Biology subjects

Hoff, K.

Publications and source records attributed to Hoff, K..

10 recordsLinked to original sources

bioRxiv2026

In vivo acquired resistance to tamoxifen is associated with irreversible loss of IGF1R, upregulation of insulin receptor and enhanced sensitivity to insulin

Selective estrogen receptor modulators (SERMs) like tamoxifen are used to treat hormone receptor positive (HR+) breast cancers. Several models of in vitro acquired resistance to tamoxifen using HR+ breast cancer cell lines have been developed. There are few models of resistance to tamoxifen that are generated in vivo. Here, we generated a model of in vivo resistance to tamoxifen. We show that in vivo tamoxifen resistance is associated with loss of IGF1R. This loss is irreversible as it persists even when tamoxifen is removed. In vivo tamoxifen resistance is also associated with upregulation of insulin receptor, specifically the A isoform of IR (IR-A). These resistant cells show enhanced insulin sensitivity compared to the parental endocrine sensitive or in vitro acquired tamoxifen resistant cells. Finally, we demonstrate that in vivo resistance to tamoxifen results in enhanced proliferation in response to insulin. These studies establish that some mechanisms of in vivo resistance to tamoxifen differ from those observed for in vitro acquired resistance and increased dependence on insulin signaling drives endocrine resistance in HR+ breast cancer. Taken together, these studies present that targeting IR-A in combination with newer targets of tamoxifen resistance could be tested in endocrine resistant breast cancer.

Cancer Biology↗

bioRxiv2026

Genomic insights into Candidatus Argioplasma dusa, a bacterial symbiont of the wasp spider Argiope bruennichi

Bacterial symbionts were shown to have significant beneficial impacts on the fitness of their host in insects, but little is known on the symbionts of spiders and their interactions with their hosts. Here, we assembled and investigated the circular 575 kb genome of Candidatus Argioplasma dusa, a novel bacterial symbiont of the wasp spider Argiope bruennichi. Phylogenomic analysis placed this species within the phylum Tenericutes, in a poorly characterized clade that may represent a new order-level lineage or affiliate with the Mycoplasmatales order. With 559 predicted genes, the genome is relatively small compared to other Tenericutes genomes (on average 969 genes) and has a low GC content of ~24%. While the genome encodes genes for proteins involved in glycolysis and fermentative acetate production, it revealed minimal biosynthetic capabilities with pathways for nucleotide, amino acid and vitamin biosynthesis being absent in Ca. Argioplasma dusa. This suggests an intracellular endosymbiotic lifestyle within the spider host. The symbiont was detected in A. bruennichi populations across the distribution range of the spider but appears to be absent in certain populations.

microbiology↗

bioRxiv2025

Gene-Expression Programs in Salivary Gland Adenoid Cystic Carcinoma Analyzed Using Single-Cell and Spatial Transcriptomics

