Genomics, gene expression and DNA arrays

Elucidating transcriptomic profiles from single-cell RNA sequencing data using nature-inspired compressed sensing

Gene-expression profiling can define the cell state and gene-expression pattern of cells at the genetic level in a high-throughput manner. With the development of transcriptome techniques, processing high-dimensional genetic data has become a major challenge in expression profiling. Thanks to the recent widespread use of matrix decomposition methods in bioinformatics, a computational framework based on compressed sensing was adopted to reduce dimensionality. However, compressed sensing requires an optimization strategy to learn the modular dictionaries and activity levels from the low-dimensional random composite measurements to reconstruct the high-dimensional gene-expression data. Considering this, here we introduce and compare four compressed sensing frameworks coming from nature-inspired optimization algorithms (CSCS, ABCCS, BACS and FACS) to improve the quality of the decompression process. Several experiments establish that the three proposed methods outperform benchmark methods on nine different datasets, especially the FACS method. We illustrate therefore, the robustness and convergence of FACS in various aspects; notably, time complexity and parameter analyses highlight properties of our proposed FACS. Furthermore, differential gene-expression analysis, cell-type clustering, gene ontology enrichment and pathology analysis are conducted, which bring novel insights into cell-type identification and characterization mechanisms from different perspectives. All algorithms are written in Python and available at https://github.com/Philyzh8/Nature-inspired-CS.

DNA-targeted formation and catalytic reactions of DNAzymes for label-free ratiometric electrochemiluminescence biosensing

A label-free ratiometric electrochemiluminescence (ECL) sensing strategy for the sensitive detection of target DNA (T-DNA) was proposed on the basis of G-quadruplex/hemin-regulated ECL emissions of CdS quantum dots (QDs) and luminol with their common coreactant of H₂O₂. The ECL biosensor was constructed through stepwise assemblies of CdS QDs and hairpin DNA (H-DNA) on a glassy carbon electrode, and subsequent introduction of T-DNA resulted in the development of G-quadruplex/hemin DNAzymes via the specific recognition of T-DNA and H-DNA in the presence of hemin and K⁺ ions. The formed DNAzymes not only prompted the catalytic oxidation of hydroquinone followed by deposition of insoluble oxidation oligomers on the electrode surface to attenuate the cathodic ECL emission of CdS QDs but also triggered the catalytic oxidation of luminol to enhance the anodic ECL emission. The label-free ratiometric ECL biosensor for the detection of T-DNA showed a wide response range from 1 to 10,000 fM (10^-15 M) with a low detection limit of 0.2 fM and exhibited excellent selectivity against mismatched base sequences. This work provides a reliable and sensitive sensing platform for the detection of targets in analytical community by means of rational design of DNA sequences.

Application of omics technologies in dermatological research and skin management

BACKGROUND

"Omics" are usually based on the use of high-throughput analysis methods for global analysis of biological samples and the discovery of biomarkers, and may provide new insights into biological phenomena. Over the last few years, the development of omics technologies has considerably accelerated the pace of dermatological research.

AIMS

The purpose of this article was to review the development of omics in recent decades and their application in dermatological research.

METHODS

An extensive literature search was conducted on omics technologies since the first research on omics.

RESULTS

This article summarizes the history and main research methods of the six omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics. Their application in certain skin diseases and cosmetics research and development are also summarized.

CONCLUSIONS

This information will help to understand the mechanism of some skin diseases and the discovery of potential biomarkers, and provide new insights for skin health management and cosmetics research and development.

Long-term dynamic compression enhancement TGF-β3-induced chondrogenesis in bovine stem cells: a gene expression analysis

Background

Bioengineering has demonstrated the potential of utilising mesenchymal stem cells (MSCs), growth factors, and mechanical stimuli to treat cartilage defects. However, the underlying genes and pathways are largely unclear. This is the first study on screening and identifying the hub genes involved in mechanically enhanced chondrogenesis and their potential molecular mechanisms.

Methods

The datasets were downloaded from the Gene Expression Omnibus (GEO) database and contain six transforming growth factor-beta-3 (TGF-β3) induced bovine bone marrow-derived MSCs specimens and six TGF-β3/dynamic-compression-induced specimens at day 42. Screening differentially expressed genes (DEGs) was performed and then analysed via bioinformatics methods. The Database for Annotation, Visualisation, and Integrated Discovery (DAVID) online analysis was utilised to obtain the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network of the DEGs was constructed based on data from the STRING database and visualised through the Cytoscape software. The functional modules were extracted from the PPI network for further analysis.

Results

The top 10 hub genes ranked by their connection degrees were IL6, UBE2C, TOP2A, MCM4, PLK2, SMC2, BMP2, LMO7, TRIM36, and MAPK8. Multiple signalling pathways (including the PI3K-Akt signalling pathway, the toll-like receptor signalling pathway, the TNF signalling pathway, and the MAPK pathway) may impact the sensation, transduction, and reaction of external mechanical stimuli.

Conclusions

This study provides a theoretical finding showing that gene UBE2C, IL6, and MAPK8, and multiple signalling pathways may play pivotal roles in dynamic compression-enhanced chondrogenesis.

GYNOCARE Update: Modern Strategies to Improve Diagnosis and Treatment of Rare Gynecologic Tumors—Current Challenges and Future Directions

Simple Summary

More than 50% of all the tumors affecting the female genital tract can be classified as rare and usually have a poor prognosis owing to delayed diagnosis and treatment. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different countries. The European Network for Gynecological Rare Cancer Research: GYNOCARE aims to address these challenges by creating a unique network between key stakeholders covering distinct domains from basic research to cure. GYNOCARE is part of a European Collaboration in Science and Technology (COST) with the aim to focus on the development of new approaches to improve the diagnosis and treatment of rare gynecological tumors. Here, we provide a brief overview describing the goals of this COST Action and its future challenges with the aim to continue fighting against this rare cancer.

