Biology subjects

Van Criekinge, W.

Publications and source records attributed to Van Criekinge, W..

5 recordsLinked to original sources

bioRxiv2026

GRAFT: phylogenetic signal in patent applications across the tree of life

Whether closely related species are repurposed for similar biotechnologies -- a phylogenetic signal in human technological interest -- has lacked a tractable test at scale. We built GRAFT (Graph of Relatedness, Applications, Families and Taxonomy), a Neo4j knowledge graph linking the Open Tree of Life synthetic taxonomy (4.53 x 106 taxa)1 to multilingual common names2,3 and to a Google Patents BigQuery patent layer from a single 257 GB SQL scan, recovering 22,876 species in 759,182 patents with all CPC and IPC class definitions resolved. Treating each species CPC-subclass profile as a binary application vector, we tested the correlation between pairwise topological phylogenetic distance and pairwise Jaccard distance of patent profiles by Mantel test6 (999 permutations, n = 9,944 species at [≥]5 patents, 49,436,596 pairs). The global correlation was significant (Pearson r = +0.188, one-sided p = 0.001), with Bonferroni-significant phylogenetic signal in every close-distance bin from sister-species through within-class. The signal is not an artefact of the unweighted topology: re-expressing phylogenetic distance as time-calibrated divergence from the TimeTree of Life confirms Bonferroni-significant signal in every bin out to [~]500 Myr of divergence. The same graph supports a predictive query that returns sister-species bioprospecting candidates for any application: ten Angelica congeners are unflagged for medicinal preparations while A. sinensis (Chinese angelica) already carries 86,814 such edges. GRAFT is an openly extensible scaffold linking phylogeny, ecology and the global IP record.

evolutionary biology↗

bioRxiv2023

HIV-PULSE: A long-read sequencing assay for high-throughput near full-length HIV-1 proviral genome characterization

A deep understanding of the composition of the HIV-1 reservoir is necessary for the development of targeted therapies and the evaluation of curative efforts. However, current near full-length (NFL) HIV-1 proviral genome sequencing assays are based on labor-intensive and costly principles of repeated PCRs at limiting dilution, restricting their scalability. To address this, we developed a high-throughput, long-read sequencing assay called HIV-PULSE (HIV Proviral UMI-mediated Long-read Sequencing). This assay uses unique molecular identifiers (UMIs) to tag individual HIV-1 genomes, allowing for the omission of the limiting dilution step and enabling long-range PCR amplification of many NFL genomes in a single PCR reaction, while simultaneously overcoming poor single-read accuracy. We optimized the assay using HIV-infected cell lines and then applied it to blood samples from 18 individuals living with HIV on antiretroviral therapy, yielding a total of 1,308 distinct HIV-1 genomes. Benchmarking against the widely applied Full-Length Individual Proviral Sequencing assay revealed similar sensitivity (11% vs 18%) and overall good concordance, though at a significantly higher throughput. In conclusion, HIV-PULSE is a cost-efficient and scalable assay that allows for the characterization of the HIV-1 proviral landscape, making it an attractive method to study the HIV-1 reservoir composition and dynamics.

molecular biology↗

bioRxiv2022

MAGE enables population level RNAseq driven genotyping and (differential) allelic divergence detection in healthy kidney and carcinoma

Decreasing sequencing costs have instigated large-scale RNAseq experiments, yet genetic polymorphisms in such data remain poorly exploited. Currently, allele-specific expression (ASE) studies focus almost exclusively on genetic variants explaining expression differences (cis-eQTLs), largely ignoring other ASE effects. The latter are typically associated with higher variance in expression of both copies of a gene, here called Allelic Divergence (AD). We therefore developed an RNAseq-driven population-level beta-binomial mixture model for (differential) AD detection. The model simultaneously enables RNAseq-driven genotyping, which outperforms alternative RNA genotyping methods when applied on healthy kidney data from The Cancer Genome Atlas. Moreover, we identify well-known non-cis-eQTL ASE, e.g. random monoallelic expression of HLA and immunoglobulin genes in healthy kidney, as well as allele-specific aberrations in clear cell kidney carcinoma, including long-range epigenetic silencing of protocadherins, copy-number alterations, and loss of imprinting. These methods are available as the Modeller of Allelic Gene Expression (MAGE) tool suite: https://biobix.github.io/MAGE/.

bioinformatics↗

bioRxiv2020

Differential methylation as a mediator of COVID-19 susceptibility

The COVID-19 outbreak shows a huge variation in prevalence and mortality on geographical level but also within populations1. The ACE2 gene, identified as the SARS-CoV2 receptor, has been shown to facilitate the viral invasion and people with higher ACE2 expression generally are more severely affected2, 3. As there is a lot of variability in ACE2 expression between individuals we hypothesized that differential DNA methylation profiles could be (one of) the confounding factors explaining this variability. Here we show that epigenetic profiling of host tissue, especially in the ACE2 promoter region and its homologue ACE1, may be important risk factors for COVID-19. Our results propose that variable methylation can explain (part of) the differential susceptibility, symptom severity and death rate for COVID-19. Our findings are a promising starting point to further evaluate the potential of ACE1/2 methylation and other candidates as a predictor for clinical outcome upon SARS-CoV2 infection.

genomics↗

bioRxiv2018

A Comprehensive Overview Of Genomic Imprinting In Breast & Its Deregulation In Cancer

Genomic imprinting, the parent-of-origin specific monoallelic expression of genes, plays an important role in growth and development. Loss of imprinting of individual genes has been found in varying cancers, yet data-analytical challenges have impeded systematic studies so far. We developed a mixture distribution model to detect monoallelically expressed loci in a genome-wide manner without the need for genotyping data, and applied the methodology on TCGA breast tissue RNA-seq data. We identified 35 putatively imprinted genes in healthy breast. In breast cancer however, HM13 was featured by significant loss of imprinting and expression upregulation, which could be linked to DNA demethylation. Other imprinted genes (25 out of 35) demonstrated consistent expression downregulation in breast cancer, which often correlated with loss of imprinting. A breast imprinted gene network, deregulated in cancer, might hence be present. In summary, our novel methodology highlights the massive deregulation of imprinting in breast cancer.

cancer biology↗