Biology subjects

Kraft, A.

Publications and source records attributed to Kraft, A..

15 recordsLinked to original sources

bioRxiv2026

Protein co-scientist aided simulations of the structural and functional impact of O-GlcNAc Transferase TPR domain deletions and amino acid substitutions associated with X-Linked Intellectual Disability

O-GlcNAc transferase (OGT) is an essential glycosyltransferase that regulates cellular processes through the addition of O-linked {beta}-N-acetyglucosamine (O-GlcNAc) to target proteins. Substrate recognition and protein-protein interactions are mediated by the tetratricopeptide repeat (TPR) domain, yet the contributions of individual TPR repeats to structural stability and function remain poorly understood. Here, we employed an integrated computational framework combining an AI protein co-scientist, Speak to a Protein, which incorporates Boltz-2 for structure prediction and rDock for ligand binding evaluation, with external tools including AlphaFold3 for more advanced structure prediction and the HDOCK server for protein-protein docking. This framework was used to investigate how deletions of TPR domains and disease-associated amino acid substitutions within the TPR domain affect OGT structure and function. Five synthetic truncation OGT isoforms, thirteen single-TPR deletion variants, and five X-linked intellectual disability (XLID)-associated amino acid substitutions were modeled and aligned to wild-type OGT for comparison. Structural perturbations were quantified using backbone root mean square deviation (RMSD), while functional effects were assessed through docking analyses with UDP-GlcNAc, Host Cell Factor 1 (HCF1), and Tet methylcytosine dioxygenase 1 (TET1). Variation in TPR length altered global protein structure and induced long-range conformational changes within the catalytic domain despite no sequence modifications in that region. Single TPR deletions showed that individual repeats contribute unequally to structural stability, with deletions in mid-to-late TPR domains producing the largest structural disruptions. Disease-associated amino acid substitutions also generated conformational changes and reduced predicted ligand binding, consistent with known reductions in enzymatic activity in XLID. Protein-protein docking analyses demonstrated substrate-specific effects, as domain deletion and amino acid substitution variants of OGT had different interactions with TET1 and HCF1. Collectively, these findings identify the TPR domain as a regulatory domain that governs structure, ligand recognition, and protein interactions. More broadly, this work demonstrates how the integration of a protein co-scientist with protein structure modeling tools can be used to predict the structural and functional consequences of domain deletions or natural amino acid variants.

bioinformatics↗

bioRxiv2026

Structure-activity of outer membrane proteins in native bacterial membrane vesicles by solid-state NMR

The outer membranes (OMs) of bacterial pathogens are potent virulence factors and serve as the first line of defense against host immunity. Their striking bilayer asymmetry is essential for function but poses an exceptional challenge for reconstitution in vitro, limiting structure-activity analysis to artificial non-native platforms that can interfere with structure and function. Here, we describe bacterial OM vesicles (OMVs), natively secreted from the cellular OM during bacterial cell growth and development, as an effective vehicle for structure-activity analysis in situ based on solid-state nuclear magnetic resonance (NMR). We show that E. coli OMVs may be engineered to express a range of isotopically labeled target OM proteins, and isolated for solid-state NMR magic angle spinning (MAS) experiments. High resolution NMR spectra are obtained for three bacterial virulence factors: the adhesion invasion locus (Ail) and plasminogen activator protease (Pla) from Yersinia pestis, and the major porin (OmpF) from E. coli. The spectra reflect the native protein structures, report on the specific OMV membrane environment, and may be used to map protein interactions with their human host ligands, specifically the multifunctional glycoprotein Vitronectin (Vn) which binds Ail as part of its serum protection activity. Notably, OMVs support protein functionality, enabling structure and activity to be correlated in situ. OMVs expressing plasmid-encoded Ail recruit human Vn and confer serum protection to wild-type E. coli cells, while OMVs expressing plasmid-encoded Pla support the proteolytic activity of Pla. Taken together, the data establish OMVs as a robust new platform for structure-activity analysis of OM proteins in situ, offer new insights about the complexity of the bacterial OM, and reveal additional functional aspects of OMVs as key ancillary units of bacterial infection.

biophysics↗

bioRxiv2025

Exosomal cargo genes as biomarkers and potential mediators of exosome-driven cell communication in Pleural Mesothelioma