Abstract

More than 50% of all gynecologic tumors can be classified as rare (defined as an incidence of ≤6 per 100,000 women) and usually have a poor prognosis owing to delayed diagnosis and treatment. In contrast to almost all other common solid tumors, the treatment of rare gynecologic tumors (RGT) is often based on expert opinion, retrospective studies, or extrapolation from other tumor sites with similar histology, leading to difficulty in developing guidelines for clinical practice. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different European countries and indeed, worldwide. The GYNOCARE, COST Action CA18117 (European Network for Gynecological Rare Cancer Research) programme aims to address these challenges through the creation of a unique network between key stakeholders covering distinct domains from concept to cure: basic research on RGT, biobanking, bridging with industry, and setting up the legal and regulatory requirements for international innovative clinical trials. On this basis, members of this COST Action, (Working Group 1, “Basic and Translational Research on Rare Gynecological Cancer”) have decided to focus their future efforts on the development of new approaches to improve the diagnosis and treatment of RGT. Here, we provide a brief overview of the current state-of-the-art and describe the goals of this COST Action and its future challenges with the aim to stimulate discussion and promote synergy across scientists engaged in the fight against this rare cancer worldwide.

Up-Regulation of Nfat5 mRNA and Fzd4 mRNA as a Marker of Poor Outcome in Neonatal Hypoxic-Ischemic Encephalopathy

OBJECTIVE

To evaluate umbilical cord messenger RNA (mRNA) expression as biomarkers for the grade of hypoxic-ischemic encephalopathy (HIE) and long-term neurodevelopment outcome.

STUDY DESIGN

Infants were recruited from the BiHiVE1 study, Ireland (2009-2011), and the BiHiVE2 study, Ireland, and Sweden (2013-2015). Infants with HIE were assigned modified Sarnat scores at 24 hours and followed at 18-36 months. mRNA expression from cord blood was measured using quantitative real-time polymerase chain reaction.

RESULTS

We studied 124 infants (controls, n = 37; perinatal asphyxia, n = 43; and HIE, n = 44). Fzd4 mRNA increased in severe HIE (median relative quantification, 2.98; IQR, 2.23-3.68) vs mild HIE (0.88; IQR, 0.46-1.37; P = .004), and in severe HIE vs moderate HIE (1.06; IQR, 0.81-1.20; P = .003). Fzd4 mRNA also increased in infants eligible for therapeutic hypothermia (1.20; IQR, 0.92-2.37) vs those who were ineligible for therapeutic hypothermia group (0.81; IQR, 0.46-1.53; P = .017). Neurodevelopmental outcome was analyzed for 56 infants. Nfat5 mRNA increased in infants with severely abnormal (1.26; IQR, 1.17-1.39) vs normal outcomes (0.97; IQR, 0.83-1.24; P = .036), and also in infants with severely abnormal vs mildly abnormal outcomes (0.96; IQR, 0.80-1.06; P = .013). Fzd4 mRNA increased in infants with severely abnormal (2.51; IQR, 1.60-3.56) vs normal outcomes (0.74; IQR, 0.48-1.49; P = .004) and in infants with severely abnormal vs mildly abnormal outcomes (0.97; IQR, 0.75-1.34; P = .026).

CONCLUSIONS

Increased Fzd4 mRNA expression was observed in cord blood of infants with severe HIE; Nfat5 mRNA and Fzd4 mRNA expression were increased in infants with severely abnormal long-term outcomes. These mRNA may augment current measures as early objective markers of HIE severity at delivery.

22q11.2 deletion syndrome and schizophrenia

22q11.2 deletion is a common microdeletion that causes an array of developmental defects including 22q11.2 deletion syndrome (22q11DS) or DiGeorge syndrome and velocardiofacial syndrome. About 30% of patients with 22q11.2 deletion develop schizophrenia. Mice with deletion of the ortholog region in mouse chromosome 16qA13 exhibit schizophrenia-like abnormal behaviors. It is suggested that the genes deleted in 22q11DS are involved in the pathogenesis of schizophrenia. Among these genes, COMT, ZDHHC8, DGCR8, and PRODH have been identified as schizophrenia susceptibility genes. And DGCR2 is also found to be associated with schizophrenia. In this review, we focused on these five genes and reviewed their functions in the brain and the potential pathophysiological mechanisms in schizophrenia, which will give us a deeper understanding of the pathology of schizophrenia.

The Non-Coding RNA GAS5 and Its Role in Tumor Therapy-Induced Resistance

The growth arrest-specific transcript 5 (GAS5) is a >200-nt lncRNA molecule that regulates several cellular functions, including proliferation, apoptosis, invasion and metastasis, across different types of human cancers. Here, we reviewed the current literature on the expression of GAS5 in leukemia, cervical, breast, ovarian, prostate, urinary bladder, lung, gastric, colorectal, liver, osteosarcoma and brain cancers, as well as its interaction with various miRNAs and its effect on therapy-related resistance in these malignancies. The general consensus is that GAS5 acts as a tumor suppressor across different tumor types and that its up-regulation results in tumor sensitization to chemotherapy or radiotherapy. GAS5 seems to play a previously unappreciated, but significant role in tumor therapy-induced resistance.

Application of the common base method to regression and analysis of covariance (ANCOVA) in qPCR experiments and subsequent relative expression calculation

Background

Quantitative polymerase chain reaction (qPCR) is the technique of choice for quantifying gene expression. While the technique itself is well established, approaches for the analysis of qPCR data continue to improve.

Results

Here we expand on the common base method to develop procedures for testing linear relationships between gene expression and either a measured dependent variable, independent variable, or expression of another gene. We further develop functions relating variables to a relative expression value and develop calculations for determination of associated confidence intervals.

Conclusions

Traditional qPCR analysis methods typically rely on paired designs. The common base method does not require such pairing of samples. It is therefore applicable to other designs within the general linear model such as linear regression and analysis of covariance. The methodology presented here is also simple enough to be performed using basic spreadsheet software.

MasterPATH: network analysis of functional genomics screening data

Background

Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks.

Results

Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition.

