Biology subjects

Aksianiuk, V.

Publications and source records attributed to Aksianiuk, V..

4 recordsLinked to original sources

bioRxiv2026

Proximity labeling at H3K9me3 reveals VRK-1 regulate global chromatin distribution in C. elegans

Heterochromatin marked by histone H3 lysine 9 di- or trimethylation (H3K9me2/3) underpins transcriptional silencing and nuclear organization, yet its full complement of associated proteins remains incompletely defined. Here, we apply ChromID proximity labelling with the mouse HP1{beta} chromodomains to map the H3K9me3-proximal proteome in Caenorhabditis elegans, recovering known heterochromatin factors alongside previously uncharacterized candidates. We pursued one such candidate, the vaccinia-related kinase VRK-1, because of its established but poorly understood links to chromatin organization. Intriguingly, VRK-1 dynamically relocates from a broad nuclear distribution to the nuclear periphery upon azide or heat stress. Following these stresses, bulk chromatin exhibits similarly increased peripheral enrichment and apparent compaction, as assessed by radial fluorescence profiles. Although VRK-1 is not necessary for stress-induced chromatin reorganization, decompaction and repositioning of chromatin away from the nuclear envelope during recovery requires VRK-1. VRK-1 depletion leads to persistent perinuclear chromatin retention and compromises post-stress survival. Furthermore, loss of VRK-1 catalytic activity results in over-retention of chromatin at the nuclear periphery under normal growth conditions; this phenotype can be reversed by depletion of a key VRK-1 substrate at the nuclear envelope BAF-1. Our findings identify VRK-1 as a key regulator that controls the interaction of chromatin with the nuclear lamina through regulation of BAF-1.

cell biology↗

bioRxiv2026

Physiological architecture and evolutionary origins of cellular adaptability

The capacity to adapt to complex environments ( adaptability) is a defining property of cells. Yet, its relationship across single-cell physiology, population-level responses, and evolutionary timescales remains unclear. We performed single-cell transcriptional profiling of budding yeast across 20 complex environments before and after long-term selection for increased osmotolerance. In ancestral populations, transcriptional responses organized into a reproducible hierarchy where adaptation to certain cues took precedence over others. This hierarchy mechanistically originated within individual cells through contingent regulation of translation initiation. Evolution for >3,000 generations under osmotic stress increased osmotolerance while reordering this hierarchy, selectively deprioritizing osmotic stress as an organizing axis of adaptation. The derived strain exhibited impaired integration of stress responses, defective translational coordination, and reduced fitness outside the selected condition. Together, these findings illustrate that cellular adaptability is an evolved architecture whose form is set by the history of selection. One Sentence SummaryCells adapt to complex environments via a prioritization hierarchy; this hierarchy is shaped by the frequency and magnitude of environmental fluctuations in their evolutionary history.

cell biology↗

bioRxiv2024

Statistical design of a synthetic microbiome that clears a multi-drug resistant gut pathogen

Engineering functional microbiomes is challenging due to complex interactions between bacteria and their environments1-6. Using a set of 848 gut commensal strains and clearance of multi-drug resistant Klebsiella pneumoniae (Kp-MH258) as a target function, we engineered a functional 15-member synthetic microbiome--SynCom15--through a statistical approach agnostic to strain phenotype, mechanism of action, bacterial interactions, or composition of natural microbiomes. Our approach involved designing, building, and testing 96 metagenomically diverse consortia, learning a generative model using community strain presence/absence as input, and distilling model constraints through statistical inference. SynCom15 cleared Kp-MH258 across in vitro, ex vivo, and in vivo environments, matching the efficacy of a fecal microbiome transplant in a clinically relevant murine model of infection. The mechanism of suppression by SynCom15 was related to fatty acid production coupled with environmental acidification. SynCom15 also suppressed other pathogens--Clostridioides difficile, Escherichia coli, and other K. pneumoniae strains--but through different mechanisms. Sensitivity analysis revealed models trained on strain presence/absence captured the statistical structure of pathogen suppression, illustrating that community representation was key to our approach succeeding. Our framework, Constraint Distillation, could be a general and efficient strategy for building emergent complex systems, offering a path towards synthetic ecology more broadly.

synthetic biology↗

bioRxiv2021

Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Cellular phenotypes emerge from a hierarchy of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be extracted from the statistical pattern of proteome variation as measured across thousands of bacteria and that these hierarchies reflect the emergence of complex bacterial phenotypes. We describe the mathematics underlying our statistical approach and validate our results through gene-set enrichment analysis and comparison to existing experimentally-derived hierarchical databases. We demonstrate the biological utility of our unbiased hierarchical models by creating a model of motility in Pseudomonas aeruginosa and using it to discover a previously unappreciated genetic effector of twitch-based motility. Overall, our approach, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), predicts hierarchies of protein interaction networks describing emergent biological function using only the statistical pattern of bacterial proteome variation.

systems biology↗