Biology subjects

Nikolov, M.

Publications and source records attributed to Nikolov, M..

6 recordsLinked to original sources

bioRxiv2026

Gothic Identity as Cultural Practice: Paleogenomic Evidence for Multi - Ethnic Assemblages Under Gothic Material Culture in Late Antique Bulgaria (4th - 6th centuries CE)

We report genome-wide ancient DNA from 38 individuals associated with Gothic-period mortuary contexts at two Bulgarian sites: the Aquae Calidae necropolis ([~]320 - 375 CE; n=23) and the Aul of Khan Omurtag (AKO; [~]350 - 489 CE; n=15). Although linked by closely similar Gothic-associated material culture and east - west Arian burial practices, the two assemblages are genetically distinct. Aquae Calidae individuals are modeled with predominantly Anatolian-related ancestry (50 - 85% in qpAdm), whereas AKO individuals are modeled with predominantly Wielbark/Chernyakhov-related northern ancestry (60 - 78%). Across the full assemblage, no single ancestry model fits all individuals, whose profiles range from nearly unadmixed Chernyakhov-like to predominantly Anatolian-related. DATES analysis places admixture between northern European and southern Balkan/Anatolian-related ancestry components approximately 11 - 13 generations before burial (point estimate [~]50 CE; 95% CI, 85 BCE - 183 CE; Z=5.26), raising the possibility that this mixture predated the earliest documented Gothic - Roman contacts ([~]170 CE). The convergence of dates across alternative southern proxies, together with reduced fit when Balkan and Anatolian sources are pooled into a single southern reference, favors admixture involving a pre-blended Balkan - Anatolian substrate over two temporally distinct southern admixture pulses. This signal was not recovered in the non-Gothic Roman-period Balkan populations tested as controls and is consistent with admixture in a trans-Danubian frontier setting, potentially including Roman Dacia after 106 CE. These findings support models in which Gothic affiliation in the Balkans operated as a cultural-political framework encompassing populations of diverse biological ancestry.

genomics↗

bioRxiv2024

AI-readiness for Biomedical Data: Bridge2AI Recommendations

Biomedical research is rapidly adopting artificial intelligence (AI). Yet the inherent complexity of biomedical data preparation requires implementing actionable, robust criteria for ethical and explainable AI (XAI) at the "pre-model" stage, encompassing data acquisition, detailed transformations, and ethical governance. Simple conformance to FAIR (Findable, Accessible, Interoperable, Reusable) Principles is insufficient. Here, we define criteria and practices for reliable AI-readiness of biomedical data, developed by the NIH Bridge to Artificial Intelligence (Bridge2AI) Standards Working Group across seven core dimensions of dataset AI-readiness: FAIRness, Provenance, Characterization, Ethics, Pre-model Explainability, Sustainability, and Computability. Conformance to these criteria provides a basis for pre-model scientific rigor and ethical integrity, mitigating downstream risks of bias and error prior to AI modeling. We apply and evaluate these standards across all four Bridge2AI flagship datasets, spanning functional genomics to clinical medicine, and encode them in machine-actionable metadata bound to the datasets. This framework sets a benchmark for preparing ethical, reusable datasets in biomedical AI and provides standardized methods for reliable pre-model data evaluation.

bioinformatics↗

bioRxiv2024

Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement

The Data Coordinating Center (DCC) of the Human Tumor Atlas Network (HTAN) has played a crucial role in enabling the broad sharing and effective utilization of HTAN data within the scienti[fi]c community. Data from the [fi]rst phase of HTAN are now available publicly. We describe the diverse datasets and modalities shared, multiple access routes to HTAN assay data and metadata, data standards, technical infrastructure and governance approaches, as well as our approach to sustained community engagement. HTAN data can be accessed via the HTAN Portal, explored in visualization tools--including CellxGene, Minerva, and cBioPortal--and analyzed in the cloud through the NCI Cancer Research Data Commons nodes. We have developed a streamlined infrastructure to ingest and disseminate data by leveraging the Synapse platform. Taken together, the HTAN DCCs approach demonstrates a successful model for coordinating, standardizing, and disseminating complex cancer research data via multiple resources in the cancer data ecosystem, offering valuable insights for similar consortia, and researchers looking to leverage HTAN data.

cancer biology↗

bioRxiv2021

A tripartite structure, the complex nuclear receptor element (cNRE), is a cis-regulatory module of viral origin required for atrial chamber preferential gene expression

