Assessing the Location of Ionic and Molecular Solutes in a Molecularly Heterogeneous and Nonionic Deep Eutectic Solvent

Copyright © 2020 American Chemical Society. Deep eutectic solvents (DES) are emerging sustainable designer solvents viewed as greener and better alternatives to ionic liquids. Nonionic DESs possess unique properties such as viscosity and hydrophobicity that make them desirable in microextraction applications such as oil-spill remediation. This work builds upon a nonionic DES, NMA-LA DES, previously designed by our group. The NMA-LA DES presents a rich nanoscopic morphology that could be used to allocate solutes of different polarities. In this work, the possibility of solvating different solutes within the nanoscopically heterogeneous molecular structure of the NMA-LA DES is investigated using ionic and molecular solutes. In particular, the localized vibrational transitions in these solutes are used as reporters of the DES molecular structure via vibrational spectroscopy. The FTIR and 2DIR data suggest that the ionic solute is confined in a polar and continuous domain formed by NMA, clearly sensing the direct effect of the change in NMA concentration. In the case of the molecular nonionic and polar solute, the data indicates that the solute resides in the interface between the polar and nonpolar domains. Finally, the results for the nonpolar and nonionic solute (W(CO)6) are unexpected and less conclusive. Contrary to its polarity, the data suggest that the W(CO)6 resides within the NMA polar domain of the DES, probably by inducing a domain restructuring in the solvent. However, the data are not conclusive enough to discard the possibility that the restructuring comprises not only the polar domain but also the interface. Overall, our results demonstrate that the NMA-LA DES has nanoscopic domains with affinity to particular molecular properties, such as polarity. Thus, the presented results have a direct implication to separation science

Prevalence and associated risk factors of ophthalmic problems of working donkeys (Equus asinus) in Mekelle, Northern Ethiopia

A cross-sectional study was conducted from December 2017 to March 2018 aimed at determining the prevalence and associated risk factors of ophthalmic problems of working donkeys in Mekelle, Northern Ethiopia. Study animals were selected by random sampling method to obtain the primary data in the form of direct physical examination and history was obtained from the owner of each examined donkey at the same time. Descriptive statistics was employed to summarize the data and uni-variant logistic regression was used to quantify the degree of association between ophthalmic problems and identified risk factors. Accordingly, out of the total 384 examined, 181 were found to be positive for ophthalmic problems with an overall prevalence of 47.14%. In this study, a statistical significant difference (P<0.05) were found among age groups and sexes. Female donkeys (63.01%) were 2.08 (OR=2.08; 95% CI: 1.105, 3.942) times more likely to have ophthalmic problems compared to male donkeys (44.97%). Donkeys with age category of >10 years (62.07%) and 6-10 years (49.6%) were 3.34 (OR=3.34; 95% CI: 2.002, 5.578) and 2.01 (OR=2.01; 95% CI: 1.225, 3.296) times more likely to have ophthalmic problems as compared to the age group of <6 years (32.87%) old, respectively. Ophthalmic problems are relatively higher in the right eye (41.44%) than the left (39.78%). Higher percentages were observed due to conjuctival defect (29.28%), followed by general lesion of eyelid (26.52%) and corneal defects (13.26%). The possible causes were diseases (62.98%) and mechanical damages (37.02%). In conclusion, the prevalence of ophthalmic problems in the study area is very high. Therefore, better management practices and awareness creation among donkey owners is highly recommended. Keywords: Mekelle; Ophthalmic problems; Prevalence; Risk Factors; Working Donkey

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

<p>Abstract</p> <p>Background</p> <p>Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information.</p> <p>Results</p> <p>The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations.</p> <p>Conclusion</p> <p>The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (<it>e.g</it>., LOOCV) and biologically (<it>e.g</it>., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.</p

Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration

Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation

The inhibition of the Human Immunodeficiency Virus type 1 activity by crude and purified human pregnancy plug mucus and mucins in an inhibition assay

Background: The female reproductive tract is amongst the main routes for Human Immunodeficiency Virus (HIV) transmission. Cervical mucus however is known to protect the female reproductive tract from bacterial invasion and fluid loss and regulates and facilitates sperm transport to the upper reproductive tract. The purpose of this study was to purify and characterize pregnancy plug mucins and determine their anti-HIV-1 activity in an HIV inhibition assay. Methods: Pregnancy plug mucins were purified by caesium chloride density-gradient ultra-centrifugation and characterized by Western blotting analysis. The anti-HIV-1 activities of the crude pregnancy plug mucus and purified pregnancy plug mucins was determined by incubating them with HIV-1 prior to infection of the human T lymphoblastoid cell line (CEM SS cells). Results: The pregnancy plug mucus had MUC1, MUC2, MUC5AC and MUC5B. The HIV inhibition assay revealed that while the purified pregnancy plug mucins inhibit HIV-1 activity by approximately 97.5%, the crude pregnancy plug mucus failed to inhibit HIV-1 activity. Conclusion: Although it is not clear why the crude sample did not inhibit HIV-1 activity, it may be that the amount of mucins in the crude pregnancy plug mucus (which contains water, mucins, lipids, nucleic acids, lactoferrin, lysozyme, immunoglobulins and ions), is insufficient to cause viral inhibition or aggregation.Peer Reviewe

An early response regulatory cluster induced by low temperature and hydrogen peroxide in seedlings of chilling-tolerant japonica rice

10.1186/1471-2164-8-175BMC Genomics817

MetaboSearch: Tool for Mass-Based Metabolite Identification Using Multiple Databases

Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB), Madison Metabolomics Consortium Database (MMCD), Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI) keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information. Availability: The software tool is available at http://omics.georgetown.edu/MetaboSearch.html

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example