Pleural mesothelioma (PM) is a rare yet aggressive and heterogeneous cancer type with very poor survival rates. Due to its long latency period and nonspecific symptoms, the disease is usually detected at advanced stages, limiting available treatment options and leading to poor survival rates. So far, the disease can only be confirmed through invasive thoracoscopic biopsy. Moreover, the proposed circulating protein biomarkers lack sensitivity and specificity, highlighting the urgent need for novel approaches. In our previous work, we characterized the transcriptomic profile of extracellular vesicles secreted by PM cells and demonstrated their feasibility as a source of circulating biomarkers. To further investigate the role of circulating RNA in tumor progression and its potential use in non-invasive testing for PM, here we present a detailed characterization of RNA cargo carried by PM exosomes - a distinct subset of extracellular vesicles known to serve as key mediators of intercellular communication. By utilizing primary cell cultures established from tissue samples of 11 PM and 6 non-PM patients, followed by exosome isolation, and total RNA sequencing of RNA from isolated exosomes, matching cells, and tissues, we provide new evidence on exosomal RNA secretion, regulation of PM-exosome cargo genes, and their potential functions in recipient cells. We show that PM-exosomal cargo is enriched in genes associated with proliferation, whose key transcriptional factors are SETDB1, FOXM1 and GATA2. We identified Pcbp2, Srsf1 and Srsf9, RNA-binding proteins, which may be involved in selective cargo sorting into PM-exosomes. Our analysis identified six secreted genes, including GAS5, AL031666.3, RSLD1D1, AC103740.2, ADAM10, and AC020892.1, whose exosomal expression was associated with patient survival, as promising biomarkers for patient diagnosis, stratification, and prognosis assessment. Finally, we identify potential target genes of candidate LncRNAs biomarkers and processes in which they are involved.

cancer biology↗

bioRxiv2025

Unifying DNA methylation-based in silico cell-type deconvolution with methyldeconv

SummaryCell-type deconvolution is widely applied to gene expression and DNA methylation data, but access to methods for the latter remains limited. We introduce deconvMe, a new R package that simplifies access to DNA methylation-based deconvolution methods predominantly for blood data, and we additionally compare their estimates to those from gene expression and experimental ground truth data using a unique matched blood dataset.

bioinformatics↗

bioRxiv2025

CDState: an unsupervised approach to predict malignant cell heterogeneity in tumor bulk RNA-sequencing data

Intratumor transcriptional heterogeneity (ITTH), defined by the coexistence of diverse cell states within one tumor, complicates cancer treatment by contributing to variable therapeutic responses. Although single-cell RNA sequencing can resolve this complexity, its cost and technical demands limit its large-scale use. Bulk RNA-seq data provide a scalable alternative, but most deconvolution methods depend on predefined references, restricting their ability to detect novel malignant states. Unsupervised approaches avoid these constraints but are not tailored to capture heterogeneity within the malignant compartment. To address these limitations, we introduce CDState, an unsupervised method for inferring malignant cell subpopulations from bulk RNA-seq data. CDState utilizes non-negative matrix factorization improved with sum-to-one constraint and a cosine similarity-based optimization to deconvolve bulk gene expression into distinct cell state profiles. We demonstrate robustness of CDState on bulkified single-cell RNA-seq datasets from five cancer types, showing that it outperforms existing unsupervised deconvolution methods in the estimation of both cell state proportions and gene expression profiles. Applied to 33 cancer types from The Cancer Genome Atlas, CDState reveals recurrent gene programs, including epithelial-mesenchymal transition, MYC targets, and oxidative phosphorylation, as major contributors to malignant cell ITTH. We further link malignant states to patient clinical features, identifying states associated with poor prognosis. We propose an intratumor heterogeneity index and show its association with patient survival, clinical characteristics, and therapeutic response. Finally, we identify mutations and copy number alterations in genes such as TP53, KRAS, PIK3CA, SOX2, and SATB1 as potential genetic drivers of malignant cell ITTH across cancer types.

bioinformatics↗

bioRxiv2024

Filtering cells with high mitochondrial content removes viable metabolically altered malignant cell populations in cancer single-cell studies

BackgroundSingle-cell transcriptomics has transformed our understanding of cellular diversity in biological systems. However, systematic noise, often introduced by low-quality cells, can obscure biological signals if not properly accounted for. Thus, one of the common quality control steps involves filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), as high pctMT typically indicates cell death. Yet, commonly used filtering thresholds, primarily derived from studies on healthy tissues, may be overly stringent for malignant cells, which often naturally exhibit higher baseline mitochondrial gene expression. We analyzed public single-cell RNA-seq and spatial data to investigate if malignant cells with high pctMT are viable and functionally significant subpopulations. ResultsWe analyzed nine single-cell RNA-seq datasets from uveal melanoma, breast, lung, kidney, head and neck, prostate, and pancreatic cancers, including 439,507 cells from 151 patients. Malignant cells exhibited significantly higher pctMT than nonmalignant cells without a significant increase in dissociation-induced stress signature scores. Malignant cells with high pctMT showed metabolic dysregulation, including increased xenobiotic metabolism, which is implicated in cancer therapeutic response. Our analysis of pctMT in cancer cell lines uncovered associations with resistance and sensitivity to certain classes of drugs. Additionally, we observed a link between pctMT and malignant cell transcriptional heterogeneity as well as patient clinical features. ConclusionsThis study provides a detailed exploration of the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in single-cell RNA-seq analyses of tumors. Our findings have the potential to improve data interpretation and refine the biological conclusions of future cancer studies.

