Biology subjects

Pavicic, M.

Publications and source records attributed to Pavicic, M..

7 recordsLinked to original sources

bioRxiv2025

Telomere-to-telomere assemblies reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by helitrons

BackgroundThe model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases (KCS), which has been difficult to confirm based on short-read assemblies. New long-read sequencing provides opportunities to develop telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed in traditional analyses. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. ResultsOur analysis of 78 telomere-to-telomere long-read haplotypes identified more than twice as many KCS genes as previously reported, along with numerous intragenic non-synonymous substitutions. Random forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a helitron transposon. A phylogenetic analysis indicates they are the evolutionary mechanism for generating KCS tandem arrays. ConclusionsLong-read sequencing and telomere-to-telomere assembles revealed large-effect loci critical to genetic studies that are unattainable from short-reads. These approaches also have the potential to reveal novel insights into genome structure and function, such as the helitrons identified here. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

genomics↗

bioRxiv2025

Population and adaptation history of 739 Thlaspi arvense natural accessions

Pennycress (Thlaspi arvense) is a promising intermediate oilseed crop, producing oil suitable for conversion to biofuels--including aviation fuels. While domestication efforts are ongoing, a deeper understanding of the genetic architecture of traits is crucial for informing future breeding efforts. Here, we conducted the largest genomic and phenotypic survey of pennycress to date, analyzing 739 accessions collected across four continents. Leveraging whole-genome sequencing and field-collected phenotypes, we characterized the standing genetic variation underlying key agronomic traits and climate resilience. Our findings revealed multiple independent migration events to North America, with substantial genetic admixture. We identified homologs of Arabidopsis thaliana flowering-time genes that contribute to adaptation and demonstrated the agronomic benefits of winter-type pennycress. Furthermore, through multi-season field trials, we identified a genomic region containing a cluster of mTERF genes strongly associated with green canopy coverage, a critical trait for biomass retention and yield stability. These insights provide a genomic roadmap for accelerating pennycress domestication and improving its resilience to climate variability.

genomics↗

bioRxiv2024

MENTOR: Multiplex Embedding of Networks for Team-Based Omics Research

While the proliferation of data-driven omics technologies has continued to accelerate, methods of identifying relationships among large-scale changes from omics experiments have stagnated. It is therefore imperative to develop methods that can identify key mechanisms among one or more omics experiments in order to advance biological discovery. To solve this problem, here we describe the network-based algorithm MENTOR - Multiplex Embedding of Networks for Team-Based Omics Research. We demonstrate MENTORs utility as a supervised learning approach to successfully partition a gene set containing multiple ontological functions into their respective functions. Subsequently, we used MENTOR as an unsupervised learning approach to identify important biological functions pertaining to the host genetic architectures in Populus trichocarpa associated with microbial abundance of multiple taxa. Moreover, as open source software designed with scientific teams in mind, we demonstrate the ability to use the output of MENTOR to facilitate distributed interpretation of omics experiments.

bioinformatics↗

bioRxiv2024

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Leveraging the use of multiplex multi-omic networks, key insights into genetic and epigenetic mechanisms supporting biofuel production have been uncovered. Here, we introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelisation on high performance cluster systems, which enables high throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

bioinformatics↗

bioRxiv2023

Transcriptome analysis reveals novel regulators of the Scots pine stilbene pathway

Stilbenes are developmentally induced metabolites in Scots pine heartwood where they have important role in protecting wood from decaying fungi. The stilbene pathway is also stress inducible, and ultraviolet (UV)-C radiation was among the first discovered artificial stress activators of the pathway. Many conditions that activate the pathway are known, but the specific transcriptional regulators and the biosynthetic enzyme responsible for activating the stilbene precursor cinnamate in the pathway are unknown. Here we describe the first large-scale transcriptomic analysis of pine needles in response to UV-C exposure and treatment with translational inhibitor cycloheximide, both activating the transcription of stilbene pathway genes, to uncover the candidates for the transcriptional regulation of the pathway and the cinnamate activating CoA ligase. We show that the regulation of pine stilbene pathway has shared features with grapevine stilbene pathway. In both species, the stilbene pathway was transcriptionally activated after UV-C treatment, protein phosphatase inhibitors and plant hormones ethylene and jasmonate. The pine stilbene synthase promoter retains its inducibility when transformed in plants that normally do not synthesize stilbenes. Pine stilbene synthase promoter was able to activate the expression of a reporter gene in Arabidopsis in response to UV-C exposure and phosphatase inhibitors, but not as response to plant hormone treatment. This indicates that stilbene synthase gene regulation occurs both via ancient stress-response pathway(s) but also via species specific regulators. With transcriptomic approach we identified candidate enzymes for cinnamate acting CoA ligase and transcription factors regulating the pathway.

plant biology↗

bioRxiv2021

The emergence of highly fit SARS-CoV-2 variants accelerated by recombination

The SARS-CoV-2 pandemic recently entered an alarming new phase with the emergence of the variants of concern (VOC) and understanding their biology is paramount to predicting future ones. Current efforts mainly focus on mutations in the spike glycoprotein (S), but changes in other regions of the viral proteome are likely key. We analyzed more than 900,000 SARS-CoV-2 genomes with a computational systems biology approach including a haplotype network and protein structural analyses to reveal lineage-defining mutations and their critical functional attributes. Our results indicate that increased transmission is promoted by epistasis, i.e., combinations of mutations in S and other viral proteins. Mutations in the non-S proteins involve immune-antagonism and replication performance, suggesting convergent evolution. Furthermore, adaptive mutations appear in geographically disparate locations, suggesting that either independent, repeat mutation events or recombination among different strains are generating VOC. We demonstrate that recombination is a stronger hypothesis, and may be accelerating the emergence of VOC by bringing together cooperative mutations. This emphasizes the importance of a global response to stop the COVID-19 pandemic.

evolutionary biology↗

bioRxiv2020

Confronting the COVID-19 Pandemic with Systems Biology

Using a Systems Biology approach, we integrated genomic, transcriptomic, proteomic, and molecular structure information to provide a holistic understanding of the COVID-19 pandemic. The expression data analysis of the Renin Angiotensin System indicates mild nasal, oral or throat infections are likely and that the gastrointestinal tissues are a common primary target of SARS-CoV-2. Extreme symptoms in the lower respiratory system likely result from a secondary-infection possibly by a comorbidity-driven upregulation of ACE2 in the lung. The remarkable differences in expression of other RAS elements, the elimination of macrophages and the activation of cytokines in COVID-19 bronchoalveolar samples suggest that a functional immune deficiency is a critical outcome of COVID-19. We posit that using a non-respiratory system as a major pathway of infection is likely determining the unprecedented global spread of this coronavirus. One Sentence SummaryA Systems Approach Indicates Non-respiratory Pathways of Infection as Key for the COVID-19 Pandemic

systems biology↗