Biology subjects

Ohno-Machado, L.

Publications and source records attributed to Ohno-Machado, L..

6 recordsLinked to original sources

bioRxiv2026

Variant-Specific Rewiring of Lysosomal Positioning by STARD9 Produces Opposite Cholesterol Trafficking Outcomes

Residual cardiovascular risk persists despite lipid-lowering therapy, independent of LDL and Lp(a), reflecting cholesterol dysregulation. We identify STARD9, a lysosomal cholesterol-sensing kinesin, as a causal gene for divergent familial dyslipidemias and premature atherosclerosis. Its START domain binds cholesterol to govern lysosomal positioning. Rare variants segregate with autosomal dominant hypercholesterolemia and premature coronary disease, while common variants associate with reduced HDL and elevated triglycerides. Both converge on lysosomal cholesterol sequestration, ER depletion, mTORC1 SREBP2 activation, impaired autophagy, and NF-kB inflammation, yet diverge through opposite lysosomal positioning: the rare variant disperses lysosomes peripherally, phenocopying START domain loss across cholesterol sequestration, lysosomal positioning, and TFEB activation, triggering LDLR degradation and CASM/TFEB-dependent efflux that preserves HDL, whereas the common variant causes perinuclear retention that suppresses TFEB dependent lipid-handling transcripts, yielding hypertriglyceridemia and low HDL. In Stard9-knockout mice, enterocyte cholesterol sequestration drives SREBP2 dependent NPC1L1 upregulation and apical localization, increasing intestinal cholesterol absorption. These findings position lysosomal cholesterol trafficking as a targetable node in statin-refractory cardiovascular disease.

genetics↗

bioRxiv2020

Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation

ABSTRACTGenotype imputation is a fundamental step in genomic data analysis such as GWAS, where missing variant genotypes are predicted using the existing genotypes of nearby ‘tag’ variants. Imputation greatly decreases the genotyping cost and provides high-quality estimates of common variant genotypes. As population panels increase, e.g., the TOPMED Project, genotype imputation is becoming more accurate, but it requires high computational power. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. To address this problem, we developed the first fully secure genotype imputation by utilizing ultra-fast homomorphic encryption (HE) techniques that can evaluate millions of imputation models in seconds. In HE-based methods, the genotype data is end-to-end encrypted, i.e., encrypted in transit, at rest, and, most importantly, in analysis, and can be decrypted only by the data owner. We compared secure imputation with three other state-of-the-art non-secure methods under different settings. We found that HE-based methods provide full genetic data security with comparable or slightly lower accuracy. In addition, HE-based methods have time and memory requirements that are comparable and even lower than the non-secure methods. We provide five different implementations and workflows that make use of three cutting-edge HE schemes (BFV, CKKS, TFHE) developed by the top contestants of the iDASH19 Genome Privacy Challenge. Our results provide strong evidence that HE-based methods can practically perform resource-intensive computations for high throughput genetic data analysis. In addition, the publicly available codebases provide a reference for the development of secure genomic data analysis methods.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

Discovering Data Access and Use Requirements Using the Data Tags Suite (DATS) Model

This article presents elements in the Data Tags Suite (DATS) metadata schema describing data access, data use conditions, and consent information. DATS is a product of the bioCADDIE Project, which created a data discovery index for searching across all types of biomedical data. The \"access and use\" metadata items in DATS are designed from the perspective of a researcher who wants to find and re-use existing data. Data reuse is often controlled to protect the privacy of subjects and patients. We focus on the impact of data protection procedures on data users. However, these procedures are part of a larger environment around patient privacy protection, and this article puts DATS metadata into the context of the administrative, legal, and technical systems used to protect confidential data.

bioinformatics↗

bioRxiv2017

Sharing genetic admixture and diversity of public biomedical datasets

Genetic ancestry and admixture are critical co-factors to study phenotype-genotype associations using cohorts of human subjects. Most publically available molecular datasets - genomes, exomes or transcriptomes - are however missing this information or only share self-reported ancestry. This represents a limitation to identify and re-purpose datasets to investigate the contribution of race and ethnicity to diseases and traits. we propose an analytical framework to enrich the meta-data from publically available cohorts with admixture information and a resulting diversity score at continental resolution, calculated directly from the data. We illustrate the utility and versatility of the framework using The Cancer Genome Atlas datasets indexed and searched through the DataMed Data Discovery Index. Data repositories or data contributors can use this framework to provide, as metadata, admixture for controlled access datasets, minimizing the work involved in requesting a dataset that may ultimately prove inadequate for a researchers purpose. With the increasingly global scale of human genetics research, research on disease risk and susceptibility would benefit greatly from the adequate estimation and sharing of admixture data following a framework such as the one presented.

genetics↗

bioRxiv2017

DATS: the data tag suite to enable discoverability of datasets

Todays science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMeds goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.

bioinformatics↗

bioRxiv2016

DataMed: Finding useful data across multiple biomedical data repositories

The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the NIH Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various datasets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature. DataMed supports Findability and Accessibility of datasets. These characteristics - along with Interoperability and Reusability - compose the four FAIR principles to facilitate knowledge discovery in todays big data-intensive science landscape.

bioinformatics↗