scholarly journals A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 449 ◽  
Author(s):  
JiaRui Li ◽  
Lei Chen ◽  
Yu-Hang Zhang ◽  
XiangYin Kong ◽  
Tao Huang ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Expression Patterns ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Support Vector ◽  
Specific Gene ◽  
Human Tissues ◽  
Tissue Development ◽  
Tissue Specific ◽  
And Function

Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on “qualitatively tissue-specific expressed genes” which are highly enriched in one or a group of tissues but paid less attention to “quantitatively tissue-specific expressed genes”, which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying “quantitatively tissue-specific expressed genes” capable of distinguishing 25 human tissues from their expression patterns. Our results uncovered the expression of 432 genes as optimal features for tissue classification, which were obtained with a Matthews Correlation Coefficient (MCC) of more than 0.99 yielded by a support vector machine (SVM). This constructed model was superior to the SVM model using tissue enriched genes and yielded MCC of 0.985 on an independent test dataset, indicating its good generalization ability. These 432 genes were proven to be widely expressed in multiple tissues and a literature review of the top 23 genes found that most of them support their discriminating powers. As a complement to previous studies, our discovery of these quantitatively tissue-specific genes provides insights into the detailed understanding of tissue development and function.

scholarly journals FLAMs: A self-replicating ex vivo model of alveolar macrophages for functional genetic studies

2021 ◽  
Author(s):  
Sean Thomas ◽  
Kathryn Wierenga ◽  
James Pestka ◽  
Andrew Olive
Keyword(s):  
Alveolar Macrophages ◽  
Ex Vivo ◽  
Fetal Liver ◽  
Expression Profiles ◽  
Surface Expression ◽  
Specific Gene ◽  
Ex Vivo Model ◽  
A Genome ◽  
Underlying Mechanisms ◽  
And Function

Alveolar macrophages (AMs) are tissue resident cells in the lungs derived from the fetal liver that maintain lung homeostasis and respond to inhaled stimuli. While the importance of AMs is undisputed, they remain refractory to standard experimental approaches and high-throughput functional genetics as they are challenging to isolate and rapidly lose AM properties in standard culture. This limitation hinders our understanding of key regulatory mechanisms that control AM maintenance and function. Here, we describe the development of a new model, fetal liver-derived alveolar-like macrophages (FLAMs), which maintains cellular morphologies, expression profiles, and functional mechanisms similar to murine AMs. FLAMs combine treatment with two key cytokines for AM maintenance, GM-CSF and TGFβ. We leveraged the long-term stability of FLAMs to develop functional genetic tools using CRISPR-Cas9-mediated gene editing. Targeted editing confirmed the role of AM-specific gene Marco and the IL-1 receptor Il1r1 in modulating the AM response to crystalline silica. Furthermore, a genome-wide knockout library using FLAMs identified novel genes required for surface expression of the AM marker Siglec-F, most notably those related to the peroxisome. Taken together, our results suggest that FLAMs are a stable, self-replicating model of AM function that enables previously impossible global genetic approaches to define the underlying mechanisms of AM maintenance and function.

scholarly journals On the scope and limitations of baker's yeast as a model organism for studying human tissue-specific pathways

2014 ◽  
Author(s):  
Shahin Mohammadi ◽  
Baharak Saberidokht ◽  
Shankar Subramaniam ◽  
Ananth Grama
Keyword(s):  
Drug Targets ◽  
Model Organism ◽  
Functional Space ◽  
Computational Method ◽  
Therapeutic Interventions ◽  
Human Tissues ◽  
Human Interactome ◽  
Tissue Specific ◽  
Complex Disorders ◽  
Human Genes

Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue. We propose a novel computational and statistical framework to systematically quantify the suitability of yeast as a model organism for different human tissues. We develop a computational method for dissecting the human interactome into tissue-specific cellular networks. Using these networks, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. We study these sub-spaces in detail, and relate them to the overall similarity of each tissue with yeast. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. We show that human-specific subsets of tissue-selective genes are significantly associated with the onset and development of a number of pathologies. Consequently, they provide excellent candidates as drug targets for therapeutic interventions. We also present a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans.

