scholarly journals A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

2019 ◽  
Author(s):  
Yue Qiu ◽  
Tianhuan Lu ◽  
Hansaim Lim ◽  
Lei Xie
Keyword(s):  
Disease Modeling ◽  
Drug Repurposing ◽  
Gaussian Mixture ◽  
Large Sets ◽  
Peak Deconvolution ◽  
Cellular Expression ◽  
Disease Mechanisms ◽  
Bayes Theory ◽  
Signature Detection ◽  
Accurate Expression

AbstractLINCS L1000 dataset produced by L1000 assay contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, severe noise in the dataset makes the detection of reliable gene expression signals difficult. Existing methods for the peak deconvolution, either k-means based or Gaussian mixture model, cannot reliably recover the accurate expression level of genes in many cases, thereby limiting their robust applications in biomedical studies. Here, we have developed a novel Bayes’ theory based deconvolution algorithm that gives unbiased likelihood estimations for peak positions and characterizes the peak with a probability based z-scores. Based on above algorithm, a pipeline is built to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed new pipeline is rigorously evaluated using the similarity between bio-replicates and between drugs with shared targets. The results show that the new signature derived from the proposed algorithm gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, our new Bayesian-based peak deconvolution and z-score calculation method may significantly boost the performance of invaluable L1000 data in the down-stream applications such as drug repurposing, disease modeling, and gene function prediction.

scholarly journals A Bayesian approach to accurate and robust signature detection on LINCS L1000 data

2020 ◽  
Vol 36 (9) ◽  
pp. 2787-2795
Author(s):  
Yue Qiu ◽  
Tianhuan Lu ◽  
Hansaim Lim ◽  
Lei Xie
Keyword(s):  
Disease Modeling ◽  
Drug Repurposing ◽  
Supplementary Information ◽  
Large Sets ◽  
Peak Deconvolution ◽  
Cellular Expression ◽  
Disease Mechanisms ◽  
Signature Detection ◽  
Accurate Expression ◽  
Z Scores

Abstract Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. Availability and implementation The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modeling Rare Human Disorders in Mice: The Finnish Disease Heritage

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3158
Author(s):  
Tomáš Zárybnický ◽  
Anne Heikkinen ◽  
Salla M. Kangas ◽  
Marika Karikoski ◽  
Guillermo Antonio Martínez-Nieto ◽  
...  
Keyword(s):  
Human Physiology ◽  
Animal Models ◽  
Mouse Models ◽  
Rare Diseases ◽  
Disease Modeling ◽  
Common Disease ◽  
Molecular Pathways ◽  
Finnish Population ◽  
Disease Mechanisms ◽  
Human Disorders

The modification of genes in animal models has evidently and comprehensively improved our knowledge on proteins and signaling pathways in human physiology and pathology. In this review, we discuss almost 40 monogenic rare diseases that are enriched in the Finnish population and defined as the Finnish disease heritage (FDH). We will highlight how gene-modified mouse models have greatly facilitated the understanding of the pathological manifestations of these diseases and how some of the diseases still lack proper models. We urge the establishment of subsequent international consortiums to cooperatively plan and carry out future human disease modeling strategies. Detailed information on disease mechanisms brings along broader understanding of the molecular pathways they act along both parallel and transverse to the proteins affected in rare diseases, therefore also aiding understanding of common disease pathologies.

scholarly journals Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

2020 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Claudia Beaurivage ◽  
Matthew Hancock ◽  
Erik Verschueren
Keyword(s):  
Dihedral Angle ◽  
Target Selection ◽  
Gaussian Mixture Models ◽  
Drug Repurposing ◽  
Gaussian Mixture ◽  
Rational Drug Design ◽  
Drug Candidates ◽  
Novel Approach ◽  
Protein Fingerprinting ◽  
Rational Drug

AbstractTarget evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.

Genome editing of hPSCs: Recent progress in hPSC-based disease modeling for understanding disease mechanisms

2021 ◽  
Author(s):  
Dong-Kyu Choi ◽  
Yong-Kyu Kim ◽  
Ji HoonYu ◽  
Sang-Hyun Min ◽  
Sang-Wook Park
Keyword(s):  
Genome Editing ◽  
Recent Progress ◽  
Disease Modeling ◽  
Disease Mechanisms

scholarly journals A Personalized Glomerulus Chip Engineered from Stem Cell-Derived Epithelium and Vascular Endothelium

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 967
Author(s):  
Yasmin Roye ◽  
Rohan Bhattacharya ◽  
Xingrui Mou ◽  
Yuhao Zhou ◽  
Morgan A. Burt ◽  
...  
Keyword(s):  
Stem Cell ◽  
Kidney Disease ◽  
Structural Integrity ◽  
Disease Modeling ◽  
Patient Specific ◽  
Ips Cell ◽  
Filtration Barrier ◽  
Disease Mechanisms ◽  
Kidney Glomerulus