bioinformatics↗

bioRxiv2024

Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we performed multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing, along with spatial profiling. We identified tumor microenvironmental features previously described to associate with therapy response. We identified five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which were associated with differential epigenetic plasticity and clinical outcomes, and for which we inferred candidate transcription factor regulons. Furthermore, we revealed diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predicted their significant interactions with microenvironmental cell types. We validated our findings in three external single-cell RNA-seq and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

cancer biology↗

bioRxiv2024

Solid state NMR spectral editing of histidine, arginine and lysine using Hadamard encoding

The NMR signals from protein sidechains are rich in information about intra- and inter-molecular interactions, but their detection can be complicated due to spectral overlap as well as conformational and hydrogen exchange. In this work, we demonstrate a protocol for multi-dimensional solid-state NMR spectral editing of signals from basic sidechains based on Hadamard matrix encoding. The Hadamard method acquires multi-dimensional experiments in such a way that both the backbone and under-sampled sidechain signals can be decoded for unambiguous editing in the 15N spectral frequency dimension. All multi-dimensional 15N-edited solid-state NMR experiments can be acquired using this strategy, thereby accelerating the acquisition of spectra spanning broad frequency bandwidth. Application of these methods to the ferritin nanocage, reveals signals from N atoms from His, Arg, Lys and Trp sidechains, as well as their tightly bound, ordered water molecules. The Hadamard approach adds to the arsenal of spectroscopic approaches for protein NMR signal detection.

biophysics↗

bioRxiv2023

ANS: Adjusted Neighborhood Scoring to improve assessment of gene signatures in single-cell RNA-seq data

In the field of single-cell RNA sequencing (scRNA-seq), gene signature scoring is integral for pinpointing and characterizing distinct cell populations. However, challenges arise in ensuring the robustness and comparability of scores across various gene signatures and across different batches and conditions. Here, we evaluated the stability of established methods such as Scanpy, UCell, and JASMINE in the context of scoring cells of different types and states. On eight cancer and healthy scRNA-seq datasets, we reported that none of the existing methods provide fair gene signature scores that can be used in unsupervised cell state annotation based on the highest signature values. Addressing this challenge, we introduced a new scoring method, the Adjusted Neighbourhood Scoring (ANS), that builds on the traditional Scanpy method and improves the handling of the control gene sets. We further exemplified the usability of ANS scoring in differentiating between cancer-associated fibroblasts and malignant cells undergoing epithelial-mesenchymal transition (EMT) in four cancer types, and evidenced excellent classification performance (AUCPR train: 0.95-0.99, AUCPR test: 0.91-0.99). In summary, our research introduces ANS as a robust and deterministic scoring approach that enables the comparison of diverse gene signatures and score-based annotation of cell types and states. The results of our study contribute to the development of more accurate and reliable methods for analyzing scRNA-seq data.

bioinformatics↗

bioRxiv2022

CanSig: Discovering de novo shared transcriptional programs in single cancer cells

Single-cell RNA sequencing (scRNA-seq) facilitates the discovery of gene signatures that define cell states across patients, which could be used in patient stratification and drug discovery. However, the lack of standardization in computational methodologies to analyse these data impedes the reproducibility of signature detection. To address this, we developed CanSig, a comprehensive benchmarking tool that evaluates methods for identifying transcriptional signatures in cancer. CanSig integrates metrics for batch correction and biological signal conservation with a gene signature correlation metric to score according to rediscovery, cross-dataset reproducibility, and clinical relevance. We applied CanSig to ten methods and to ten scRNA-seq datasets from four human cancer types--glioblastoma, breast cancer, lung adenocarcinoma, and cutaneous squamous cell carcinoma-- representing 116 patients and 105,000 malignant cells. Our results identify BBKNN as a leading method. We showed that the signatures identified with these methods correlate with clinically relevant outcomes, including patient survival and lymph node metastasis. Thus, CanSig establishes a standardized framework for reproducible cancer transcriptomics analysis.

bioinformatics↗

bioRxiv2021

Enhanced fitness of SARS-CoV-2 variant of concern B.1.1.7, but not B.1.351, in animal models