Conclusion

The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

Transcriptome Analysis and Expression of Selected Cationic Amino Acid Transporters in the Liver of Broiler Chicken Fed Diets with Varying Concentrations of Lysine

Amino acids are known to play a key role in gene expression regulation. Amino acid signaling is mediated via two pathways: the mammalian target of rapamycin complex 1 (mTORC1) and the amino acid responsive (AAR) pathways. Cationic amino acid transporters (CATs) are crucial in these pathways due to their sensing, signaling and transport functions. The availability of certain amino acids plays a key role in the intake of other amino acids, hence affecting growth in young birds. However, the specific mechanism for regulating lysine transport for growth is not clear. In this study, we analyze the transcriptome profiles and mRNA expression of selected cationic amino acid transporters in the livers of broilers fed low and high lysine diets. Birds consumed high-lysine (1.42% lysine) or low-lysine (0.85% lysine) diets while the control group consumed 1.14% lysine diet. These concentrations of lysine represent 125% (high lysine), 75% (low lysine) and 100% (control), respectively, of the National Research Council's (NRC) recommendation for broiler chickens. After comparing the two groups, 210 differentially expressed genes (DEGs) were identified (fold change >1 and false discovery rate (FDR) <0.05). When comparing the high lysine and the low lysine treatments, there were 67 upregulated genes and 143 downregulated genes among these DEGs. Analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Gene Ontology (GO) enrichment analysis show that cellular growth, lipid metabolism and lysine metabolism pathways were among the significantly enriched pathways. This study contributes to a better understanding of the potential molecular mechanisms underlying the correlation between lysine intake, body weight gain (BWG) and feed intake (FI) in broiler chickens. Moreover, the DEGs obtained in this study may be used as potential candidate genes for further investigation of broiler growth customized responses to individualized nutrients such as amino acids.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

Transcriptome Analysis of Testes and Uterus: Reproductive Dysfunction Induced by Toxoplasma gondii in Mice

Toxoplasma gondii (T. gondii) infection in female mammals during pregnancy can result in poor pregnancy. Similarly, it can result in male reproductive disorders in male mammals. Although the testes and uterus have very different biological makeup, they are still both attacked by T. gondii resulting in reproductive dysfunctions. We hypothesized that there are significant common genes in the testes and uterus that interact with T. gondii. Finding out and studying these genes is vital to understand the infection mechanism of T. gondii and the induced disease pathogenesis. To achieve this goal, we built a mice model of acute infection with T. gondii and the testes and uterus of the mice were sequenced by RNA-Seq. A total of 291 and 679 significantly differently expressed genes (DEGs) were obtained from the testes and the uterus, respectively. In the Gene Ontology (GO) analysis, part of the DEGs in the testes and uterus were related to 35 GO functions. When compared with the KEGG database, seven pathways affecting both the testes and uterus during the course of T. gondii infection were identified. In addition, Toxoplasmosis can significantly affect the expression of Nlrp5 and Insc leading to negative outcomes in the host. On the other hand, the host regulates Gbp7, Gbp2b, and Ifit3 to defend against T. gondii infection.

DNA Tetrahedra Modules as Versatile Optical Sensing Platforms for Multiplexed Analysis of miRNAs, Endonucleases, and Aptamer-Ligand Complexes

The sensing modules for analyzing miRNAs or the endonucleases consist of tetrahedra functionalized with three different fluorophore-quencher pairs in spatially quenched configurations and hairpin units acting as recognition elements for the analytes. Three different miRNAs (miRNA-21, miRNA-221, and miRNA-155) or three different endonucleases (Nt.BbvCI, EcoRI, and HindIII) uncage the respective hairpins, leading to the switched-on fluorescence of the respective fluorophores and to the multiplex detection of the respective analytes. In addition, a tetrahedron module for the multiplexed analysis of aptamer ligand complexes (ligands = ATP, thrombin, VEGF) is introduced. The module includes edges modified with three spatially separated fluorophore-quencher pairs that were stretched by the respective aptamer strands to yield a switched-on fluorescent state. Formation of the respective aptamer ligands reconfigures the edges into fluorophore-quenched caged-hairpin structures that enable the multiplexed analysis of the aptamer-ligand complexes. The facile permeation of the tetrahedra structures into cells is used for the imaging of MCF-7 and HepG2 cancer cells and their discrimination from normal epithelial MCF-10A breast cells.

Nucleic Acid Quantitation with Log-Linear Response Hybridization Probe Sets

Concentrations of different nucleic acid species in biological samples span many orders of magnitude. A real-time polymerase chain reaction maps the concentration of a target nucleic acid sequence log-linearly into cycle threshold to enable quantitation with a wide dynamic range but suffers from enzymatic biases. Here, we present a general design for constructing hybridization probe sets with highly log-linear response curves to enable accurate enzyme-free quantitation across large ranges (more than 6 logs) of target DNA concentrations. The sensitivity of each component probe is accurately adjusted via formulation stoichiometry to reduce the standard error of target quantitation down to 7%. As a proof of concept, we show multiplexed quantitation of three microRNA species in total RNA of the human brain and liver.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

A novel liquid biopsy-based approach for highly specific cancer diagnostics: mitigating false responses in assaying patient plasma-derived circulating microRNAs through combined SERS and plasmon-enhanced fluorescence analyses

Studies have shown that microRNAs, which are small noncoding RNAs, hold tremendous promise as next-generation circulating biomarkers for early cancer detection via liquid biopsies. A novel, solid-state nanoplasmonic sensor capable of assaying circulating microRNAs through a combined surface-enhanced Raman scattering (SERS) and plasmon-enhanced fluorescence (PEF) approach has been developed. Here, the unique localized surface plasmon resonance properties of chemically-synthesized gold triangular nanoprisms (Au TNPs) are utilized to create large SERS and PEF enhancements. With careful modification to the surface of Au TNPs, this sensing approach is capable of quantifying circulating microRNAs at femtogram/microliter concentrations. Uniquely, the multimodal analytical methods mitigate both false positive and false negative responses and demonstrate the high stability of our sensors within bodily fluids. As a proof of concept, microRNA-10b and microRNA-96 were directly assayed from the plasma of six bladder cancer patients. Results show potential for a highly specific liquid biopsy method that could be used in point-of-care clinical diagnostics to increase early cancer detection or any other diseases including SARS-CoV-2 in which RNAs can be used as biomarkers.