scholarly journals Phylogenetic Comparison and Splicing Analysis of the U1 snRNP-specific Protein U1C in Eukaryotes

2021 ◽  
Vol 8 ◽  
Author(s):  
Kai-Lu Zhang ◽  
Jian-Li Zhou ◽  
Jing-Fang Yang ◽  
Yu-Zhen Zhao ◽  
Debatosh Das ◽  
...  
Keyword(s):  
Gene Family ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Specific Protein ◽  
Comprehensive Overview ◽  
Small Nuclear Ribonucleoprotein ◽  
U1 Snrnp ◽  
Multiple Copies ◽  
And Function

As a pivotal regulator of 5’ splice site recognition, U1 small nuclear ribonucleoprotein (U1 snRNP)-specific protein C (U1C) regulates pre-mRNA splicing by interacting with other components of the U1 snRNP complex. Previous studies have shown that U1 snRNP and its components are linked to a variety of diseases, including cancer. However, the phylogenetic relationships and expression profiles of U1C have not been studied systematically. To this end, we identified a total of 110 animal U1C genes and compared them to homologues from yeast and plants. Bioinformatics analysis shows that the structure and function of U1C proteins is relatively conserved and is found in multiple copies in a few members of the U1C gene family. Furthermore, the expression patterns reveal that U1Cs have potential roles in cancer progression and human development. In summary, our study presents a comprehensive overview of the animal U1C gene family, which can provide fundamental data and potential cues for further research in deciphering the molecular function of this splicing regulator.

scholarly journals PREDICTIVE MODELLING AND ANALYTICS FOR DIABETES USING A MACHINE LEARNING APPROACH

2021 ◽  
Vol 9 (1) ◽  
pp. 215-223
Author(s):  
Prateek Mishra, Dr.Anurag Sharma, Dr. Abhishek Badholia
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Supervised Machine Learning ◽  
Undiagnosed Diabetes ◽  
Support Vector ◽  
Entire Body ◽  
Data Manipulation ◽  
Kernel Support Vector Machine

Adverse effects can be seen in the entire body due to the major disorders known as Diabetes. The risk of dangers like diabetic nephropathy, cardiac stroke and other disorders can increase severally because of the undiagnosed diabetes. Around the globe the people are suffering from this disease. For a healthy life early detection of this disease is very curtail. As the causes of the diabetes is increasing rapidly this disease might turn up as a reason for worldwide concern. Increasing the chances for a more accurate predictions and form experiences automatic learning by computational method may be provided by Machine Learning (ML). With the help of R data manipulation tool for trends development and with risk factor patterns detection in Pima Indian diabetes technique of machine learning is been used in the current researches. With the use of R data manipulation tool analysis and development five different predictive models is done for the categorization of patients into diabetic and non- diabetic.  supervised machine learning algorithms namely multifactor dimensionality reduction (MDR), k-nearest neighbor (k-NN), artificial neural network (ANN) radial basis function (RBF) kernel support vector machine and linear kernel support vector machine (SVM-linear) are used for this purpose.

scholarly journals Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer

2019 ◽  
Vol 104 (11) ◽  
pp. 5225-5237 ◽  
Author(s):  
Mariam Haffa ◽  
Andreana N Holowatyj ◽  
Mario Kratz ◽  
Reka Toth ◽  
Axel Benner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Adipose Tissue ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Human Study ◽  
Subcutaneous Fat ◽  
Specific Gene ◽  
Adipose Tissue Depots

