Biology subjects

Feller, A.

Publications and source records attributed to Feller, A..

5 recordsLinked to original sources

bioRxiv2026

Specification of embryonic shoot stem cells via a small RNA-driven morphogenic circuit

The specification of embryonic stem cells capable of self-renewing and differentiation into virtually any cell type, is one of the most consequential events in the development of a multicellular organism. Yet the mechanisms establishing embryonic stem cell fate remain poorly understood, particularly in monocotyledonous cereals. Using the classic mutant leafbladeless1-raggedseedling1, we show that the small RNA tasiARF acts as a primary epidermis-derived morphogenic signal that organizes shoot stem cell specification in the maize embryo. tasiARF restricts expression of the AUXIN RESPONSE FACTOR 3 (ARF3) transcription factor, which modulates cell wall mechanics and guides the differential localization of PIN-FORMED (PIN) auxin efflux carriers, establishing a localized auxin minimum permissive for stem cell fate. Interestingly, loss of this auxin minimum and the associated shoot stem cell defects in tasiARF-deficient embryos are buffered by natural variation at a quantitative trait locus (QTL) controlling expression of MICROTUBULE-ASSOCIATED PROTEIN 65-3 (MAP65-3), a critical factor determining cell division orientation, which reshapes auxin dynamics and restores stem cell specification, and is itself under tasiARF-ARF3 control. Thus, embryonic shoot stem cell specification in maize is governed by an intricate morphogenic circuit that couples small RNA-mediated positional information to a self-stabilizing network interdependently linking cell wall mechanics, auxin signaling, and cell division patterning. This mechanistic framework reveals the redeployment of an ancient small RNA pathway as a lineage-specific innovation to establish a conserved, stem cell-permissive low auxin environment within the divergent embryonic architecture of monocotyledonous cereals. More broadly, it identifies molecular entry points for the engineering of embryogenic competence and regeneration capacity for the improvement of cereal crops.

plant biology↗

bioRxiv2026

Combining Machine Learning and Directed Evolution for Optimization of a Monooxygenase

L-3,4-dihydroxyphenylalanine (L-Dopa) is an important pharmaceutical for the treatment of Parkinsons disease and a precursor to numerous catechol-containing compounds. The flavin-dependent monooxygenase HpaBC is a promising biocatalyst for microbial L-Dopa production but exhibits limited native activity toward L-tyrosine. Although structure-based machine learning (ML) models have become increasingly popular for protein engineering, relatively few studies have systematically compared their performance or evaluated their integration into iterative engineering workflows. Here, we benchmarked multiple ML models for their ability to predict activity enhancing mutations in HpaBC. Experimentally validated single mutants were used to seed combinatorial design with EVOLVEpro, generating progressively improved higher-order variants. We next evaluated how expanding the EVOLVEpro training set with directed evolution derived variants influenced combinatorial predictions and finally explored an expanded sequence space by allowing combinations of both machine learning derived and directed evolution derived mutations. This workflow produced HpaBC variants with substantially improved activity. Although incorporating directed evolution data substantially altered EVOLVEpros predicted mutational trajectories, both training strategies converged on variants with comparable activities, demonstrating that distinct regions of sequence space can yield similarly optimized enzymes. Together, these results provide a systematic comparison of zero-shot ML models and establish an iterative framework for integrating machine learning with directed evolution to accelerate enzyme engineering.

biochemistry↗

bioRxiv2026

Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations

Deep learning models have emerged as promising tools in protein engineering. In particular, they can be used to predict mutation fitness without the need for task-specific training, a process known as zero-shot prediction. However, the respective strengths and limitations of zero-shot predictions remain poorly understood. Here, we argue that commonly used large-scale benchmarks obscure important failure modes relevant to practical protein engineering, including an inability to pinpoint highly fit mutations or variants driving new-to-nature functions. Beyond these practical failures, we identify a fundamental limitation of zero-shot prediction: a generic fitness score cannot simultaneously optimize for distinct, competing engineering targets, meaning it is inherently disconnected from the phenotype of interest. Moreover, in a systematic comparison of a wide range of available models, we demonstrate that most models show comparable zero-shot performance, irrespective of model architecture and/or input modality (sequence vs. structure). Ultimately, we find that zero-shot predictions serve only as coarse filters separating fit mutations from deleterious ones, failing to reliably identify the mutations that would be most valuable in protein engineering.

bioengineering↗

bioRxiv2022

The START domain potentiates HD-ZIPIII transcriptional activity

HD-ZIPIII transcription factors (TFs) were repeatedly deployed over 725 million years of evolution to regulate central developmental innovations. The START domain of this pivotal class of developmental regulators was recognized over twenty years ago, but its putative ligands and functional contributions remain unknown. Here, we demonstrate that the START domain promotes HD-ZIPIII TF homodimerization and increases transcriptional potency. Effects on transcriptional output can be ported onto heterologous TFs, consistent with principles of evolution via domain capture. We also show the START domain binds several species of phospholipids, and that mutations in conserved residues predicted to affect either ligand binding, or its downstream readout, abolish HD-ZIPIII DNA-binding competence. Our data present a model in which the START domain potentiates transcriptional activity and uses ligand-induced conformational change to render HD-ZIPIII dimers competent to bind DNA. These findings resolve a long-standing mystery in plant development and highlight the flexible and diverse regulatory potential coded within this widely distributed evolutionary module.

plant biology↗

bioRxiv2019

Patients with different cancer types are stratified by CBC data

Searching for informative indices indicating cancer type and cause of the disease is of great importance. Here we tried to identify those indices from the data of complete blood analysis, only. We studied the inhomogeneity in the mutual distribution of a number of oncology patients with various types of tumors in the space of qualitative data provided by the complete blood count. The patients with oncology in hematology system were excluded. Ultimate goal is to reveal the relation between such inhomogeneity issues and the cause of a disease. We used the database on complete blood count comprising oncology patients with various causes of tumor development. The analysis has been carried out both by linear (K-means) and non-linear (elastic map technique) methods. No linear clustering has been found. On the contrary, elastic map technique yields stable clustering identifying not less than three clusters, in the set of patients. No relation of those clusters to sex or age of patients has been found. Four indices (namely, BAS, EOS, WBC and IG) exhibit no relation to the cluster structure, while all others do it. Thus, the patients are stratified according to their respond on the stress caused by cancer tumor. The data on complete blood count may be used for preliminary diagnostics of a tumor and its cause, for oncology patients. This type of analysis is cheap, standard and available at any medical organization.

cancer biology↗