scholarly journals Contributions from the 2018 Literature on Bioinformatics and Translational Informatics

2019 ◽  
Vol 28 (01) ◽  
pp. 190-193 ◽  
Author(s):  
Malika Smaïl-Tabbone ◽  
Bastien Rance ◽  
Keyword(s):  
Expression Profiles ◽  
Leading Edge ◽  
Computational Method ◽  
Omics Data ◽  
Neural Network Approach ◽  
Editorial Committee ◽  
Chemotherapy Drugs ◽  
Phenotype Classification ◽  
Translational Informatics

Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

scholarly journals Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 188-192
Author(s):  
Malika Smaïl-Tabbone ◽  
Bastien Rance ◽  
Keyword(s):  
Open Data ◽  
Leading Edge ◽  
Open Science ◽  
Editorial Committee ◽  
Cancer Pathways ◽  
Sequence Of Events ◽  
Independent Review ◽  
Logical Modeling ◽  
Translational Informatics ◽  
Selection Of

Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

The Predominant microRNAs in β-cell Clusters for Insulin Regulation and Diabetic Control

2020 ◽  
Vol 21 (7) ◽  
pp. 722-734
Author(s):  
Adele Soltani ◽  
Arefeh Jafarian ◽  
Abdolamir Allameh
Keyword(s):  
Mirna Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Diabetic Control ◽  
In Vitro Proliferation ◽  
Cell Functions ◽  
Mirna Expression Profiles ◽  
Multiple Processes ◽  
The Impact

micro (mi)-RNAs are vital regulators of multiple processes including insulin signaling pathways and glucose metabolism. Pancreatic β-cells function is dependent on some miRNAs and their target mRNA, which together form a complex regulative network. Several miRNAs are known to be directly involved in β-cells functions such as insulin expression and secretion. These small RNAs may also play significant roles in the fate of β-cells such as proliferation, differentiation, survival and apoptosis. Among the miRNAs, miR-7, miR-9, miR-375, miR-130 and miR-124 are of particular interest due to being highly expressed in these cells. Under diabetic conditions, although no specific miRNA profile has been noticed, the expression of some miRNAs and their target mRNAs are altered by posttranscriptional mechanisms, exerting diverse signs in the pathobiology of various diabetic complications. The aim of this review article is to discuss miRNAs involved in the process of stem cells differentiation into β-cells, resulting in enhanced β-cell functions with respect to diabetic disorders. This paper will also look into the impact of miRNA expression patterns on in vitro proliferation and differentiation of β-cells. The efficacy of the computational genomics and biochemical analysis to link the changes in miRNA expression profiles of stem cell-derived β-cells to therapeutically relevant outputs will be discussed as well.

scholarly journals Honeysuckle Aqueous Extracts Induced let-7a Suppress EV71 Replication and Pathogenesis In Vitro and In Vivo and Is Predicted to Inhibit SARS-CoV-2

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
Ying-Ray Lee ◽  
Chia-Ming Chang ◽  
Yuan-Chieh Yeh ◽  
Chi-Ying F. Huang ◽  
Feng-Mao Lin ◽  
...  
Keyword(s):  
Protein Expression ◽  
Western Blotting ◽  
Expression Profiles ◽  
Plaque Assay ◽  
Virus Titer ◽  
Enterovirus 71 ◽  
Luciferase Reporter ◽  
Pathogen Invasion

Honeysuckle (Lonicera japonica Thunb) is a traditional Chinese medicine (TCM) with an antipathogenic activity. MicroRNAs (miRNAs) are small non-coding RNA molecules that are ubiquitously expressed in cells. Endogenous miRNA may function as an innate response to block pathogen invasion. The miRNA expression profiles of both mice and humans after the ingestion of honeysuckle were obtained. Fifteen overexpressed miRNAs overlapped and were predicted to be capable of targeting three viruses: dengue virus (DENV), enterovirus 71 (EV71) and SARS-CoV-2. Among them, let-7a was examined to be capable of targeting the EV71 RNA genome by reporter assay and Western blotting. Moreover, honeysuckle-induced let-7a suppression of EV71 RNA and protein expression as well as viral replication were investigated both in vitro and in vivo. We demonstrated that let-7a targeted EV71 at the predicted sequences using luciferase reporter plasmids as well as two infectious replicons (pMP4-y-5 and pTOPO-4643). The suppression of EV71 replication and viral load was demonstrated in two cell lines by luciferase activity, RT-PCR, real-time PCR, Western blotting and plaque assay. Furthermore, EV71-infected suckling mice fed honeysuckle extract or inoculated with let-7a showed decreased clinical scores and a prolonged survival time accompanied with decreased viral RNA, protein expression and virus titer. The ingestion of honeysuckle attenuates EV71 replication and related pathogenesis partially through the upregulation of let-7a expression both in vitro and in vivo. Our previous report and the current findings imply that both honeysuckle and upregulated let-7a can execute a suppressive function against the replication of DENV and EV71. Taken together, this evidence indicates that honeysuckle can induce the expression of let-7a and that this miRNA as well as 11 other miRNAs have great potential to prevent and suppress EV71 replication.