Predicting future dynamics from short-term time series using an Anticipated Learning Machine

Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.

Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels

Purpose

We aimed to establish radiotranscriptomics signatures based on serum miRNA levels and computed tomography (CT) texture features and develop nomogram models for predicting radiotherapy response in patients with nonsmall cell lung cancer (NSCLC).

Methods

We first used established radioresistant NSCLC cell lines for miRNA selection. At the same time, patients (103 for training set and 71 for validation set) with NSCLC were enrolled. Their pretreatment contrast‐enhanced CT texture features were extracted and their serum miRNA levels were obtained. Then, radiotranscriptomics feature selection was implemented with the least absolute shrinkage and selection operator (LASSO), and signatures were generated by logistic or Cox regression for objective response rate (ORR), overall survival (OS), and progression‐free survival (PFS). Afterward, radiotranscriptomics signature‐based nomograms were constructed and assessed for clinical use.

Results

Four miRNAs and 22 reproducible contrast‐enhanced CT features were used for radiotranscriptomics feature selection and we generated ORR‐, OS‐, and PFS‐ related radiotranscriptomics signatures. In patients with NSCLC who received radiotherapy, the radiotranscriptomics signatures were independently associated with ORR, OS, and PFS in both the training (OR: 2.94, P < .001; HR: 2.90, P < .001; HR: 3.58, P = .001) and validation set (OR: 2.94, P = .026; HR: 2.14, P = .004; HR: 2.64, P = .016). We also obtained a satisfactory nomogram for ORR. The C‐index values for the ORR nomogram were 0.86 [95% confidence interval (CI), 0.75 to 0.92] in the training set and 0.81 (95% CI, 0.69 to 0.89) in the validation set. The calibration‐in‐the‐large and calibration slope performed well. Decision curve analysis indicated a satisfactory net benefit.

Conclusions

The radiotranscriptomics signature could be an independent biomarker for evaluating radiotherapeutic responses in patients with NSCLC. The radiotranscriptomics signature‐based nomogram could be used to predict patients’ ORR, which would represent progress in individualized medicine.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Elemental characterisation of the pyramidal neuron layer within the rat and mouse hippocampus

A unique combination of sensitivity, resolution, and penetration make X-ray fluorescence imaging (XFI) ideally suited to investigate trace elemental distributions in the biological context. XFI has gained widespread use as an analytical technique in the biological sciences, and in particular enables exciting new avenues of research in the field of neuroscience. In this study, elemental mapping by XFI was applied to characterise the elemental content within neuronal cell layers of hippocampal sub-regions of mice and rats. Although classical histochemical methods for metal detection exist, such approaches are typically limited to qualitative analysis. Specifically, histochemical methods are not uniformly sensitive to all chemical forms of a metal, often displaying variable sensitivity to specific "pools" or chemical forms of a metal. In addition, histochemical methods require fixation and extensive chemical treatment of samples, creating the strong likelihood for metal redistribution, leaching, or contamination. Direct quantitative elemental mapping of total elemental pools, in situ within ex vivo tissue sections, without the need for chemical fixation or addition of staining reagents is not possible with traditional histochemical methods; however, such a capability, which is provided by XFI, can offer an enormous analytical advantage. The results we report herein demonstrate the analytical advantage of XFI elemental mapping for direct, label-free metal quantification, in situ within ex vivo brain tissue sections. Specifically, we definitively characterise for the first time, the abundance of Fe within the pyramidal cell layers of the hippocampus. Localisation of Fe to this cell layer is not reproducibly achieved with classical Perls histochemical Fe stains. The ability of XFI to directly quantify neuronal elemental (P, S, Cl, K, Ca, Fe, Cu, Zn) distributions, revealed unique profiles of Fe and Zn within anatomical sub-regions of the hippocampus i.e., cornu ammonis 1, 2 or 3 (CA1, CA2 or CA3) sub-regions. Interestingly, our study reveals a unique Fe gradient across neuron populations within the non-degenerating and pathology free rat hippocampus, which curiously mirrors the pattern of region-specific vulnerability of the hippocampus that has previously been established to occur in various neurodegenerative diseases.

Propargylamine-selective dual fluorescence turn-on method for post-synthetic labeling of DNA

We have developed a propargylamine-selective dual fluorescence turn-on system, using ylidenemalononitrile enamines, for post-synthetic DNA labeling, allowing the direct monitoring of DNA using dual emission in living cells.

Emerging Technologies for Tissue Engineering: From Gene Editing to Personalized Medicine

IMPACT STATEMENT

History has shown us how tissue engineering can be advanced by embracing technological innovation. In this perspective, we highlight some of the most promising emerging technologies and discuss how they can be integrated into existing tissue engineering protocols. The proposed technologies offer the opportunity to reshape how we currently design, engineer, and characterize tissue grafts for improved in vivo regeneration.

Effect of chronic intermittent hypoxia on gene expression profiles of rat liver: a better understanding of OSA-related liver disease

PURPOSE

Obstructive sleep apnea (OSA) and OSA-associated chronic intermittent hypoxia (CIH) have been suggested to be associated with increased risk of liver disease. Little is known about the biological pathophysiology and underlying molecular mechanisms. Here we use whole-genome expression profiling to explore the transcriptomic changes induced by CIH in rat liver.

METHODS

Rats (n = 3) were exposed to CIH for 8 weeks and were compared with rats exposed to normoxia (n = 3). Illumina HiSeq 4000 platform was used to examine differentially expressed genes (DEGs) in the liver between control group and CIH rat model. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to validate DEGs. Biological functions of DEGs were determined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.

RESULTS

Compared with control group, 318 genes were identified to be dysregulated in the liver of CIH rat model, with 211genes upregulated and 107 genes downregulated. Bioinformatics analysis showed that these genes were extensively related to various physiologic processes such as hepatic metabolism, apoptotic process, and oxidative stress. 10 genes with 5 upregulated and 5 downregulated were selected and further verified by qRT-PCR.