Abstract Context Adipose tissue inflammation and dysregulated energy homeostasis are key mechanisms linking obesity and cancer. Distinct adipose tissue depots strongly differ in their metabolic profiles; however, comprehensive studies of depot-specific perturbations among patients with cancer are lacking. Objective We compared transcriptome profiles of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) from patients with colorectal cancer and assessed the associations of different anthropometric measures with depot-specific gene expression. Design Whole transcriptomes of VAT and SAT were measured in 233 patients from the ColoCare Study, and visceral and subcutaneous fat area were quantified via CT. Results VAT compared with SAT showed elevated gene expression of cytokines, cell adhesion molecules, and key regulators of metabolic homeostasis. Increased fat area was associated with downregulated lipid and small molecule metabolism and upregulated inflammatory pathways in both compartments. Comparing these patterns between depots proved specific and more pronounced gene expression alterations in SAT and identified unique associations of integrins and lipid metabolism–related enzymes. VAT gene expression patterns that were associated with visceral fat area poorly overlapped with patterns associated with self-reported body mass index (BMI). However, subcutaneous fat area and BMI showed similar associations with SAT gene expression. Conclusions This large-scale human study demonstrates pronounced disparities between distinct adipose tissue depots and reveals that BMI poorly correlates with fat mass–associated changes in VAT. Taken together, these results provide crucial evidence for the necessity to differentiate between distinct adipose tissue depots for a correct characterization of gene expression profiles that may affect metabolic health of patients with colorectal cancer.

scholarly journals Evaluation of Plant-Derived Promoters for Constitutive and Tissue-Specific Gene Expression in Potato

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1520
Author(s):  
Dmitry Miroshnichenko ◽  
Aleksey Firsov ◽  
Vadim Timerbaev ◽  
Oleg Kozlov ◽  
Anna Klementyeva ◽  
...  
Keyword(s):  
Gene Expression ◽  
Specific Activity ◽  
Expression Patterns ◽  
Camv 35S Promoter ◽  
35S Promoter ◽  
Specific Gene ◽  
Transcriptional Level ◽  
Promoter Strength ◽  
Tissue Specific ◽  
Camv 35S

Various plant-derived promoters can be used to regulate ectopic gene expression in potato. In the present study, four promoters derived from the potato genome have been characterized by the expression of identical cassettes carrying the fusion with the reporter β-glucuronidase (gusA) gene. The strengths of StUbi, StGBSS, StPat, and StLhca3 promoters were compared with the conventional constitutive CaMV 35S promoter in various organs (leaves, stems, roots, and tubers) of greenhouse-grown plants. The final amount of gene product was determined at the post-transcriptional level using histochemical analysis, fluorometric measurements, and Western blot analysis. The promoter strength comparison demonstrated that the StUbi promoter generally provided a higher level of constitutive β-glucuronidase accumulation than the viral CaMV 35S promoter. Although the StLhca3 promoter was predominantly expressed in a green tissue-specific manner (leaves and stems) while StGBSS and StPat mainly provided tuber-specific activity, a “promoter leakage” was also found. However, the degree of unspecific activity depended on the particular transgenic line and tissue. According to fluorometric data, the functional activity of promoters in leaves could be arranged as follows: StLhca3 > StUbi > CaMV 35S > StPat > StGBSS (from highest to lowest). In tubers, the higher expression was detected in transgenic plants expressing StPat-gusA fusion construct, and the strength order was as follows: StPat > StGBSS > StUbi > CaMV 35S > StLhca3. The observed differences between expression patterns are discussed considering the benefits and limitations for the usage of each promoter to regulate the expression of genes in a particular potato tissue.

scholarly journals Genome-wide and expression analysis of B-box gene family in pepper

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Ma ◽  
Jia-xi Dai ◽  
Xiao-wei Liu ◽  
Duo Lin
Keyword(s):  
Transcription Factors ◽  
Abiotic Stress ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Transient Expression Assay ◽  
Specific Expression ◽  
Tissue Specific ◽  
Duplication Events ◽  
Onion Inner Epidermis