Adenoid cystic carcinoma of the salivary gland (SGACC) is a highly aggressive malignancy characterized by poor patient survival outcomes. While several studies have analyzed the transcriptome of the salivary gland at the bulk and single-cell level, no spatial transcriptomic analyses of this tissue have been published. Most of the existing publications on SGACC have predominantly relied on bulk and single cell RNA sequencing approaches, which do not resolve the spatially localized transcriptional heterogeneity nor have the resolution for defining molecular markers within tumor subpopulations. SGACC is clinically notable for the presence of multiple tumor clones, distinct spatial phenotypes, and its indolent yet invasive nature coupled with a high propensity for distant metastasis. These features may reflect co-expression of tumor-associated markers across diverse cellular niches, and a resultant biological complexity which causes standard treatment such as surgical resection, radiation therapy, and chemotherapy to be largely ineffective in significantly improving long-term survival, and highlights the need for more precise, targeted therapeutic strategies. Herein, we analyzed single cell (n = 4) and high-resolution spatial transcriptomics samples (n = 5) to characterize cancer cell populations in MYB- and non-MYB-expressing cell states, delineated gene expression signatures, and identified critical molecular interactions specific to SGACC. We used Visum HD to obtain spatial transcriptomics data at 2{micro}m squared high resolution. This allowed a multi-omics approach comprising single cell and spatial transcriptomic methods to enable the discovery of novel transcriptional signatures and microenvironmental features not captured by conventional methods. Spatial mapping revealed marked cellular heterogeneity and demonstrated how tissue environments influence cellular transcriptomics. To tumor heterogeneity, we focused on tumorigenic cell populations, profiled plasma and T cell enrichment within the tumor microenvironment and identified key pathways and transcriptional drivers including the MYB-NFIB fusion underlying the tumor cluster formation. Our findings indicate an upregulation of genes involved in extracellular matrix remodeling, autophagy, and reactive stromal cell populations. We further found evidence of partial epithelial-mesenchymal transition (P-EMT) programming within MYB-expressing tumor clusters. Pathway analysis revealed that mutations in the spatial query sample prominently affect the PI3K-AKT and IL-17 signaling pathways, together with a downregulation of canonical Wnt signaling in some regions of the tissue architecture adjacent to immune cells. Collectively, these results underscore the complex regulatory landscape of SGACC and offer insights into its cellular dynamics and possible therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence

Recent results of clinical trials in antibody drug conjugate therapies have significantly broadened treatment options for the HER2 low and ultra-low breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remain challenging, especially in low and ultra-low HER2 expression ranges. We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest mAb pharmDx (Dako Omnis) (HercepTest mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression. Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&E, HercepTest (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128x128{micro}m2 area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0<IHC<1+, and IHC 1+ groups, and high degree of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R2 of 0.87. The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.

pathology↗

bioRxiv2024

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments using both TeleProt and standard directed evolution (DE) approaches in parallel, we find that our approach found a significantly better top-performing enzyme variant than DE, had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55K nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design.

bioinformatics↗

bioRxiv2024

Spatial Transcriptomics Sequencing of Mouse Liver at 2 Micron Resolution Using a Novel Spatial DNA Chip

Spatial transcriptomics enables analysis of gene expression that is spatially resolved within a tissue section, making it possible to elucidate the relationship between individual cells within the context of the tissue. This transformative technology enables researchers to better understand gene function within the context of health tissue, developmental processes, and disease. In this study, we present an innovative spatial transcriptomics technology and data using a high-resolution DNA chip with a total capture region size of 6.5 x 6.5 mm containing 2 x 2 {micro}m features for spatial barcoding with no gaps between the features, thereby maximizing the capture area. These chips are manufactured at wafer scale using photolithography and are transferred to hydrogels, making them compatible with existing workflows for fresh frozen or paraffin-embedded samples. Herein, we examined a fresh frozen sample from an adult mouse liver. To analyze the data, we binned 10 x 10 features to represent a 20 {micro}m x 20 {micro}m capture area. We obtained 1.3 billion unique mapped reads, 68.78% sequencing saturation, with a median of 16,967 unique reads per region, indicating the potential for more unique reads with deeper sequencing. This high-resolution mapping of liver cell types and the visualization of gene expression patterns illustrate significant advancements in spatial sequencing technology.

genomics↗

bioRxiv2021

SARS-CoV-2 Variant Identification Using a Genome Tiling Array and Genotyping Probes

With over three million deaths worldwide attributed to the respiratory disease COVID-19 caused by the novel coronavirus SARS-CoV-2, it is essential that continued efforts be made to track the evolution and spread of the virus globally. We previously presented a rapid and cost-effective method to sequence the entire SARS-CoV-2 genome with 95% coverage and 99.9% accuracy. This method is advantageous for identifying and tracking variants in the SARS-CoV-2 genome when compared to traditional short read sequencing methods which can be time consuming and costly. Herein we present the addition of genotyping probes to our DNA chip which target known SARS-CoV-2 variants. The incorporation of the genotyping probe sets along with the advent of a moving average filter have improved our sequencing coverage and accuracy of the SARS-CoV-2 genome.

