Biology subjects

Bornelov, S.

Publications and source records attributed to Bornelov, S..

4 recordsLinked to original sources

bioRxiv2023

Unistrand piRNA clusters are an evolutionary conserved mechanism to suppress endogenous retroviruses across the Drosophila genus

The PIWI-interacting RNA (piRNA) pathway prevents endogenous genomic parasites, transposable elements, from damaging the genetic material of animal gonadal cells. Specific regions in the genome, called piRNA clusters, are thought to define each species piRNA repertoire and therefore its capacity to recognize and silence specific transposon families. The unistrand cluster flamenco (flam) is essential in the somatic compartment of the Drosophila ovary to restrict Gypsy-family transposons from infecting the neighbouring germ cells. Disruption of flam results in transposon derepression and sterility, yet it remains unknown whether this silencing mechanism is present more widely. Here, we systematically characterised 119 Drosophila species and identify five additional flam-like clusters separated by up to 45 million years. Small RNA-sequencing validated these as bona-fide unistrand piRNA clusters expressed in somatic cells of the ovary, where they selectively target transposons of the Gypsy family. Together, our study provides compelling evidence of a widely conserved transposon silencing mechanism that co-evolved with virus-like Gypsy-family transposons.

molecular biology↗

bioRxiv2022

Creating a 'Timeline' of ductal carcinoma in situ to identify processes and biomarkers for progression towards invasive ductal carcinoma

Ductal carcinoma in situ (DCIS) is considered a non-invasive precursor to breast cancer, and although associated with an increased risk of developing invasive disease, many women with DCIS will never progress beyond their in situ diagnosis. The path from normal duct to invasive disease is not well understood, and efforts to do so are hampered by the substantial heterogeneity that exists between patients and even within patients. Using gene expression analysis, we have generated a Timeline of disease progression, utilising the variability within patients and combining >2,000 individually micro-dissected ductal lesions from 145 patients into one continuous trajectory. Using this Timeline we show there is a progressive loss in basal layer integrity, coupled with two epithelial to mesenchymal transitions (EMT), one early in the timeline and a second just prior to cells leaving the duct. We identify early processes and potential biomarkers, including CAMK2N1, MNX1, ADCY5, HOXC11 and ANKRD22, whose reduced expression is associated with the progression of DCIS to invasive breast cancer.

cancer biology↗

bioRxiv2021

Mapping the biogenesis of forward programmed megakaryocytes from induced pluripotent stem cells

Platelet deficiency, known as thrombocytopenia, can cause haemorrhage and is treated with platelet transfusions. We developed a system for the production of platelet precursor cells, megakaryocytes, from pluripotent stem cells. These cultures can be maintained for >100 days, implying culture renewal by megakaryocyte progenitors (MKPs). However, it is unclear whether the MKP state in vitro mirrors the state in vivo, and MKPs cannot be purified using conventional surface markers. We performed single cell RNA sequencing throughout in vitro differentiation and mapped each state to its equivalent in vivo. This enabled the identification of 5 surface markers which reproducibly purify MKPs, allowing us an insight into their transcriptional and epigenetic profiles. Finally, we performed culture optimisation, increasing MKP production. Altogether, this study has mapped parallels between the MKP states in vivo and in vitro and allowed the purification of MKPs, accelerating the progress of in vitro-derived transfusion products towards the clinic.

developmental biology↗

bioRxiv2019

R.ROSETTA: a package for analysis of rule-based classification models

MotivationFor machine learning to matter beyond intellectual curiosity, the models developed therefrom must be adopted within the greater scientific community. In this study, we developed an interpretable machine learning framework that allows identification of semantics from various datatypes. Our package can analyze and illuminate co-predictive mechanisms reflecting biological processes. ResultsWe present R.ROSETTA, an R package for building and analyzing interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. Investigating case-control studies of autism, we showed that our tool provided hypotheses for potential interdependencies among features that discerned phenotype classes. These interdependencies regarded neurodevelopmental and autism-related genes. Although our sample application of R.ROSETTA was used for transcriptomic data analysis, R.ROSETTA works perfectly with any decision-related omics data. AvailabilityThe R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA. Contactmateusz.garbulowski@icm.uu.se (Mateusz Garbulowski), jan.komorowski@icm.uu.se (Jan Komorowski)

bioinformatics↗