Biology subjects

Przytycka, T.

Publications and source records attributed to Przytycka, T..

3 recordsLinked to original sources

bioRxiv2024

Diverse somatic Transformer and sex chromosome karyotype pathways regulate gene expression in Drosophila gonad development

The somatic sex determination gene transformer (tra) is required for the highly sexually dimorphic development of most somatic cells, including those of the gonads. In addition, somatic tra is required for the germline development even though it is not required for sex determination within germ cells. Germ cell autonomous gene expression is also necessary for their sex determination. To understand the interplay between these signals, we compared the phenotype and gene expression of larval wild-type gonads and the sex-transformed tra gonads. XX larval ovaries transformed into testes were dramatically smaller than wild-type, with significant reductions in germ cell number, likely due to altered geometry of the stem cell niche. Additionally, there was a defect in progression into spermatocyte stages. XY larval testes transformed into ovaries had excessive germ cells, possibly due to the earlier onset of cell division. We suggest that germ cells are neither fully female nor male following somatic sex transformation, with certain pathways characteristic of each sex expressed in tra mutants. We found multiple patterns of somatic and germline gene expression control exclusively due to tra, exclusively due to sex chromosome karyotype, but usually due to a combination of these factors showing tra and sex chromosome karyotype pathways regulate gene expression during Drosophila gonad development.

developmental biology↗

bioRxiv2024

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

genomics↗

bioRxiv2017

AptaBlocks: Accelerating the Design of RNA-based Drug Delivery Systems

Synthetic RNA molecules are increasingly used to alter cellular functions. These successful applications indicate that RNA-based therapeutics might be able to target currently undruggable genes. However, to achieve this promise, an effective method for delivering therapeutic RNAs into specific cells is required. Recently, RNA aptamers emerged as promising delivery agents due to their ability of binding specific cell receptors. Crucially, these aptamers can frequently be internalized into the cells expressing these receptors on their surfaces. This property is leveraged in aptamer based drug delivery systems by combining such receptor-specific aptamers with a therapeutic \"cargo\" such that the aptamer facilitates the internalization of the cargo into the cell. The advancement of this technology however is contingent on an efficient method to produce stable molecular complexes that include specific aptamers and cargoes. A recently proposed experimental procedure for obtaining such complexes relies on conjugating the aptamer and the cargo with complementary RNA strands so that when such modified molecules are incubated together, the complementary RNA strands hybridize to form a double-stranded \"sticky bridge\" connecting the aptamer with its cargo. However, designing appropriate sticky bridge sequences guaranteeing the formation and stability of the complex while simultaneously not interfering with the aptamer or the cargo as well as not causing spurious aggregation of the molecules during incubation has proven highly challenging. To fill this gap, we developed AptaBlocks, a computational method to design sticky bridges to connect RNA-based molecules (blocks). AptaBlocks relies on a biophysically inspired theoretical model capturing the complex objectives of the design and yet is simple enough to allow for efficient parameter estimation. Given this model, the sticky bridge sequence is optimized using a Monte Carlo algorithm based on heat-bath transitions. The effectiveness of the algorithm has been verified computationally and experimentally. AptaBlocks can be used in variety of experimental settings and its preliminary version has already been leveraged to design an aptamer based delivery system for a cytotoxic drug targeting Pancreatic ductal adenocarcinoma cells. It is thus expected that AptaBlocks will play a substantial role in accelerating RNA-based drug delivery design.\n\nAptaBlocks is available at https://github.com/wyjhxq/AptaBlocks.

bioinformatics↗