Biology subjects

Fritz, A.

Publications and source records attributed to Fritz, A..

18 recordsLinked to original sources

bioRxiv2026

Flywheel Genomics: Simultaneous trait discovery and genetic gain in plant breeding

Genomic mapping has yielded extensive catalogs of quantitative trait loci underlying agronomic traits, yet translating these discoveries into breeding gains remains inefficient. Here, we introduce Flywheel Genomics, a framework that integrates trait discovery directly within rapid cycling breeding populations. Using empirical data from a smallholder-oriented sorghum breeding program, we demonstrate that recurrent intermating and selection maintain genetic diversity, effective population size, and recombination while reducing confounding from plant height and maturity. Within this population, we resolve loci underlying simple adaptive and complex environmentally responsive traits and generate large segregating populations for mapping and near-isogenic lines for locus validation. We further demonstrate applicability in a public wheat breeding program, where known agronomic loci were readily detected. Simulations show that rapid cycling better preserves the population genetic properties required for Flywheel Genomics than conventional pure line development. By integrating discovery with improvement, Flywheel Genomics reframes breeding programs as engines of both crop improvement and genetic insight.

genetics↗

bioRxiv2026

lncRNA MANCR isoforms selectively mediate multiple levels of epigenomic and P53-responsive transcriptional control in triple negative breast cancer

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and are frequently dysregulated in cancer. The mitotically associated lncRNA MANCR is highly expressed in aggressive cancers and contributes to genomic instability in triple-negative breast cancer (TNBC), but the molecular mechanisms underlying its activity remain poorly defined. Here we integrate computational and experimental approaches to examine the structure and regulatory interactions of MANCR isoforms. Analysis of transcriptomic datasets revealed tumor-type-specific expression patterns for seven MANCR isoforms in breast cancer cell lines. Computational prediction of RNA secondary structures identified conserved structural features across isoforms, suggesting potential functional specialization. We identify p53 as a MANCR-interacting protein through computational docking and RNA immunoprecipitation sequencing (RIP-seq) and demonstrate that MANCR depletion reduces p53-dependent transcriptional activity. Chromatin isolation by RNA purification sequencing (ChIRP-seq) revealed 1, 250 genomic regions associated with MANCR, including enrichment of p53 consensus motifs and GC-rich sequence elements. Motif analysis further identified candidate sequence features associated with MANCR-occupied chromatin regions. Computational prediction of RNA-miRNA interactions identified multiple potential miRNA binding sites across MANCR isoforms, including miR-6756-5p, which targets the androgen receptor (AR). Consistent with this prediction, AR expression decreased following MANCR knockdown in TNBC cells. Together, these results suggest that MANCR isoforms may contribute to transcriptional regulation in TNBC through interactions with chromatin, p53 signaling pathways, and potential miRNA regulatory networks. One Sentence SummaryMitotically-associated lncRNA (MANCR) is prevalent in aggressive cancers interacting with DNA, P53, and miRNAs, to mediate multiple levels of epigenetic transcriptional control in triple negative breast cancer.

cancer biology↗

bioRxiv2026

Acute degron-mediated RUNX1 loss reprograms enhancer activity to epigenetically drive epithelial destabilization and initiate cancer hallmarks

The RUNX1 transcription factor mediates cell-type specific gene expression. RUNX1 suppression and perturbations are recurrently associated with breast tumor initiation and progression. However, the mechanisms governing the dual roles of RUNX1 in sustaining the mammary epithelial phenotype while epigenetically suppressing initiation of cancer-compromised gene expression are poorly understood. To address this, we used the power of degron-mediated acute, selective, and complete RUNX1 ablation in human mammary epithelial cells. RUNX1 mediates promoter and distal enhancer-driven expression of a gene cohort. Dynamic epigenomic responsiveness upon RUNX1 ablation reveals a rapid and selective decrease in chromatin accessibility and H3K27ac at RUNX1-bound enhancers, but not promoters. While differentially initiated and expressed genes contacted by RUNX1-bound enhancers are enriched in pathways involved in epithelial maintenance and stemness, genes with RUNX1-promoter occupancy support DNA damage responsiveness. Modified cell morphology, metabolic control, increased breast cancer stemness, plasticity, anchorage-independent survival, chemoresistance, and perturbed DNA damage reactivity are observed upon RUNX1 ablation. Together, these findings define RUNX1 as an epigenetic tumor suppressor that maintains epithelial cell state by preserving enhancer activity and preventing gene expression associated with hallmarks of cancer.

cancer biology↗

bioRxiv2026

DisGeneFormer: Precise Disease Gene Prioritization by Integrating Local and Global Graph Attention