Progress in understanding kidney disease mechanisms and the development of targeted therapeutics have been limited by the lack of functional in vitro models that can closely recapitulate human physiological responses. Organ Chip (or organ-on-a-chip) microfluidic devices provide unique opportunities to overcome some of these challenges given their ability to model the structure and function of tissues and organs in vitro. Previously established organ chip models typically consist of heterogenous cell populations sourced from multiple donors, limiting their applications in patient-specific disease modeling and personalized medicine. In this study, we engineered a personalized glomerulus chip system reconstituted from human induced pluripotent stem (iPS) cell-derived vascular endothelial cells (ECs) and podocytes from a single patient. Our stem cell-derived kidney glomerulus chip successfully mimics the structure and some essential functions of the glomerular filtration barrier. We further modeled glomerular injury in our tissue chips by administering a clinically relevant dose of the chemotherapy drug Adriamycin. The drug disrupts the structural integrity of the endothelium and the podocyte tissue layers, leading to significant albuminuria as observed in patients with glomerulopathies. We anticipate that the personalized glomerulus chip model established in this report could help advance future studies of kidney disease mechanisms and the discovery of personalized therapies. Given the remarkable ability of human iPS cells to differentiate into almost any cell type, this work also provides a blueprint for the establishment of more personalized organ chip and ‘body-on-a-chip’ models in the future.

scholarly journals Neuronal and Astrocytic Differentiation from Sanfilippo C Syndrome iPSCs for Disease Modeling and Drug Development

2020 ◽  
Vol 9 (3) ◽  
pp. 644 ◽  
Author(s):  
Noelia Benetó ◽  
Monica Cozar ◽  
Laura Castilla-Vallmanya ◽  
Oskar G. Zetterdahl ◽  
Madalina Sacultanu ◽  
...  
Keyword(s):  
Drug Development ◽  
Disease Modeling ◽  
Neuronal Model ◽  
Therapeutic Interventions ◽  
Neural Cells ◽  
Lysosomal Storage ◽  
Sanfilippo Syndrome ◽  
Cellular Models ◽  
Disease Mechanisms

Sanfilippo syndrome type C (mucopolysaccharidosis IIIC) is an early-onset neurodegenerative lysosomal storage disorder, which is currently untreatable. The vast majority of studies focusing on disease mechanisms of Sanfilippo syndrome were performed on non-neural cells or mouse models, which present obvious limitations. Induced pluripotent stem cells (iPSCs) are an efficient way to model human diseases in vitro. Recently developed transcription factor-based differentiation protocols allow fast and efficient conversion of iPSCs into the cell type of interest. By applying these protocols, we have generated new neuronal and astrocytic models of Sanfilippo syndrome using our previously established disease iPSC lines. Moreover, our neuronal model exhibits disease-specific molecular phenotypes, such as increase in lysosomes and heparan sulfate. Lastly, we tested an experimental, siRNA-based treatment previously shown to be successful in patients’ fibroblasts and demonstrated its lack of efficacy in neurons. Our findings highlight the need to use relevant human cellular models to test therapeutic interventions and shows the applicability of our neuronal and astrocytic models of Sanfilippo syndrome for future studies on disease mechanisms and drug development.

Biological and biomedical applications of collagenous biomaterials

1990 ◽  
Vol 48 (3) ◽  
pp. 856-857
Author(s):  
Yasushi P. Kato ◽  
Michael G. Dunn ◽  
Frederick H. Silver ◽  
Arthur J. Wasserman
Keyword(s):  
Biomedical Applications ◽  
Soft Tissues ◽  
Collagen Fibers ◽  
Bone Collagen ◽  
Cover Slip ◽  
Fibroblast Migration ◽  
Cellular Expression ◽  
Defect Repair

Collagenous biomaterials have been used for growing cells in vitro as well as for augmentation and replacement of hard and soft tissues. The substratum used for culturing cells is implicated in the modulation of phenotypic cellular expression, cellular orientation and adhesion. Collagen may have a strong influence on these cellular parameters when used as a substrate in vitro. Clinically, collagen has many applications to wound healing including, skin and bone substitution, tendon, ligament, and nerve replacement. In this report we demonstrate two uses of collagen. First as a fiber to support fibroblast growth in vitro, and second as a demineralized bone/collagen sponge for radial bone defect repair in vivo.For the in vitro study, collagen fibers were prepared as described previously. Primary rat tendon fibroblasts (1° RTF) were isolated and cultured for 5 days on 1 X 15 mm sterile cover slips. Six to seven collagen fibers, were glued parallel to each other onto a circular cover slip (D=18mm) and the 1 X 15mm cover slip populated with 1° RTF was placed at the center perpendicular to the collagen fibers. Fibroblast migration from the 1 x 15mm cover slip onto and along the collagen fibers was measured daily using a phase contrast microscope (Olympus CK-2) with a calibrated eyepiece. Migratory rates for fibroblasts were determined from 36 fibers over 4 days.

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