scholarly journals Controllable Drug Delivery by Na+/K+ ATPase α1 Targeting Peptide Conjugated DSPE-PEG Nanocarriers for Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110278
Author(s):  
Yayan Yang ◽  
Qian Feng ◽  
Chuanfeng Ding ◽  
Wei Kang ◽  
Xiufeng Xiao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Delivery ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Click Reaction ◽  
Chemotherapy Drugs ◽  
Targeting Peptide ◽  
And Migration

Although Epirubicin (EPI) is a commonly used anthracycline for the treatment of breast cancer in clinic, the serious side effects limit its long-term administration including myelosuppression and cardiomyopathy. Nanomedicines have been widely utilized as drug delivery vehicles to achieve precise targeting of breast cancer cells. Herein, we prepared a DSPE-PEG nanocarrier conjugated a peptide, which targeted the breast cancer overexpression protein Na+/K+ ATPase α1 (NKA-α1). The nanocarrier encapsulated the EPI and grafted with the NKA-α1 targeting peptide through the click reaction between maleimide and thiol groups. The EPI was slowly released from the nanocarrier after entering the breast cancer cells with the guidance of the targeting NKA-α1 peptide. The precise and controllable delivery and release of the EPI into the breast cancer cells dramatically inhibited the cells proliferation and migration in vitro and suppressed the tumor volume in vivo. These results demonstrate significant prospects for this nanocarrier as a promising platform for numerous chemotherapy drugs.

scholarly journals Differential expression and correlation analysis of miRNA–mRNA profiles in swine testicular cells infected with porcine epidemic diarrhea virus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoqian Zhang ◽  
Chang Li ◽  
Bingzhou Zhang ◽  
Zhonghua Li ◽  
Wei Zeng ◽  
...  
Keyword(s):  
Differential Expression ◽  
Porcine Epidemic Diarrhea Virus ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Bacterial Invasion ◽  
Sequencing Data ◽  
Diarrhea Virus ◽  
Comparison Groups ◽  
Porcine Epidemic Diarrhea

AbstractThe variant virulent porcine epidemic diarrhea virus (PEDV) strain (YN15) can cause severe porcine epidemic diarrhea (PED); however, the attenuated vaccine-like PEDV strain (YN144) can induce immunity in piglets. To investigate the differences in pathogenesis and epigenetic mechanisms between the two strains, differential expression and correlation analyses of the microRNA (miRNA) and mRNA in swine testicular (ST) cells infected with YN15, YN144, and mock were performed on three comparison groups (YN15 vs Control, YN144 vs Control, and YN15 vs YN144). The mRNA and miRNA expression profiles were obtained using next-generation sequencing (NGS), and the differentially expressed (DE) (p-value < 0.05) mRNA and miRNA were obtained using DESeq R package. mRNAs targeted by DE miRNAs were predicted using the miRanda algortithm. 8039, 8631 and 3310 DE mRNAs, and 36, 36, and 22 DE miRNAs were identified in the three comparison groups, respectively. 14,140, 15,367 and 3771 DE miRNA–mRNA (targeted by DE miRNAs) interaction pairs with negatively correlated expression patterns were identified, and interaction networks were constructed using Cytoscape. Six DE miRNAs and six DE mRNAs were randomly selected to verify the sequencing data by real-time relative quantitative reverse transcription polymerase chain reaction (qRT-PCR). Based on bioinformatics analysis, we discovered the differences were mostly involved in host immune responses and viral pathogenicity, including NF-κB signaling pathway and bacterial invasion of epithelial cells, etc. This is the first comprehensive comparison of DE miRNA–mRNA pairs in YN15 and YN144 infection in vitro, which could provide novel strategies for the prevention and control of PED.