Identifying genes associated with human diseases is essential for effective diagnosis and treatment. Experimentally identifying disease-causing genes is time-consuming and expensive. Computational prioritization methods aim to streamline this process by ranking genes based on their likelihood of association with a given disease. However, existing methods often report long ranked lists consisting of thousands of potential disease genes, often containing a high number of false positives. This fails to meet the practical needs of clinicians who require shorter, more precise candidate lists. To address this problem, we introduce DisGeneFormer (DGF), an end-to-end disease-gene prioritization pipeline. Our approach is based on two distinct graph representations, modeling gene and disease relationships, respectively. Each graph is first processed separately by graph attention and then jointly by a transformer module to combine within-graph and cross-graph knowledge through local and global attention. We propose an evaluation pipeline based on the precision of a top K ranked gene list, with K set to clinically feasible values between 5 and 50, relying solely on experimentally verified associations as ground truth. Our evaluation demonstrates that DGF substantially outperforms existing methods. We additionally assessed the influence of the negative data sampling strategy as well as analyses of the effect of graph topology and features on the performance of our model.

bioinformatics↗

bioRxiv2025

Genome reorganization and its functional impact during breast cancer progression

Cancer progression involves extensive alterations in epigenetic and gene expression programs, but the accompanying changes in higher-order genome organization remain less well understood. Using high-resolution Micro-C mapping in the MCF10 cell model of breast cancer, we profiled chromatin compartments, topologically associated domains, and chromatin loops. We find large-scale compartmental shifts occur predominantly in early stages of cancer development, with more fine-scale structural changes in TADs and looping accumulating during the later transition to metastasis. Relating these chromatin features to gene expression and enhancer-associated histone marks revealed that many differentially expressed genes are physically connected to distal regulatory elements. While enhancer-promoter contact frequency and distal enhancer activity correlated with gene expression, strong changes in chromatin looping were relatively infrequent during progression, suggesting that alterations in chromatin contacts are not globally necessary but may facilitate gene regulation at a subset of genes. These results elucidate the connection between gene regulation and genome remodeling in a cell-based cancer progression model.

genomics↗

bioRxiv2024

Genetic modulation of yield and phenotypic plasticity of yield in winter wheat

For winter wheat in the US Central Great Plains, phenotypic plasticity of yield is agronomically adaptive, i.e., genotypes with higher plasticity have higher yield in high yielding environments with no tradeoff in stressful, low yielding environments. Using data from variety trials conducted between 2000 and 2022 and cultivars released between 1967 and 2022, we explored time trends in phenotypic plasticity and heritability of yield. We hypothesize that i) if yield plasticity is agronomically adaptive, then newer cultivars will have higher yield plasticity; ii) heritability of yield is declining in the time series; and iii) genomic regions associated with yield depend on the environment and do not fully overlap with those associated with phenotypic plasticity of yield. Breeding for yield and agronomic adaptation increased phenotypic plasticity of yield at 0.5% year -1; broad sense heritability of yield decreased from 0.23 in 1993 to 0.15 in 2017. Genome-wide-association analysis shows genomic regions associated with yield varied between high yielding and stressful environments and were partially independent of those associated with yield plasticity. Newer cultivars have a higher frequency of alleles associated with yield and its plasticity. We discuss implications for breeding and agronomy aimed to improve wheat phenotypes.

genetics↗

bioRxiv2024

Effects of Martian magnetic and gravitational fields across multiple generations of the nematode C. elegans

AbstractsLife on Mars will require organisms to endure sustained exposure to reduced gravity and near-absent planetary magnetism, yet little is known about how these environmental factors may combine to potentially affect biology across generations. Here, we investigated the transgenerational effects of Martian gravity and magnetism on neuromuscular, sensory, and morphological function using Caenorhabditis elegans as a model. Animals were reared continuously under ground-based simulated Martian conditions across six generations and assessed using high-throughput behavioral and morphometric assays. Animals remained viable but acquired progressive transgenerational impairments across multiple functional domains. Swimming frequency showed immediate and severe deficits at all generations tested (Cohens d = 2.6-4.2), while chemotaxis deficits emerged more gradually, becoming significant by Generation 4. Morphological changes followed non-monotonic trajectories, with transient compensatory growth at Generation 4 followed by increased developmental variability at Generation 6. These differential patterns of impairment across neuromuscular, sensory, and developmental systems reveal domain-specific vulnerabilities to sustained Martian conditions. Critically, by Generation 6, Mars lineages exhibited a three-to-eight-fold increase in phenotypic variability, consistent with a loss of developmental canalization that could pose a more serious challenge for sustained colonization than simple mean fitness reductions.

physiology↗

bioRxiv2024

Ecogeographic signals of local adaptation in a wild relative help to identify variants associated with improved wheat performance under drought stress

Prioritizing wild relative diversity for improving crop adaptation to emerging drought-prone environments is challenging. Here, we combined the genome-wide environmental scans (GWES) in wheat diploid ancestor Aegilops tauschii with allele testing in the genetic backgrounds of adapted cultivars to identify new diversity for improving wheat adaptation to water-limiting conditions. Evaluation of adaptive allele effects was carried out in Ae. tauschii-wheat introgression lines (ILs) phenotyped for multiple agronomic traits under irrigated and water-limiting conditions using both UAS-based imaging and conventional approaches. The GWES showed that climatic gradients alone explain most (57.8%) of genomic variation in Ae. tauschii, with many alleles associated with climatic factors in Ae. tauschii being linked with improved performance of ILs under water-limiting conditions. The most significant GWES SNP located on chromosome 4D and associated with temperature annual range was linked with reduced canopy temperature in ILs. Our results suggest that (i) introgression of climate-adaptive alleles from Ae. tauschii have potential to improve wheat performance under water-limiting conditions, (ii) variants controlling physiological processes responsible for maintaining leaf temperature are likely among the targets of adaptive selection in a wild relative, and (iii) adaptive variation uncovered by GWES in wild relatives has potential to improve climate resilience of crop varieties.