CONCLUSIONS

CIH resulted in altered gene expression patterns in the liver of rat. The DEGs were related to various physiological and pathological processes in CIH rat liver. These data provide a better understanding of the mechanisms and underlying molecular changes of OSA-related liver disease.

Transcriptome and DNA Methylation Analyses of the Molecular Mechanisms Underlying with Longissimus dorsi Muscles at Different Stages of Development in the Polled Yak

DNA methylation modifications are implicated in many biological processes. As the most common epigenetic mechanism DNA methylation also affects muscle growth and development. The majority of previous studies have focused on different varieties of yak, but little is known about the epigenetic regulation mechanisms in different age groups of animals. The development of muscles in the different stages of yak growth remains unclear. In this study, we selected the longissimus dorsi muscle tissue at three different growth stages of the yak, namely, 90-day-old fetuses (group E), six months old (group M), and three years old (group A). Using RNA-Seq transcriptome sequencing and methyl-RAD whole-genome methylation sequencing technology, changes in gene expression levels and DNA methylation status throughout the genome were investigated during the stages of yak development. Each group was represented by three biological replicates. The intersections of expression patterns of 7694 differentially expressed genes (DEGs) were identified (padj < 0.01, |log2FC| > 1.2) at each of the three developmental periods. Time-series expression profile clustering analysis indicated that the DEGs were significantly arranged into eight clusters which could be divided into two classes (padj < 0.05), class I profiles that were downregulated and class II profiles that were upregulated. Based on this cluster analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that DEGs from class I profiles were significantly (padj < 0.05) enriched in 21 pathways, the most enriched pathway being the Axon guidance signaling pathway. DEGs from the class II profile were significantly enriched in 58 pathways, the pathway most strongly enriched being Metabolic pathway. After establishing the methylation profiles of the whole genomes, and using two groups of comparisons, the three combinations of groups (M-vs.-E, M-vs.-A, A-vs.-E) were found to have 1344, 822, and 420 genes, respectively, that were differentially methylated at CCGG sites and 2282, 3056, and 537 genes, respectively, at CCWGG sites. The two sets of data were integrated and the negative correlations between DEGs and differentially methylated promoters (DMPs) analyzed, which confirmed that TMEM8C, IGF2, CACNA1S and MUSTN1 were methylated in the promoter region and that expression of the modified genes was negatively correlated. Interestingly, these four genes, from what was mentioned above, perform vital roles in yak muscle growth and represent a reference for future genomic and epigenomic studies in muscle development, in addition to enabling marker-assisted selection of growth traits.

HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

Transcriptomic analysis of reproductive damage in the epididymis of male Kunming mice induced by chronic infection of Toxoplasma gondii PRU strain

BACKGROUND

Some researchers have reported that Toxoplasma gondii can cause serious reproductive impairment in male animals. Specifically, T. gondii destroy the quality of sperm in the epididymis, which affects their sexual ability. However, among such studies, none have investigated the male reproductive transcriptome. Therefore, to investigate the relationship between T. gondii and sperm maturation, we infected mice with T. gondii prugniaud (PRU) strain and performed transcriptome sequencing of the epididymis.

RESULTS

Compared with the control group, 431 upregulated and 229 downregulated differentially expressed genes (DEGs) were found (P-value < 0.05, false discovery rate (FDR) < 0.05 and |log2 (fold change)| ≥ 1). According to results of a bioinformatics analysis, Gene Ontology (GO) function is divided into three categories: cellular component, molecular function and biological process. Upon performing GO analysis, we found that some DEGs correlated with an integral part of membrane, protein complex, cell surface, ATP binding, immune system process, signal transduction and metabolic process which are responsible for the epididymal injury. DEGs were mapped to 101 unique KEGG pathways. Pathways such as cytokine-cytokine receptor interaction, glycolysis/gluconeogenesis and apoptosis are closely related to sperm quality. Moreover, Tnfsf10 and spata18 can damage the mitochondria in sperm, which decreases sperm motility and morphology.

CONCLUSIONS

We sequenced the reproductive system of male mice chronically infected with T. gondii, which provides a new direction for research into male sterility caused by Toxoplasma infection. This work provides valuable information and a comprehensive database for future studies of the interaction between T. gondii infection and the male reproductive system.

Challenges and opportunity of recent genome editing and multi-omics in cyanobacteria and microalgae for biorefinery

Microalgae and cyanobacteria are easy to culture, with higher growth rates and photosynthetic efficiencies compared to terrestrial plants, and thus generating higher productivity. The concept of microalgal biorefinery is to assimilate carbon dioxide and convert it to chemical energy/value-added products, such as vitamins, carotenoids, fatty acids, proteins and nucleic acids, to be applied in bioenergy, health foods, aquaculture feed, pharmaceutical and medical fields. Therefore, microalgae are annotated as the third generation feedstock in bioenergy and biorefinery. In past decades, many studies thrived to improve the carbon sequestration efficiency as well as enhance value-added compounds from different algae, especially via genetic engineering, synthetic biology, metabolic design and regulation. From the traditional Agrobacterium-mediated transformation DNA to novel CRISPR (clustered regularly interspaced short palindromic repeats) technology applied in microalgae and cyanobacteria, this review has highlighted the genome editing technology for biorefinery that is a highly environmental friendly trend to sustainable and renewable development.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

The influence of geometry and other fundamental challenges for bio-sensing with field effect transistors

We present a review of field effect transistors (FET) from the point of view of their applications to label-free sensing in the era of genomics and proteomics. Here, rather than a collection of Bio-FET achievements, we propose an analysis of the different issues hampering the use of these devices into clinical applications. We make a particular emphasis on the influence of the sensor geometry in the phenomena of mass transport of analytes, which is a topic that has been traditionally overlooked in the analysis and design of biosensors, but that plays a central role in the achievement of low limits of detection. Other issues like the screening of charges by the ions in liquids with physiological ionic strength and the non-specific binding are also reviewed. In conclusion, we give an overview of different solutions that have been proposed to address all these challenges, demonstrating the potential of field effect transistors owing to their ease of integration with other semiconductor components for developing cost-effective, highly multiplexed sensors for next-generation medicines.

