Biology subjects

Zak, D.

Publications and source records attributed to Zak, D..

4 recordsLinked to original sources

bioRxiv2024

Targeting Unique Ligand Binding Domain Structural Features Downregulates DKK1 in Y537S ESR1 Mutant Breast Cancer Cells

Resistance to endocrine therapies remains a major clinical hurdle in breast cancer. Mutations to estrogen receptor alpha (ER) arise after continued therapeutic pressure. Next generation selective estrogen receptor modulators and degraders/downregulators (SERMs and SERDs) show clinical efficacy, but responses are often non-durable. A tyrosine to serine point mutation at position 537 in the ER ligand binding domain (LBD) is among the most common and most pathogenic alteration in this setting. It enables endocrine therapy resistance by superceding intrinsic structural-energetic gatekeepers of ER hormone-dependence, it enhances metastatic burden by enabling neomorphic ER-dependent transcriptional programs, and it resists SERM and SERD inhibiton by reducing their binding affinities and abilities to antagonize transcriptional coregulator binding. However, a subset of SERMs and SERDs can achieve efficacy by adopting poses that force the mutation to engage in a new interaction that favors the therapeutic receptor antagonist conformation. We previously described a chemically unconventional SERM, T6I-29, that demonstrates significant anti-proliferative activities in Y537S ER breast cancer cells. Here, we use a comprehensive suite of structural-biochemical, in vitro, and in vivo approaches to better T6I-29s activities in breast cancer cells harboring Y537S ER. RNA sequencing in cells treated with T6I-29 reveals a neomorphic downregulation of DKK1, a secreted glycoprotein known to play oncogenic roles in other cancers. Importantly, we find that DKK1 is significantly enriched in ER+ breast cancer plasma compared to healthy controls. This study shows how new SERMs and SERDs can identify new therapeutic pathways in endocrine-resistant ER+ breast cancers.

cancer biology↗

bioRxiv2020

Temporal and spatial dynamics of peat microbiomes in drained and rewetted soils of three temperate peatlands

In the last decades, rewetting of drained peatlands is on the rise worldwide, to restore the significant carbon sink function. Rewetted peatlands differ substantially from their pristine counterparts and can, thus, be considered as novel ecosystems. Despite the increasing understanding of peat microbiomes, little is known about the seasonal dynamics and network interactions of the microbial communities in these novel ecosystems, especially in rewetted groundwater-fed peatlands, i.e. fens. Here, we investigated the seasonal dynamics in both prokaryotic and eukaryotic microbiomes in three common types of fens in Northern Germany, namely percolation fen, alder forest and coastal fen. The eukaryotic microbiomes, including fungi, protists and metazoa, showed significant changes of their community structures across the seasons in contrast to largely unaffected prokaryotic microbiomes. The co-occurrence network in the summer showed a distinct topology compared to networks in the other seasons, which was driven by the increased connections among protists, as well as between protists and the other microbial groups. Our results also indicated that the dynamics in eukaryotic microbiomes differed between fen types, specifically in terms of saprotrophs, arbuscular mycorrhiza and grazers of bacteria. Our study provides the insight that microbial eukaryotes mainly define the seasonal dynamics of microbiomes in rewetted fen peatlands. Accordingly, future research should unravel the importance of eukaryotes for biogeochemical processes, especially the under-characterized protists and metazoa, in these novel yet poorly understood ecosystems.

microbiology↗

bioRxiv2019

Rewetting of three drained peatlands drives congruent compositional changes in pro- and eukaryotic microbiomes through environmental filtering

Drained peatlands are significant sources of the greenhouse gas (GHG) carbon dioxide. Rewetting is a proven strategy to protect carbon stocks; however, it can lead to increased emissions of the potent GHG methane. The response to rewetting of soil microbiomes as drivers of these processes is poorly understood, as are biotic and abiotic factors that control community composition. We analyzed the pro- and eukaryotic microbiomes of three contrasting pairs of minerotrophic fens subject to decade-long drainage and subsequent rewetting. Also, abiotic soil properties including moisture, dissolved organic matter, methane fluxes and ecosystem respiration rates. The composition of the microbiomes was fen-type-specific, but all rewetted sites showed higher abundance of anaerobic taxa compared to drained sites. Based on multi-variate statistics and network analyses we identified soil moisture as major driver of community composition. Furthermore, salinity drove the separation between coastal and freshwater fen communities. Methanogens were more than tenfold more abundant in rewetted than in drained sites, while their abundance was lowest in the coastal fen, likely due to competition with sulfate reducers. The microbiome compositions were reflected in methane fluxes from the sites. Our results shed light on the factors that structure fen microbiomes via environmental filtering.

microbiology↗

bioRxiv2018

Multinomial modelling of TB/HIV co-infection yields a robust predictive signature and generates hypotheses about the HIV+TB+ disease state.

BackgroundCurrent diagnostics are inadequate to meet the challenges presented by coinfection with Mycobacterium tuberculosis (Mtb) and HIV, the leading cause of death for HIV-infected individuals. Improved characterisation of Mtb/HIV coinfection as a distinct disease state may lead to better identification and treatment of affected individuals.\n\nMethodsFour previously published TB and HIV co-infection related datasets were used to train and validate multinomial machine learning classifiers that simultaneously predict TB and HIV status. Classifier predictive performance was measured using leave-one-out cross validation on the training set and blind predictive performance on multiple test sets using area under the ROC curve (AUC) as the performance metric. Linear modelling of signature gene expression was applied to systematically classify genes as TB-only, HIV-only or combined TB/HIV.\n\nResultsThe optimal signature discovered was a single 10-gene random forest multinomial signature that robustly discriminates active tuberculosis (TB) from other non-TB disease states with improved performance compared with previously published signatures (AUC: 0. 87), and specifically discriminates active TB/HIV co-infection from all other conditions (AUC: 0.88). Signature genes exhibited a variety of transcriptional patterns including both TB-only and HIV-only response genes and genes with expression patterns driven by interactions between HIV and TB infection states, including the CD8+ T-cell receptor LAG3 and the apoptosis-related gene CERKL.\n\nConclusionsBy explicitly including distinct disease states within the machine learning analysis framework, we developed a compact and highly diagnostic signature that simultaneously discriminates multiple disease states associated with Mtb/HIV co-infection. Examination of the expression patterns of signature genes suggests mechanisms underlying the unique inflammatory conditions associated with active TB in the presence of HIV. In particular, we observed that disregulation of CD8+ effector T-cell and NK-cell associated genes may be an important feature of Mtb/HIV co-infection.

systems biology↗