FU) has the fastest drug release rate owing to the undulated amphiphilic boundary. In contrast, PBH-2 with the mixing method of L121-(5-FU) has a prolonged drug release rate at 67% for one month of the continuous drug release experiment because the flat lamellar amphiphilic boundary of PBH-2 drags the migration of 5-FU from the hydrophobic core. Therefore, the PBHs developed in the study possess great potential for targeted delivery and successfully served as a microenvironment model to elucidate the diffusion pathway of 5-FU.

RACK-1 regulates let-7 microRNA expression and terminal cell differentiation in Caenorhabditis elegans.

The let-7 microRNA (miRNA) regulates cell cycle exit and terminal differentiation in the C. elegans heterochronic gene pathway. Low expression of let-7 results in retarded vulva and hypodermal cell development in C. elegans and has been associated with several human cancers. Previously, the versatile scaffold protein receptor for activated C kinase 1 (RACK1) was proposed to facilitate recruitment of the miRNA-induced silencing complex (miRISC) to the polysome and to be required for miRNA function in C. elegans and humans. Here, we show that depletion of C. elegans RACK-1 by RNAi increases let-7 miRNA levels and suppresses the retarded terminal differentiation of lateral hypodermal seam cells in mutants carrying the hypomorphic let-7(n2853) allele or lacking the let-7 family miRNA genes mir-48 and mir-241. Depletion of RACK-1 also increases the levels of precursor let-7 miRNA. When Dicer is knocked down and pre-miRNA processing is inhibited, depletion of RACK-1 still leads to increased levels of pre-let-7, suggesting that RACK-1 affects a biogenesis mechanism upstream of Dicer. No changes in the activity of the let-7 promoter or the levels of primary let-7 miRNA are associated with depletion of RACK-1, suggesting that RACK-1 affects let-7 miRNA biogenesis at the post-transcriptional level. Interestingly, rack-1 knockdown also increases the levels of a few other precursor miRNAs. Our results reveal that RACK-1 controls the biogenesis of a subset of miRNAs, including let-7, and in this way plays a role in the heterochronic gene pathway during C. elegans development.

Infrared electroabsorption spectroscopy of N,N-dimethyl-p-nitroaniline in acetonitrile/C2Cl4: solvation of the solute and self-association of acetonitrile.

Solvated structures of N,N-dimethyl-p-nitroaniline (DMPNA), an analog of p-nitroaniline (PNA), and self-associated structures of acetonitrile (ACN) in mixed solvents of ACN and C(2)Cl(4) were studied using infrared (IR) electroabsorption and FTIR spectroscopies. IR electroabsorption spectroscopy measures changes in IR absorption intensity upon application of external electric field modulation, which are a sensitive probe for permanent dipole moments. In ACN/CCl(4), PNA has been shown to occur as two distinct solvated forms, namely, 1:1 and 1:2 forms, which have one and two ACN molecule(s), respectively, associated with PNA. The IR electroabsorption and FTIR measurements on DMPNA show that, unlike PNA, DMPNA occurs as a monomer in ACN/C(2)Cl(4) rather than as specific solvated structures analogous to the 1:1 and 1:2 forms because of the substitution effect. Not only does the N,N-dimethyl substitution in DMPNA hamper solvation of ACN at the N(CH(3))(2) group, but it also indirectly blocks strong interactions with ACN at the NO(2) group. Furthermore, by using the ΔA signal of DMPNA as an internal intensity standard, it was found that the dipole moment of ACN in the DMPNA/ACN/C(2)Cl(4) system is about 1.5 times larger than that of the ACN monomer in dilute CCl(4) solution. This large value of the dipole moment in the solution studied here is attributable to the formation of a head-to-tail linear dimer of ACN, whereas the antiparallel dimer is energetically more favorable in the gas phase.

Synthesis, in vitro macrophage response and detoxification of bamboo charcoal beads for purifying blood.

Bamboo charcoal beads (BCBs) were formed by coprecipitating bamboo charcoal particles with chitosan in alkaline solution. The amount of chitosan in the BCBs and their surface properties were measured. When 13-52 mg BCBs were exposed to RAW 264.7 macrophages, the amount of nitric oxide released and the cell viability were close to those of the blank. The amount of cytokine IL-6 secreted by macrophages did not depend on the dose of BCBs but macrophages secreted more TNF-alpha in response to higher doses of BCBs. However, the cytokine levels were relatively low, suggesting the favorable biocompatibility of BCBs. In adsorption experiments, BCBs adsorbed and released bovine serum albumin at particular concentrations, whereas BCBs adsorbed L-phenylalanine without a sign of release. This difference is attributed to the hydrophilicity and the pore size of the BCBs. Finally, the potential of BCBs as biocompatible adsorbents in blood detoxification is considered.

Unexpected inhibitory regulation of glutamate release from rat cerebrocortical nerve terminals by presynaptic 5-hydroxytryptamine-2A receptors.

Presynaptic 5-HT(2A) receptor modulation of glutamate release from rat cerebrocortical nerve terminals (synaptosomes) was investigated by using the 5-HT(2A/2C) receptor agonist (+/-)-1-[2,5-dimethoxy-4-iodophenyl]-2-aminopropane (DOI). DOI potently inhibited 4-aminopyridine (4AP)-evoked glutamate release. Involvement of presynaptic 5-HT(2A) receptors in this modulation of 4AP-evoked release was confirmed by blockade of the DOI-mediated inhibition by the 5-HT(2A) receptor antagonist ketanserin but not by the 5-HT(2C) receptor antagonist RS102221. Inhibition of glutamate release by DOI was associated with a reduction of 4AP-evoked depolarization and downstream elevation of cytoplasmic free calcium concentration ([Ca(2+)](C)) mediated via P/Q- and N-type voltage-dependent Ca(2+) channels (VDCCs). In contrast to the DOI effect on 4AP-evoked release, the agonist had no effect on high external [K(+)] (30 mM)-induced (KCl) stimulation of VDCCs or glutamate release. Likewise, release mediated by direct Ca(2+) entry with Ca(2+) ionophore (ionomycin) or by hypertonic sucrose was unaffected by DOI. Mechanistically, DOI modulation of 4AP-evoked glutamate release appeared to involve a phospholipase C/protein kinase C signaling cascade, insofar as pretreatment of synaptosomes with the phospholipase C inhibitor U73122 or protein kinase C inhibitors Ro320432 or GF109203X all effectively occluded the inhibitory effect of the agonist. Together, these results suggest that presynaptic 5-HT(2A) receptors present on glutamatergic terminals effect an unexpected depression of glutamate release by negatively modulating nerve terminal excitability and downstream VDCC activation through a signaling cascade involving phospholipase C/protein kinase C. These observations invoke presynaptic inhibitory 5-HT(2A) receptor function as a potential target for drugs to mitigate the effects of excessive glutamatergic transmission.