Abstract Background BBX transcription factors are a kind of zinc finger transcription factors with one or two B-box domains, which partilant in plant growth, development and response to abiotic or biotic stress. The BBX family has been identified in Arabidopsis, rice, tomato and some other model plant genomes. Results Here, 24 CaBBX genes were identified in pepper (Capsicum annuum L.), and the phylogenic analysis, structures, chromosomal location, gene expression patterns and subcellular localizations were also carried out to understand the evolution and function of CaBBX genes. All these CaBBXs were divided into five classes, and 20 of them distributed in 11 of 12 pepper chromosomes unevenly. Most duplication events occurred in subgroup I. Quantitative RT-PCR indicated that several CaBBX genes were induced by abiotic stress and hormones, some had tissue-specific expression profiles or differentially expressed at developmental stages. Most of CaBBX members were predicated to be nucleus-localized in consistent with the transient expression assay by onion inner epidermis of the three tested CaBBX members (CaBBX5, 6 and 20). Conclusion Several CaBBX genes were induced by abiotic stress and exogenous phytohormones, some expressed tissue-specific and variously at different developmental stage. The detected CaBBXs act as nucleus-localized transcription factors. Our data might be a foundation in the identification of CaBBX genes, and a further understanding of their biological function in future studies.

scholarly journals Novel insights into potential therapeutic targets and biomarkers using integrated multi-omics approaches for dilated and ischemic cardiomyopathies

2020 ◽  
Author(s):  
Auste Kanapeckaite ◽  
Neringa Burokiene
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Expression Profiles ◽  
Target Selection ◽  
Therapeutic Targets ◽  
Gene Expression Profiles ◽  
Development Stage ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Specific Gene

At present heart failure treatment targets symptoms based on the left ventricle dysfunction severity; however, lack of systemic studies and available biological data to uncover heterogeneous underlying mechanisms on the scale of genomic, transcriptional and expressed protein level signifies the need to shift the analytical paradigm toward network centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover heart failure specific networks and potential therapeutic targets or biomarkers. Furthermore, it was demonstrated that transcriptomics data in combination with minded data from public databases can be used to elucidate specific gene expression profiles. This was achieved using machine learning algorithms to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system also introduced in this study. The described methodology could be very useful for the target selection and evaluation during the pre-clinical therapeutics development stage. Finally, the present study shed new light into the complex etiology of the heart failure differentiating between subtle changes in dilated and ischemic cardiomyopathy on the single cell, proteome and whole transcriptome level.

Differential expression of the closely linked KISS1, REN, and FLJ10761 genes in transgenic mice

2004 ◽  
Vol 17 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Ravi Nistala ◽  
Xiaoji Zhang ◽  
Curt D. Sigmund
Keyword(s):  
Transgenic Mice ◽  
Human Chromosome ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Artificial Chromosome ◽  
Chromosome 1 ◽  
Specific Expression ◽  
Tissue Specific ◽  
Tissue Specific Expression ◽  
Human Chromosome 1

We previously reported the development and characterization of transgenic mice containing a large 160-kb P1 artificial chromosome (PAC) encompassing the renin (REN) locus from human chromosome 1. Here we demonstrate that PAC160 not only encodes REN, but also complete copies of the next upstream (KISS1) and downstream ( FLJ10761 ) gene along human chromosome 1. Incomplete copies of the second upstream (PEPP3) and downstream (SOX13) genes are also present. The gene order PEPP3-KISS1-REN-FLJ10761-SOX13 is conserved in mice containing either one or two copies of the REN locus. Despite the close localization of KISS1, REN, and FLJ10761 , they each exhibit distinct, yet overlapping tissue-specific expression profiles in humans. The tissue-specific expression patterns of REN and FLJ10761 were retained in transgenic mice containing PAC160. Expression of REN and FLJ10761 were also proportional to copy number. Expression of KISS1 in PAC160 mice showed both similarities and differences to humans. These data suggest that expression of gene blocks encoded on large genomic clones are retained when the clones are used to generate transgenic mice. Genomic elements which act to insulate genes from their neighbors are also apparently retained.

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