genetics↗

bioRxiv2023

CRISPR-based editing of the ω- and γ-gliadin gene clusters reduces wheat immunoreactivity without affecting grain protein quality

Wheat immunotoxicity is associated with abnormal reaction to gluten-derived peptides. Attempts to reduce immunotoxicity using breeding and biotechnology often affect dough quality. Here, the multiplexed CRISPR-Cas9 editing of cultivar Fielder was used to modify the {omega} -and {gamma}-gliadin gene clusters abundant in immunoreactive peptides identified in the genomes assembled using the long-read sequencing technologies. The whole genome sequencing of an edited line showed editing or deletion of nearly all {omega}-gliadin and half of the {gamma}-gliadin gene copies and lack of editing in the /{beta}-gliadin genes. The detected 62% and 52% reduction in {omega}- and {gamma}-gliadin content, respectively, had no negative impact on grain protein quality. A 47-fold immunoreactivity reduction compared to wild-type was detected using antibodies against immunotoxic peptides. Our results indicate that genome profiling to identify gliadin gene copies abundant in immunoreactive peptides and their targeted editing could be an effective mean for reducing immunotoxicity of wheat cultivars while minimizing the impact of editing on protein quality.

genetics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Haploflow: Strain-resolved de novo assembly of viral genomes

In viral infections often multiple related viral strains are present, due to coinfection or within-host evolution. We describe Haploflow, a de Bruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assessed Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. Haplotype reconstructed high-quality strain-resolved assemblies from clinical HCMV samples and SARS-CoV-2 genomes from wastewater metagenomes identical to genomes from clinical isolates.

bioinformatics↗

bioRxiv2020

Tutorial: Assessing metagenomics software with the CAMI benchmarking toolkit

Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices, common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized data sets, procedures, and metrics for evaluation. In this tutorial, we describe emerging standards in computational metaomics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics to assess metagenome assembly, binning, and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI data sets. This tutorial will serve as a reference to the community and facilitate informative and reproducible benchmarking in microbiome research.

bioinformatics↗

bioRxiv2020

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA-viruses

Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and six variant callers on ten lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive (FP) variant calls, which were strongly enriched in T to G changes in a "G.G" context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.

bioinformatics↗

bioRxiv2019

Chromosome-scale genome assembly provides insights into rye biology, evolution, and agronomic potential

We present a chromosome-scale annotated assembly of the rye (Secale cereale L. inbred line Lo7) genome, which we use to explore Triticeae genomic evolution, and ryes superior disease and stress tolerance. The rye genome shares chromosome-level organization with other Triticeae cereals, but exhibits unique retrotransposon dynamics and structural features. Crop improvement in rye, as well as in wheat and triticale, will profit from investigations of rye gene families implicated in pathogen resistance, low temperature tolerance, and fertility control systems for hybrid breeding. We show that rye introgressions in wheat breeding panels can be characterised in high-throughput to predict the yield effects and trade-offs of rye chromatin.

genomics↗

bioRxiv2019

CAMITAX: Taxon labels for microbial genomes

The number of microbial genome sequences is growing exponentially, also thanks to recent advances in recovering complete or near-complete genomes from metagenomes and single cells. Assigning reliable taxon labels to genomes is key and often a prerequisite for downstream analyses. We introduce CAMITAX, a scalable and reproducible workflow for the taxonomic labelling of microbial genomes recovered from isolates, single cells, and metagenomes. CAMI-TAX combines genome distance-, 16S rRNA gene-, and gene homology-based taxonomic assignments with phylogenetic placement. It uses Nextflow to orchestrate reference databases and software containers, and thus combines ease of installation and use with computational re-producibility. We evaluated the method on several hundred metagenome-assembled genomes with high-quality taxonomic annotations from the TARA Oceans project, and show that the ensemble classification method in CAMITAX improved on all individual methods across tested ranks. While we initially developed CAMITAX to aid the Critical Assessment of Metagenome Interpretation (CAMI) initiative, it evolved into a comprehensive software to reliably assign taxon labels to microbial genomes. CAMITAX is available under the Apache License 2.0 at: https://github.com/CAMI-challenge/CAMITAX

bioinformatics↗

bioRxiv2018

CAMISIM: Simulating metagenomes and microbial communities

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

bioinformatics↗

bioRxiv2017

AMBER: Assessment of Metagenome BinnERs

Reconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins, but assessing their quality is difficult due to the lack of evaluation software and standardized metrics. We present AMBER, an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark data sets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for ten different binnings on two CAMI benchmark data sets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub (https://github.com/CAMI-challenge/AMBER).

bioinformatics↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