Optimal cardiac function requires appropriate contractile proteins in each heart chamber. Atria require slow myosins to act as variable reservoirs, while ventricles demand fast myosin for swift pumping functions. Hence, myosin is under chamber-biased cis-regulatory control to achieve this functional distribution. Failure in proper regulation of myosin genes can lead to severe congenital heart dysfunction. The precise regulatory input leading to cardiac chamber-biased expression remains uncharted. To address this, we computationally and molecularly dissected the quail Slow Myosin Heavy Chain III (SMyHC III) promoter that drives specific gene expression to the atria to uncover the regulatory information leading to chamber expression and understand their evolutionary origins. We show that SMyHC III gene states are autonomously orchestrated by a complex nuclear receptor cis-regulatory element (cNRE), a 32- bp sequence with hexanucleotide binding repeats. Using in vivo transgenic assays in zebrafish and mouse models, we demonstrate that preferential atrial expression is achieved by the combinatorial regulatory input composed of atrial activation motifs and ventricular repression motifs. Through comparative genomics, we provide evidence that the cNRE emerged from an endogenous viral element, most likely through infection of an ancestral host germline. Our study reveals an evolutionary pathway to cardiac chamber-specific expression.

developmental biology↗

bioRxiv2020

Large-scale analysis of Drosophila core promoter function using synthetic promoters

The core promoter, the region immediately surrounding the transcription start site, plays a central role in setting metazoan gene expression levels, but how exactly it computes expression remains poorly understood. To dissect core promoter function, we carried out a comprehensive structure-function analysis to measure synthetic promoters activities, with and without an external stimulus (hormonal activation). By using robotics and a dual-luciferase reporter assay, we tested [~]3000 mutational variants representing 19 different Drosophila melanogaster promoter architectures. We explored the impact of different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, positioning, and flanking sequences. We observe strong effects of the mutations on activity, and a linear combination of the individual motif features can largely account for the combinatorial effects on core promoter activity. Our findings shed new light on the quantitative assessment of gene expression, a fundamental process in all metazoans.

systems biology↗

bioRxiv2020

Vector genetics, insecticide resistance and gene drives: an agent-based modeling approach to evaluate malaria transmission and elimination

Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness. Additionally, accelerated research and development of new tools that can be deployed alongside existing vector control strategies is key to eradicating malaria in the near future. Methods such as gene drives that aim to genetically modify large mosquito populations in the wild to either render them refractory to malaria or impair their reproduction may prove invaluable tools. Mathematical models of gene flow in populations, which is the transfer of genetic information from one population to another through migration, can offer invaluable insight into the behavior and potential impact of gene drives as well as the spread of insecticide resistance in the wild. Here, we present the first multi-locus, agent-based model of vector genetics that accounts for mutations and a many-to-many mapping cardinality of genotypes to phenotypes to investigate gene flow, and the propagation of gene drives in Anopheline populations. This model is embedded within a large scale individual-based model of malaria transmission representative of a high burden, high transmission setting characteristic of the Sahel. Results are presented for the selection of insecticide-resistant vectors and the spread of resistance through repeated deployment of insecticide treated nets (ITNs), in addition to scenarios where gene drives act in concert with existing vector control tools such as ITNs. The roles of seasonality, spatial distribution of vector habitat and feed sites, and existing vector control in propagating alleles that confer phenotypic traits via gene drives that result in reduced transmission are explored. The ability to model a spectrum of vector species with different genotypes and phenotypes in the context of malaria transmission allows us to test deployment strategies for existing interventions that reduce the deleterious effects of resistance and allows exploration of the impact of new tools being proposed or developed. Author summaryVector control interventions are essential to the success of global malaria control and elimination efforts but increasing insecticide resistance worldwide threatens to derail these efforts. Releasing genetically modified mosquitoes that use gene drives to pass on desired genes and their associated phenotypic traits to the entire population within a few generations has been proposed to address resistance and other issues such as transmission heterogeneity that can sustain malaria transmission indefinitely. While the ethics and safety of these methods are being debated, mathematical models offer an efficient way of predicting the behavior and estimating the efficacy of these interventions if deployed to specific regions facing challenges to reaching elimination. We have developed a detailed mathematical model of vector genetics where specific genomes code for physical attributes that influence transmission and are affected by the surrounding environment. This is the first model to incorporate an individual-based multi-locus genetic model into a detailed individual-based model of malaria transmission. This model opens the door to investigate a number of subtle but important questions such as the effects of small numbers of mosquitoes in a region sustaining malaria transmission during the low transmission season, and the success of gene drives in regions where extant vector control interventions could kill off gene drive mosquitoes before establishment. Here, we investigate the reduced efficacy of current vector control measures in the presence of insecticide resistance and evaluate the likelihood of achieving local malaria elimination using gene drive mosquitoes released into a high transmission setting alongside other vector control measures.

genetics↗