scholarly journals Novel Approach Combining Transcriptional and Evolutionary Signatures to Identify New Multiciliation Genes

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1452
Author(s):  
Audrey Defosset ◽  
Dorine Merlat ◽  
Laetitia Poidevin ◽  
Yannis Nevers ◽  
Arnaud Kress ◽  
...  

Multiciliogenesis is a complex process that allows the generation of hundreds of motile cilia on the surface of specialized cells, to create fluid flow across epithelial surfaces. Dysfunction of human multiciliated cells is associated with diseases of the brain, airway and reproductive tracts. Despite recent efforts to characterize the transcriptional events responsible for the differentiation of multiciliated cells, a lot of actors remain to be identified. In this work, we capitalize on the ever-growing quantity of high-throughput data to search for new candidate genes involved in multiciliation. After performing a large-scale screening using 10 transcriptomics datasets dedicated to multiciliation, we established a specific evolutionary signature involving Otomorpha fish to use as a criterion to select the most likely targets. Combining both approaches highlighted a list of 114 potential multiciliated candidates. We characterized these genes first by generating protein interaction networks, which showed various clusters of ciliated and multiciliated genes, and then by computing phylogenetic profiles. In the end, we selected 11 poorly characterized genes that seem like particularly promising multiciliated candidates. By combining functional and comparative genomics methods, we developed a novel type of approach to study biological processes and identify new promising candidates linked to that process.

1984 ◽  
Vol 62 (8) ◽  
pp. 998-1009 ◽  
Author(s):  
P. Muller ◽  
Lise Martin

Our primary goal is to develop a screening procedure to detect and partially characterize neurotoxic compounds. There is a great need for a new approach to screening for neurotoxicants because our industrialized world abounds with untested and potentially neurotoxic compounds. A large number of new compounds are introduced each year. Although a number of testing approaches to the screening for neurotoxicants have been proposed in the recent years, a consensus on the most adequate approach is yet to emerge. The existing methods share a number of shortcomings. Thus, most methods only detect a fraction of the tested neurotoxicants. Other methods lack the necessary resolution to detect the neurotoxic damage reproducibly and reliably. Furthermore, many screening approaches are too time consuming and costly to be used for the large-scale screening of neurotoxicants. It is, therefore, imperative to develop reliable and efficient screening methods applicable in regulatory toxicology. In this report, we describe two versions of the same method that we feel may be very beneficial for the large-scale screening of neurotoxicants. The 2-deoxyglucose (2-DG) uptake method provides an indirect measure of neuronal activity in different areas of the brain. The ability of the method to detect most, if not all, neuroactive substances is reviewed in this report. In the context of this report, a neuroactive substance is defined as a substance acting directly on the central or peripheral nervous system neurons and (or) glia. The 2-DG method equals the sensitivity of the most sensitive alternative methods which were selectively designed to detect the effects of specific groups of compounds. The generality and sensitivity of the 2-DG method are of major importance. Thus, if a tested compound does not affect the uptake of 2-DG into the brain, it is not likely to be neuroactive. Since neurotoxic compounds are a subset of neuroactive compounds, a compound that is not neuroactive is also not neurotoxic. Thus, a single test may, in some instances, determine if a tested compound is nontoxic. In addition, it appears that each compound or, at least, each family of compounds produces a characteristic profile ("pattern") of the sites of altered 2-DG uptake. This pattern can be exploited to characterize the tested compound and help us decide whether it is neurotoxic or neuroactive. Preliminary results from our laboratory indicate classical neurotoxic agents such as acrylamide, triethyltin, and 2,5-hexanedione induce a generalized depression of the 2-DG uptake throughout the brain. As a result, it may be difficult to differentiate these compounds on the basis of their respective 2-DG pattern. On the other hand, most of the therapeutics tested to date have distinct 2-DG uptake patterns which may help to distinguish therapeutics from classicial neurotoxicants. Additional experimental work is required to clarify this matter. The method has not been extensively used in the field of toxicology, mainly because the existing original 2-DG method is too complex and expensive for the large-scale screening for neurotoxicants. In this report, we review the efforts made to simplify this method as a general neurotoxicology screen. Our initial experience with this method in testing for neurotoxic compounds is also reviewed.


