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An Overview of Parasite Diversity
Published in Eric S. Loker, Bruce V. Hofkin, Parasitology, 2023
Eric S. Loker, Bruce V. Hofkin
For instance, the power of sequencing techniques like RNA-Seq allows one to rapidly profile the mRNA molecules being made within a biological sample (referred to as the transcriptome), thereby gaining a picture of what genes are being expressed. Many studies have now examined the transcriptome in different parasite life cycle stages, different tissues or organs within a parasite like a helminth, or even from single parasite cells. All provide distinctive insights into how the genome is used. Figure 2.35 is of a “heat map” that measures for a long list of parasite genes the extent to which each is expressed in different life cycle stages of S. mansoni. Note the distinctive patterns of gene expression for the six different life cycle stages shown. Increasingly detailed transcriptional profiles of individual organs of S. mansoni are available highlighting, for example, the different suites of genes produced in testes of male, or ovaries of female worms, including from worms that are paired or not. As another example, studies of different life cycle stages of the gut-inhabiting apicomplexan Cryptosporidium (sporozoites, epicellular forms and oocysts) reveal that the epicellular/intracellular stages of the parasite produce many more transcripts than found in oocysts, many of which are related to active biosynthesis for these rapidly growing stages.
Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
Drug repurposing or repositioning represents one of the more efficient approaches for finding potential therapeutics via identification of new applications for existing drugs at a lower cost and in a shorter time [129–139]. In applications to SARS-CoV-2, computational drug-repositioning approaches can be grouped into network-based models, structure-based approaches, signature- based approaches, molecular docking, genome-wide association studies (GWAS), and AI approaches [130, 140]. In signature-based drug repositioning, high-throughput omics data (transcriptomic, proteomic, or metabolomic), as well as molecular structures, and adverse effect profiles are used to compare the pattern of gene expression profiles of a drug against gene expression profiles of another drug (i.e., drug–drug comparison), disease (i.e., drug-disease comparison), or clinical phenotype [141]. In molecular docking, which is an important component of the structure-based drug repurposing (SBDR) techniques [142], unknown interactions between receptor target and leads are discovered by screening of compound libraries against targets to discover candidates for drug repurposing processes [143]. The network-based and pathway-based drug repurposing relies on the construction of biological networks by using different data types, such as disease pathology, gene expression patterns, and protein interactions [144]. The differences in genetic material related to common diseases that can be found by GWAS generate an important knowledge that can give rise to repurposing of drugs [145].
Genetic testing for personalised medicine and limitations of the current medical practise in public health
Published in Ben Y.F. Fong, Martin C.S. Wong, The Routledge Handbook of Public Health and the Community, 2021
In addition, a cohort research project on asthma, namely Unbiased BIOmarkers in PREDiction of respiratory disease outcomes (U-BIOPRED), has demonstrated that the use of the transcriptomic approach could lead to accurate diagnosis of different sub-types of asthma (Kuo et al., 2017). Different sub-types of asthma are treated differently, as they trigger different immunological responses in patients that can lead to similar phenotypes of asthmatic episodes. Together, these studies point to the direction that transcriptomic technologies allow for differentiation of subtypes of asthma and for designing effective treatment plans.
Translational impact of omics studies in alopecia areata: recent advances and future perspectives
Published in Expert Review of Clinical Immunology, 2022
F. Buket Basmanav, Regina C. Betz
AA is an autoimmune disorder with an estimated lifetime risk of about 2%. Although AA is not a life-threatening condition, it poses a large psychological burden on those who are affected and is often associated with other immune-mediated diseases as well as depression and anxiety. There are currently no FDA approved drugs and until recently, treatments have been based on modulation of immune response. So far, two GWAS of AA have been published delivering altogether 14 loci which have surpassed the genome-wide significance threshold. Follow up of these GWAS findings by a mechanistic study which involved transcriptome profiling in mice and human showed successful treatment of AA in mice models and three human subjects by using JAK inhibition as a new therapeutic modality in AA. These findings together with other clinical case reports from patients with concomitant immune-mediated diseases whose AA showed remission after JAK inhibition paved the way for initiation of first clinical trials of JAK inhibitors in treatment of AA. Several genome-wide gene expression studies led to the identification of consistent molecular disease signatures which were used for molecular monitoring of drug response in several clinical trials. Furthermore, several attempts are reported to develop clinical tools using transcriptome data for the prediction of disease prognosis and drug response. Future omics research in AA holds great translational potential for the discovery of novel therapeutic modalities and development of clinical tools for precision medicine.
Peripheral Blood Transcriptome in Patients with Sarcoidosis-Associated Uveitis
Published in Ocular Immunology and Inflammation, 2022
John A. Gonzales, Jaskirat S. Takhar, Ashlin Joye, Nisha R. Acharya, Cindi Chen, Armin Hinterwirth, Thuy Doan
Twenty participants were recruited from a convenience sample from the Francis I. Proctor Foundation at the University of California, San Francisco (UCSF). This study was approved by the UCSF Institutional Review Board and adhered to the tenets of the Declaration of Helsinki. Participants had peripheral whole blood drawn into PAXgene blood RNA tubes (QIAGEN, Germantown, MD) and prepared and stored at −80 C according to manufacturer’s recommendations. Samples were deidentified and laboratory personnel handing samples and interpreting data were masked. Differential gene expression was performed to identify host transcriptome signatures.2 Briefly, analysis of sequenced data was made using a rapid computational pipeline developed in-house to classify host genes. Quality filtered RNA transcripts were aligned to the ENSEMBL CRCh38 human genome using STAR2. Genes were filtered to include only protein-coding genes that were expressed in at least 25% of the patients. Gene count data were analyzed with DESeq2.3 Differentially expressed genes with false discovery rate (FDR) <0.01 were considered as notable.
Emerging drug targets for triple-negative breast cancer: a guided tour of the preclinical landscape
Published in Expert Opinion on Therapeutic Targets, 2022
Xuemei Xie, Jangsoon Lee, Toshiaki Iwase, Megumi Kai, Naoto T Ueno
One of the key challenges is the intratumoral heterogenicity of TNBC, whose TME is a complex entity composed of different stromal and immune cells and soluble factors. In the past decades, next-generation technology platforms and transcriptomic and computational screening methods have become a new standard for discovering novel targets for cancer treatment. However, most transcriptome analysis techniques, including the use of gene expression microarrays, serial analysis of gene expression, massively parallel signature sequencing, RNA sequencing (RNA-seq), and the detection flux of RNA seq, are based on data from the bulk cell population of a given tissue. Thus, these analyses may overlook genes that are differentially expressed by individual cells in the tissue. Indeed, different responses to treatment have been observed in clinics due to the extensive intratumoral heterogenicity of TNBC, namely, the existence of different gene expression patterns in different clusters of the same tumor [189–191]. Ideally, then, efforts to identify promising therapeutic targets in TNBC should produce results at the single-cell or single-tumor-component level.