Comparison of library construction kits for mRNA sequencing in the Illumina platform

BACKGROUND

The emergence of next-generation sequencing (NGS) technologies has made a tremendous contribution to the deciphering and significance of transcriptome analysis in biological fields. Since the advent of NGS technology in 2007, Illumina, Inc. has provided one of the most widely used sequencing platforms for NGS analysis.

OBJECTIVE

Although reagents and protocols provided by Illumina are adequately performed in transcriptome sequencing, recently, alternative reagents and protocols which are relatively cost effective are accessible. However, the kits derived from various manufacturers have advantages and disadvantages when researchers carry out the transcriptome library construction.

METHODS

We compared them using a variety of protocols to produce Illumina-compatible libraries based on transcriptome. Three different mRNA sequencing kits were selected for this study: TruSeq^® RNA Sample Preparation V2 (Illumina, Inc., USA), Universal Plus mRNA-Seq (NuGEN, Ltd., UK), and NEBNext^® Ultra™ Directional RNA Library Prep Kit for Illumina^® (New England BioLabs, Ltd., USA). We compared them focusing on cost, experimental time, and data output.

RESULTS

The quality and quantity of sequencing data obtained through the NGS technique were strongly influenced by the type of the sequencing library kits. It suggests that for transcriptome studies, researchers should select a suitable library construction kit according to the goal and resources of experiments.

CONCLUSION

The present work will help researchers to choose the right sequencing library construction kit for transcriptome analyses.

Metabolomics for a Millenniums-Old Crop: Tea Plant ( Camellia sinensis)

Tea cultivation and utilization dates back to antiquity. Today it is the most widely consumed beverage on earth due to its pleasant taste and several beneficial health properties attributed to specific metabolites. Metabolomics has a tremendous potential to correlate tea metabolites with taste and health properties in humans. Our review on the current application of metabolomics in the science of tea suggests that metabolomics is a promising frontier in the evaluation of tea quality, identification of functional genes responsible for key metabolites, investigation of their metabolic regulation, and pathway analysis in the tea plant. Furthermore, the challenges, possible solutions, and the prospects of metabolomics in tea science are reviewed.

Time-course data prediction for repeatedly measured gene expression

Variability in time course gene expression data is a natural phenomenon. The intention of this work is to predict the future time point data through observed sample data point. The Bayesian inference is carried to serve the objective. A total of 6 replicates 3 time point’s data of 218 genes expression is adopted to illustrate the method. The estimates are found consistent with HPD interval to predict the future time point gene expression value. This proposed method can be adopted in other gene expression data setup to predict the future time course data.

Miniaturized and Automated Synthesis of Biomolecules-Overview and Perspectives

Chemical synthesis is performed by reacting different chemical building blocks with defined stoichiometry, while meeting additional conditions, such as temperature and reaction time. Such a procedure is especially suited for automation and miniaturization. Life sciences lead the way to synthesizing millions of different oligonucleotides in extremely miniaturized reaction sites, e.g., pinpointing active genes in whole genomes, while chemistry advances different types of automation. Recent progress in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging could match miniaturized chemical synthesis with a powerful analytical tool to validate the outcome of many different synthesis pathways beyond applications in the life sciences. Thereby, due to the radical miniaturization of chemical synthesis, thousands of molecules can be synthesized. This in turn should allow ambitious research, e.g., finding novel synthesis routes or directly screening for photocatalysts. Herein, different technologies are discussed that might be involved in this endeavor. A special emphasis is given to the obstacles that need to be tackled when depositing tiny amounts of materials to many different extremely miniaturized reaction sites.

Effects of temperature changes in the transcriptional profile of the emerging fish pathogen Francisella noatunensis subsp. orientalis

One of the major challenges in Nile tilapia (Oreochromis niloticus L.) farming is the occurrence of bacterial infections, and the Francisella noatunensis subsp. orientalis (FNO) is an important pathogen that has emerged in last decades. Francisellosis outbreaks have been reported in the literature as occurring seasonally when water temperature is below 24 °C. The aim of this study was to quantify the median lethal doses (LD₅₀) of FNO in experimental challenges at 28 °C and 22 °C, and to investigate the impact of temperature changes in whole genome expression using microarray technology. The LD₅₀ for Nile tilapia at 28 °C was ∼10^5.7, whereas at 22 °C, the LD₅₀ was ∼10^2.2, showing that the decrease in temperature enhanced disease outcome. Out of 1917 genes screened, a total of 31 and 19 genes were down- and up-regulated at 22 °C, respectively. These genes were grouped by orthology into functional categories of: amino acid, inorganic ion, and carbohydrate transport and metabolism; transcription; and posttranslational modification, protein turnover, and chaperones. Expression of genes related to metabolism, oxidative stress, and thermal shock were regulated by temperature changes, reflecting an ability of FNO to adapt to the environment. Expression of virulence genes usually required for the Francisella genus was not changed between tested temperatures, including that of genes located on the Francisella Pathogenicity Island.

Direct label-free imaging of brain tissue using synchrotron light: a review of new spectroscopic tools for the modern neuroscientist

The incidence of brain disease and brain disorders is increasing on a global scale. Unfortunately, development of new therapeutic strategies has not increased at the same rate, and brain diseases and brain disorders now inflict substantial health and economic impacts. A greater understanding of the fundamental neurochemistry that underlies healthy brain function, and the chemical pathways that manifest in brain damage or malfunction, are required to enable and accelerate therapeutic development. A previous limitation to the study of brain function and malfunction has been the limited number of techniques that provide both a wealth of biochemical information, and spatially resolved information (i.e., there was a previous lack of techniques that provided direct biochemical or elemental imaging at the cellular level). In recent times, a suite of direct spectroscopic imaging techniques, such as Fourier transform infrared spectroscopy (FTIR), X-ray fluorescence microscopy (XFM), and X-ray absorption spectroscopy (XAS) have been adapted, optimized and integrated into the field of neuroscience, to fill the above mentioned capability-gap. Advancements at synchrotron light sources, such as improved light intensity/flux, increased detector sensitivities and new capabilities of imaging/optics, has pushed the above suite of techniques beyond "proof-of-concept" studies, to routine application to study complex research problems in the field of neuroscience (and other scientific disciplines). This review examines several of the major advancements that have occurred over the last several years, with respect to FTIR, XFM and XAS capabilities at synchrotron facilities, and how the increases in technical capabilities have being integrated and used in the field of neuroscience.