bioinformatics↗

bioRxiv2020

Identification of Fibronectin 1 as a candidate genetic modifier in a Col4a mutant mouse model of Gould syndrome

Collagen type IV alpha 1 and alpha 2 (COL4A1 and COL4A2) are major components of almost all basement membranes. COL4A1 and COL4A2 mutations cause a multisystem disorder called Gould syndrome which can affect any organ but typically involves the cerebral vasculature, eyes, kidneys and skeletal muscles. The manifestations of Gould syndrome are highly variable and animal studies suggest that allelic heterogeneity and genetic context contribute to the clinical variability. We previously characterized a mouse model of Gould syndrome caused by a Col4a1 mutation in which the severities of ocular anterior segment dysgenesis (ASD), myopathy, and intracerebral hemorrhage (ICH) were dependent on genetic background. Here, we performed a genetic modifier screen to provide insight into the mechanisms contributing to Gould syndrome pathogenesis and identified a single locus (modifier of Gould syndrome 1; MoGS1) on Chromosome 1 that suppressed ASD. A separate screen showed that the same locus ameliorated myopathy. Interestingly, MoGS1 had no effect on ICH, suggesting that this phenotype may be mechanistically distinct. We refined the MoGS1 locus to a 4.3 Mb interval containing 18 protein coding genes, including Fn1 which encodes the extracellular matrix component fibronectin 1. Molecular analysis showed that the MoGS1 locus increased Fn1 expression raising the possibility that suppression is achieved through a compensatory extracellular mechanism. Furthermore, we show evidence of increased integrin linked kinase levels and focal adhesion kinase phosphorylation in Col4a1 mutant mice that is partially restored by the MoGS1 locus implicating the involvement of integrin signaling. Taken together, our results suggest that tissue-specific mechanistic heterogeneity contributes to the variable expressivity of Gould syndrome and that perturbations in integrin signaling may play a role in ocular and muscular manifestations.

genetics↗

bioRxiv2020

BRAKER2: Automatic Eukaryotic Genome Annotation with GeneMark-EP+ and AUGUSTUS Supported by a Protein Database

Full automation of gene prediction has become an important bioinformatics task since the advent of next generation sequencing. The eukaryotic genome annotation pipeline BRAKER1 had combined self-training GeneMark-ET with AUGUSTUS to generate genes coordinates with support of transcriptomic data. Here, we introduce BRAKER2, a pipeline with GeneMark-EP+ and AUGUSTUS externally supported by cross-species protein sequences aligned to the genome. Among the challenges addressed in the development of the new pipeline was generation of reliable hints to the locations of protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. Under equal conditions, the gene prediction accuracy of BRAKER2 was shown to be higher than the one of MAKER2, yet another genome annotation pipeline. Also, in comparison with BRAKER1 supported by a large volume of transcript data, BRAKER2 could produce a better gene prediction accuracy if the evolutionary distances to the reference species in the protein database were rather small. All over, our tests demonstrated that fully automatic BRAKER2 is a fast and accurate method for structural annotation of novel eukaryotic genomes.

bioinformatics↗

bioRxiv2020

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Congenital Heart Disease (CHD) affects approximately 7-9 children per 1000 live births. Numerous genetic studies have established a role for rare genomic variants at the copy number variation (CNV) and single nucleotide variant level. In particular, the role of de novo mutations (DNM) has been highlighted in syndromic and non-syndromic CHD. To identify novel haploinsufficient CHD disease genes we performed an integrative analysis of CNVs and DNMs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm (TAA). We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed mutation rate testing for DNMs identified in 2,489 parent-offspring trios. Our combined analysis revealed 21 genes which were significantly affected by rare genomic deletions and/or constrained non-synonymous de novo mutations in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in singletons and small cases series, or show new associations with CHD. In addition, a systems level analysis revealed shared contribution of CNV deletions and DNMs in CHD probands, affecting protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes.

genetics↗