F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 44 ◽  
Author(s):  
Jose M. Villaveces ◽  
Rafael C. Jimenez ◽  
Bianca H. Habermann

Summary: Protein interaction networks have become an essential tool in large-scale data analysis, integration, and the visualization of high-throughput data in the context of complex cellular networks. Many individual databases are available that provide information on binary interactions of proteins and small molecules. Community efforts such as PSICQUIC aim to unify and standardize information emanating from these public databases. Here we introduce PsicquicGraph, an open-source, web-based visualization component for molecular interactions from PSIQUIC services. Availability: PsicquicGraph is freely available at the BioJS Registry for download and enhancement. Instructions on how to use the tool are available here http://goo.gl/kDaIgZ and the source code can be found at http://github.com/biojs/biojs and DOI:10.5281/zenodo.7709.


2019 ◽  
Author(s):  
Javier Mariscal ◽  
Tatyana Vagner ◽  
Minhyung Kim ◽  
Bo Zhou ◽  
Andrew Chin ◽  
...  

AbstractExtracellular vesicles (EVs) are membrane-enclosed particles that play an important role in cancer progression and have emerged as a promising source of circulating biomarkers. Protein S-acylation, also known as palmitoylation, has been proposed as a post-translational mechanism that modulates the dynamics of EV biogenesis and protein cargo sorting. However, technical challenges have limited large-scale profiling of the whole palmitoyl-proteins of EVs. We successfully employed a novel approach that combines low-background acyl-biotinyl exchange (LB-ABE) with label-free proteomics to analyze the palmitoyl proteome of large EVs (L-EVs) and small EVs (S-EVs) from prostate cancer cells. Here we report the first palmitoyl-protein signature of EVs, and demonstrate that L- and S-EVs harbor proteins associated with distinct biological processes and subcellular origin. We identified STEAP1, STEAP2, and ABBC4 as prostate cancer-specific palmitoyl proteins enriched in both EV populations in comparison with the originating cell lines. Importantly, the presence of the above proteins in EVs was significantly reduced upon inhibition of palmitoylation in the producing cells. These results suggest that palmitoylation may be involved in the differential sorting of proteins to distinct EV populations and allow for better detection of disease biomarkers.


Author(s):  
Benjamin Hall ◽  
Anna Niarakis

Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signalling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high throughput data. Novel, literature-based representations of biological processes and emerging machine learning algorithms offer new opportunities for model construction. Here, we review recent efforts to incorporate omic data into logic-based models and discuss critical challenges in constructing and analysing integrative, large-scale, logic-based models of biological mechanisms.


Author(s):  
Benjamin Hall ◽  
Anna Niarakis

Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signalling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models, and discuss critical difficulties in constructing and analysing integrative, large-scale, logic-based models of biological mechanisms.


1976 ◽  
Vol 7 (4) ◽  
pp. 236-241 ◽  
Author(s):  
Marisue Pickering ◽  
William R. Dopheide

This report deals with an effort to begin the process of effectively identifying children in rural areas with speech and language problems using existing school personnel. A two-day competency-based workshop for the purpose of training aides to conduct a large-scale screening of speech and language problems in elementary-school-age children is described. Training strategies, implementation, and evaluation procedures are discussed.


The success of the Program of housing stock renovation in Moscow depends on the efficiency of resource management. One of the main urban planning documents that determine the nature of the reorganization of residential areas included in the Program of renovation is the territory planning project. The implementation of the planning project is a complex process that has a time point of its beginning and end, and also includes a set of interdependent parallel-sequential activities. From an organizational point of view, it is convenient to use network planning and management methods for project implementation. These methods are based on the construction of network models, including its varieties – a Gantt chart. A special application has been developed to simulate the implementation of planning projects. The article describes the basic principles and elements of modeling. The list of the main implementation parameters of the Program of renovation obtained with the help of the developed software for modeling is presented. The variants of using the results obtained for a comprehensive analysis of the implementation of large-scale urban projects are proposed.


2019 ◽  
Author(s):  
Chem Int

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.


2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.


2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.


Sign in / Sign up

Export Citation Format

Share Document