Real-Time Monitoring of Hierarchical Self-Assembly and Induction of Circularly Polarized Luminescence from Achiral Luminogens

Constructing artificial helical structures through hierarchical self-assembly and exploring the underlying mechanism are important, and they help gain insight from the structures, processes, and functions from the biological helices and facilitate the development of material science and nanotechnology. Herein, the two enantiomers of chiral Au(I) complexes ( S)-1 and ( R)-1 were synthesized, and they exhibited impressive spontaneous hierarchical self-assembly transitions from vesicles to helical fibers. An impressive chirality inversion and amplification was accompanied by the assembly transition, as elucidated by the results of in situ and time-dependent circular dichroism spectroscopy and scanning electron microscope imaging. The two enantiomers could serve as ideal chiral templates to co-assemble with other achiral luminogens to efficiently induce the resulting co-assembly systems to show circularly polarized luminescence (CPL). Our work has provided a simple but efficient way to explore the sophisticated self-assembly process and presented a facile and effective strategy to fabricate architectures with CPL properties.

Bridging genomics and phenomics of gastric carcinoma

Genetic alterations are the starting point leading to numerous changes in clinical and pathologic features (phenotypes) of individual cancers; however, their inter-relationships in gastric cancers (GC) are unclear. We performed massive parallel sequencing of 381 cancer-related genes and compared the results with clinical and pathologic findings in 330 GC. High tumor mutation burden (TMB) accounted for 11% of GC (n = 37) and all 19 MSI-H GCs were high TMB. High TMB was significantly more frequent in intestinal-type by Lauren, tumor with higher host cellular immune response, earlier AJCC stage and favorable prognosis. The most significantly mutated genes were TP53 (54%), ARID1A (23%), CDH1 (22%), PIK3CA (12%), RNF43 (10%) and KRAS (9%). For receptor tyrosine kinases, amplifications detected by immunohistochemistry were higher than sequencing (HER2, 9.1% vs. 5.8%; EGFR, 11.2% vs. 6.1%; FGFR2, 4.6% vs. 3.9%, c-MET, 3.4% vs. 0.9%). PTEN protein loss (22%) correlated well with underlying PTEN alterations while ATM loss (27%) was not significantly correlated with genetic alterations of ATM. p53 protein expression predicted alterations of TP53 with high sensitivity (97.8%) and low (15.9%) specificity. The poorly cohesive histology/CDH1-mutant GC subgroup showed the worst survival (p < 0.001). PD-L1 expression was significantly associated with MSI-H, MLH1 loss, ATM loss, MET positivity, higher host immune response, and genetic alterations of ARID1A, BRD3, PIK3CA, KRAS, MAP3K13, CDH2, PTEN and ESR1. The merged clinical, pathology and genomics of GC provide a better understanding of GC and new insights into the treatment of GC.

An Efficient Synthesis of 5-Aminopropargyl-Pyrimidine-5'-O-Triphosphates Through Palladium-Catalyzed Sonogashira Coupling

The utilization of 5-aminopropargyl nucleotide serves as a versatile molecular biology tool for the introduction of functional groups into a nucleic acid target of interest by using in-vitro enzymatic incorporation method. This article describes a simple, reliable, general, and efficient two-step chemical method for the synthesis of 5-(3-aminopropargyl)-2'-deoxycytidine-5'-O-triphosphate, 5-(3-aminopropargyl)-cytidine-5'-O-triphosphate, 5-(3-aminopropargyl)-2'-deoxyuridine-5'-O-triphosphate, and 5-(3-aminopropargyl)-uridine-5'-O-triphosphate, starting from the corresponding pyrimidine triphosphate. The first step involves regioselective C-5 iodination of pyrimidine triphosphate using N-iodosuccinimide and sodium azide. In the second step, propargylamine is coupled to the iodo-pyrimidine using the palladium-catalyzed Sonogashira reaction, producing good yields of highly pure (>99.5% HPLC) 5-aminopropargyl-pyrimidine-5'-O-triphosphate. In this approach, the palladium-catalyzed Sonogashira coupling reaction is highly chemoselective and does not involve protection and deprotection. © 2019 by John Wiley & Sons, Inc.

p300/CBP-associated factor promotes autophagic degradation of δ-catenin through acetylation and decreases prostate cancer tumorigenicity

δ-Catenin shares common binding partners with β-catenin. As acetylation and deacetylation regulate β-catenin stability, we searched for histone acetyltransferases (HATs) or histone deacetylases (HDACs) affecting δ-catenin acetylation status and protein levels. We showed that p300/CBP-associated factor (PCAF) directly bound to and acetylated δ-catenin, whereas several class I and class II HDACs reversed this effect. Unlike β-catenin, δ-catenin was downregulated by PCAF-mediated acetylation and upregulated by HDAC-mediated deacetylation. The HDAC inhibitor trichostatin A attenuated HDAC1-mediated δ-catenin upregulation, whereas HAT or autophagy inhibitors, but not proteasome inhibitors, abolished PCAF-mediated δ-catenin downregulation. The results suggested that PCAF-mediated δ-catenin acetylation promotes its autophagic degradation in an Atg5/LC3-dependent manner. Deletions or point mutations identified several lysine residues in different δ-catenin domains involved in PCAF-mediated δ-catenin downregulation. PCAF overexpression in prostate cancer cells markedly reduced δ-catenin levels and suppressed cell growth and motility. PCAF-mediated δ-catenin downregulation inhibited E-cadherin processing and decreased the nuclear distribution of β-catenin, resulting in the suppression of β-catenin/LEF-1-mediated downstream effectors. These data demonstrate that PCAF downregulates δ-catenin by promoting its autophagic degradation and suppresses δ-catenin-mediated oncogenic signals.

