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Biology subjects

Esquivel, E.

Publications and source records attributed to Esquivel, E..

2 recordsLinked to original sources

Convergent genetic adaptation in human tumors developed under systemic hypoxia and in populations living at high altitudes

EPAS1HIF2 is the primary gene implicated in systemic hypoxia adaptation. Conversely, aberrantly activated EPAS1HIF2 acts as a tumor driver against which anti-tumor therapeutics are proven effective. We elucidated connections between adaptation to systemic hypoxia in high-altitude populations, such as Tibetans and Sherpas, and human tumors. Similar to the accelerated adaptability observed in high-altitude populations via genetic introgression, tumors from patients with hypoxia since birth exhibited impaired DNA repair and increased mutation burden. As in high-altitude dwellers, EPAS1HIF2 genetic variants were positively selected within sympathetic tumors developed under hypoxia, with a consistently high frequency of 90%. Bulk and single-cell RNA sequencing followed by in vitro studies have shown that hypoxia and EPAS1HIF2 gain-of-function tumor mutations induce COX4i2 expression and impair mitochondrial respiration, indicating that decreased cellular oxygen consumption may confer a proliferative advantage in hypoxia. Analyzing medical data from a patient cohort with hypoxia since birth who developed/did not develop tumors revealed tissue-specific and time-dependent tumorigenic effects of systemic hypoxia, which is limited to oxygen-sensitive and responsive cells, particularly during the postnatal period. This study supports connections between the EPAS1HIF2 genetic adaptation in human tumors developed under systemic hypoxia to populations living in high altitudes. The genetic adaptations in populations to different stressors can be explored further to understand tumorigenesis and tumor evolution.

cancer biology↗

Playbook Workflow Builder: Interactive Construction of Bioinformatics Workflows from a Network of Microservices

Many biomedical research projects produce large-scale datasets that may serve as resources for the research community for hypothesis generation, facilitating diverse use cases. Towards the goal of developing infrastructure to support the findability, accessibility, interoperability, and reusability (FAIR) of biomedical digital objects and maximally extracting knowledge from data, complex queries that span across data and tools from multiple resources are currently not easily possible. By utilizing existing FAIR application programming interfaces (APIs) that serve knowledge from many repositories and bioinformatics tools, different types of complex queries and workflows can be created by using these APIs together. The Playbook Workflow Builder (PWB) is a web-based platform that facilitates interactive construction of workflows by enabling users to utilize an ever-growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem. Via a user-friendly web-based user interface (UI), workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflows are provided in reports containing textual descriptions, as well as interactive and downloadable figures and tables. To demonstrate the ability of the PWB to generate meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases sieves novel targets for individual cancer patients using data from the GTEx, LINCS, Metabolomics, GlyGen, and the ExRNA Communication Consortium (ERCC) Common Fund (CF) Data Coordination Centers (DCCs). The workflows created with the PWB can be published and repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

bioinformatics↗