scholarly journals Transcriptome analysis of gravitational effects on mouse skeletal muscles under microgravity and artificial 1 g onboard environment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Risa Okada ◽  
Shin-ichiro Fujita ◽  
Riku Suzuki ◽  
Takuto Hayashi ◽  
Hirona Tsubouchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Muscle Mass ◽  
Muscle Atrophy ◽  
Transcriptome Analysis ◽  
Fiber Type ◽  
Expression Profiles ◽  
Space Station ◽  
Gene Expression Profiles

AbstractSpaceflight causes a decrease in skeletal muscle mass and strength. We set two murine experimental groups in orbit for 35 days aboard the International Space Station, under artificial earth-gravity (artificial 1 g; AG) and microgravity (μg; MG), to investigate whether artificial 1 g exposure prevents muscle atrophy at the molecular level. Our main findings indicated that AG onboard environment prevented changes under microgravity in soleus muscle not only in muscle mass and fiber type composition but also in the alteration of gene expression profiles. In particular, transcriptome analysis suggested that AG condition could prevent the alterations of some atrophy-related genes. We further screened novel candidate genes to reveal the muscle atrophy mechanism from these gene expression profiles. We suggest the potential role of Cacng1 in the atrophy of myotubes using in vitro and in vivo gene transductions. This critical project may accelerate the elucidation of muscle atrophy mechanisms.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

scholarly journals A Preclinical Evaluation of the Antitumor Activities of Edible and Medicinal Mushrooms: A Molecular Insight

2017 ◽  
Vol 17 (2) ◽  
pp. 200-209 ◽  
Author(s):  
Thomson Patrick Joseph ◽  
Warren Chanda ◽  
Arshad Ahmed Padhiar ◽  
Samana Batool ◽  
Shao LiQun ◽  
...  
Keyword(s):  
Side Effects ◽  
Biological Activities ◽  
Genetic Alterations ◽  
In Vivo Models ◽  
Medicinal Mushrooms ◽  
Chemotherapy Drugs ◽  
Development Of Resistance ◽  
Preclinical And Clinical Studies

Cancer is the leading cause of morbidity and mortality around the globe. For certain types of cancer, chemotherapy drugs have been extensively used for treatment. However, severe side effects and the development of resistance are the drawbacks of these agents. Therefore, development of new agents with no or minimal side effects is of utmost importance. In this regard, natural compounds are well recognized as drugs in several human ailments, including cancer. One class of fungi, “mushrooms,” contains numerous compounds that exhibit interesting biological activities, including antitumor activity. Many researchers, including our own group, are focusing on the anticancer potential of different mushrooms and the underlying molecular mechanism behind their action. The aim of this review is to discuss PI3K/AKT, Wnt-CTNNB1, and NF-κB signaling pathways, the occurrence of genetic alterations in them, the association of these aberrations with different human cancers and how different nodes of these pathways are targeted by various substances of mushroom origin. We have given evidence to propose the therapeutic attributes and possible mode of molecular actions of various mushroom-originated compounds. However, anticancer effects were typically demonstrated in in vitro and in vivo models and very limited number of studies have been conducted in the human population. It is our belief that this review will help the research community in designing concrete preclinical and clinical studies to test the anticancer potential of mushroom-originated compounds on different cancers harboring particular genetic alteration(s).

scholarly journals Findings from the Section on Bioinformatics and Translational Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 188-192 ◽  
Author(s):  
H. Dauchel ◽  
T. Lecroq
Keyword(s):  
Intelligent Systems ◽  
Large Scale ◽  
Cancer Genomics ◽  
Clinical Care ◽  
Evaluation Process ◽  
Omics Data ◽  
Health Domain ◽  
Medical Genomics ◽  
Research Activities ◽  
Translational Informatics

Summary Objective: To summarize excellent current research and propose a selection of best papers published in 2016 in the field of Bioinformatics and Translational Informatics with applications in the health domain and clinical care. Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. As in 2016, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section coverage. Each section editor evaluated separately the set of 951 articles returned and evaluation results were merged for retaining 15 candidate best papers for peer-review. Results: The selection and evaluation process of papers published in the Bioinformatics and Translational Informatics field yielded four excellent articles focusing this year on the secondary use and massive integration of multi-omics data for cancer genomics and non-cancer complex diseases. Papers present methods to study the functional impact of genetic variations, either at the level of the transcription or at the levels of pathway and network. Conclusions: Current research activities in Bioinformatics and Translational Informatics with applications in the health domain continue to explore new algorithms and statistical models to manage, integrate, and interpret large-scale genomic datasets. As addressed by some of the selected papers, future trends would include the question of the international collaborative sharing of clinical and omics data, and the implementation of intelligent systems to enhance routine medical genomics.

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