Laboratory Methods in Molecular Epidemiology: Bacterial Infections

In infectious disease epidemiology, the laboratory plays a critical role in diagnosis, outbreak investigations, surveillance, and characterizing biologic properties of microbes associated with their transmissibility, resistance to anti-infectives, and pathogenesis. The laboratory can inform and refine epidemiologic study design and data analyses. In public health, the laboratory functions to assess effect of an intervention. In addition to research laboratories, the new-generation molecular microbiology technology has been adapted into clinical and public health laboratories to simplify, accelerate, and make precise detection and identification of infectious disease pathogens. This technology is also being applied to subtype microbes to conduct investigations that advance our knowledge of epidemiology of old and emerging infectious diseases. Because of the recent explosive progress in molecular microbiology technology and the vast amount of data generated from the applications of this technology, this Microbiology Spectrum Curated Collection: Advances in Molecular Epidemiology of Infectious Diseases describes these methods separately for bacteria, viruses, and parasites. This review discusses past and current advancements made in laboratory methods used to conduct epidemiologic studies of bacterial infections. It describes methods used to subtype bacterial organisms based on molecular microbiology techniques, following a discussion on what is meant by bacterial "species" and "clones." Discussions on past and new genotyping tests applied to epidemiologic investigations focus on tests that compare electrophoretic band patterns, hybridization matrices, and nucleic acid sequences. Applications of these genotyping tests to address epidemiologic issues are detailed elsewhere in other reviews of this series. *This article is part of a curated collection.

Nutritional Culturomics and Big Data: Macroscopic Patterns of Change in Food, Nutrition and Diet Choices

BACKGROUND & OBJECTIVE

Nutritional culturomics (NCs) is a specific focus area of culturomics epistemology developing digital humanities and computational linguistics approaches to search for macro-patterns of public interest in food, nutrition and diet choice as a major component of cultural evolution. Cultural evolution is considered as a driver at the interface of environmental and food science, economy and policy.

METHODS

The paper presents an epistemic programme that builds on the use of big data from webbased services such as Google Trends, Google Adwords or Google Books Ngram Viewer.

RESULTS

A comparison of clearly defined NCs in terms of geography, culture, linguistics, literacy, technological setups or time period might be used to reveal variations and singularities in public's behavior in terms of adaptation and mitigation policies in the agri-food and public health sectors.

CONCLUSION

The proposed NC programme is developed along major axes: (1) the definition of an NC; (2) the reconstruction of food and diet histories; (3) the nutrition related epidemiology; (4) the understanding of variability of NCs; (5) the methodological diversification of NCs; (6) the quantifiable limitations and flaws of NCs. A series of indicative examples are presented regarding these NC epistemology components.

Discovery of potential transcriptional biomarkers in broiler chicken for detection of amantadine abuse based on RNA sequencing technology

The aim of this study was to identify candidate transcriptional biomarkers so as to provide a new method for monitoring amantadine residues during the feeding of broiler chicken. RNA sequencing (RNA-seq) and bioinformatic analyses were conducted to examine the transcriptomic changes and screen differentially expressed genes (DEGs) in broiler chicken breast muscle and liver tissues treated with amantadine. The results indicated that a total of 170 DEGs were screened from broiler chicken breast muscle tissues after amantadine was fed. Among the genes, 120 were up-regulated and 50 were down-regulated. The gene ontology (GO) terms for these genes mainly existed in the areas of hydrolase activity, immune reaction and chemokine activity. The significantly enriched pathways in the Kyoto Encyclopedia for Genes and Genomes (KEGG) were in phagosomes, cell adhesion molecules (CAMs), lysosomes and extracellular matrix (ECM) receptors. From the broiler chicken liver tissues, 172 DEGs were screened, among which 116 were up-regulated and 56 were down-regulated. The GO terms of these DEGs were related to functions such as catalytic activities, metabolic activities, oxidation-reduction activities, immune reactions and cofactor binding. The significantly enriched KEGG pathways existed in metabolism, CAM, ECM receptor reaction and drug metabolism-cytochrome P450. According to the fold-change (FC), significance levels, functional annotations and possible biological processes of DEGs, 11 and 9 candidate DEGs related to amantadine treatment were further screened from broiler chicken breast muscle and liver tissues, respectively. In addition, the quantitative real-time polymerase chain reaction (qRT-PCR) verification showed exactly concordant results with the RNA-seq data. Principal components analysis (PCA) on the qRT-PCR data resulted in the separation of treated samples from the control samples in both tissues. The results provided a basis for identification of transcriptional biomarkers for detecting amantadine residues in broiler chicken breast muscle and liver tissues.

ISOdb: A Comprehensive Database of Full-Length Isoforms Generated by Iso-Seq

The accurate landscape of transcript isoforms plays an important role in the understanding of gene function and gene regulation. However, building complete transcripts is very challenging for short reads generated using next-generation sequencing. Fortunately, isoform sequencing (Iso-Seq) using single-molecule sequencing technologies, such as PacBio SMRT, provides long reads spanning entire transcript isoforms which do not require assembly. Therefore, we have developed ISOdb, a comprehensive resource database for hosting and carrying out an in-depth analysis of Iso-Seq datasets and visualising the full-length transcript isoforms. The current version of ISOdb has collected 93 publicly available Iso-Seq samples from eight species and presents the samples in two levels: (1) sample level, including metainformation, long read distribution, isoform numbers, and alternative splicing (AS) events of each sample; (2) gene level, including the total isoforms, novel isoform number, novel AS number, and isoform visualisation of each gene. In addition, ISOdb provides a user interface in the website for uploading sample information to facilitate the collection and analysis of researchers' datasets. Currently, ISOdb is the first repository that offers comprehensive resources and convenient public access for hosting, analysing, and visualising Iso-Seq data, which is freely available.