Emerging variants of concern (VOCs) drive the SARS-CoV-2 pandemic. We assessed VOC B.1.1.7, now prevalent in several countries, and VOC B.1.351, representing the greatest threat to populations with immunity to the early SARS-CoV-2 progenitors. B.1.1.7 showed a clear fitness advantage over the progenitor variant (wt-S614G) in ferrets and two mouse models, where the substitutions in the spike glycoprotein were major drivers for fitness advantage. In the "superspreader" hamster model, B.1.1.7 and wt-S614G had comparable fitness, whereas B.1.351 was outcompeted. The VOCs had similar replication kinetics as compared to wt-S614G in human airway epithelial cultures. Our study highlights the importance of using multiple models for complete fitness characterization of VOCs and demonstrates adaptation of B.1.1.7 towards increased upper respiratory tract replication and enhanced transmission in vivo. Summary sentenceB.1.1.7 VOC outcompetes progenitor SARS-CoV-2 in upper respiratory tract replication competition in vivo.

microbiology↗

bioRxiv2021

CVnCoV protects human ACE2 transgenic mice from ancestral B BavPat1 and emerging B.1.351 SARS-CoV-2

The ongoing severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic necessitates the fast development of vaccines as the primary control option. Recently, viral mutants termed "variants of concern" (VOC) have emerged with the potential to escape host immunity. VOC B.1.351 was first discovered in South Africa in late 2020, and causes global concern due to poor neutralization with propensity to evade preexisting immunity from ancestral strains. We tested the efficacy of a spike encoding mRNA vaccine (CVnCoV) against the ancestral strain BavPat1 and the novel VOC B.1.351 in a K18-hACE2 transgenic mouse model. Naive mice and mice immunized with formalin-inactivated SARS-CoV-2 preparation were used as controls. mRNA-immunized mice developed elevated SARS-CoV-2 RBD-specific antibody as well as neutralization titers against the ancestral strain BavPat1. Neutralization titers against VOC B.1.351 were readily detectable but significantly reduced compared to BavPat1. VOC B.1.351-infected control animals experienced a delayed course of disease, yet nearly all SARS-CoV-2 challenged naive mice succumbed with virus dissemination and high viral loads. CVnCoV vaccine completely protected the animals from disease and mortality caused by either viral strain. Moreover, SARS-CoV-2 was not detected in oral swabs, lung, or brain in these groups. Only partial protection was observed in mice receiving the formalin-inactivated virus preparation. Despite lower neutralizing antibody titers compared to the ancestral strain BavPat1, CVnCoV shows complete disease protection against the novel VOC B.1.351 in our studies.

microbiology↗

bioRxiv2019

Direct monitoring of cAMP at the cardiac ryanodine receptor using a novel targeted fluorescence biosensor mouse

RationaleCyclic adenosine monophosphate (cAMP) is a ubiquitous second messenger which, upon {beta}-adrenergic receptor ({beta}-AR) stimulation, acts in microdomains to regulate cardiac excitation-contraction coupling by activating the cAMP-dependent protein kinase (PKA) phosphorylation of calcium handling proteins. One crucial microdomain is in vicinity of the cardiac ryanodine receptor type 2 (RyR2) which is associated with arrhythmogenic diastolic calcium leak from the sarcoplasmic reticulum (SR) often occurring upon RyR2 hyperphosphorylation by PKA and calcium/calmodulin-dependent kinase.\n\nObjectiveWe sought to establish a real time approach capable of directly visualizing cAMP and its pathological changes in the vicinity of RyR2 by generating a proper targeted biosensor and transgenic mouse model to express it in adult cardiomyocytes.\n\nMethods and ResultsWe generated transgenic mice expressing a novel targeted fluorescent biosensor for RyR2-associated cAMP in adult mouse cardiomyocytes. In healthy cardiomyocytes, {beta}1-AR but not {beta}2-AR stimulation strongly increased local RyR2-associated cAMP levels. However, in cardiac hypertrophy induced by aortic banding, there was a marked subcellular redistribution of phosphodiesterases (PDEs) 2, 3 and 4, which included a dramatic loss of the local pool of PDE4. This was also accompanied by measurable {beta}2-AR-induced cAMP signals, increased SR calcium leak and arrhythmia susceptibility.\n\nConclusionsOur new targeted biosensor expressed in transgenic mice can visualize cAMP levels in the vicinity of cardiac RyR2 in healthy and diseased cardiomyocytes. In the future, this novel biosensor can be used to better understand alterations of RyR2-associated cAMP in cardiovascular diseases and local actions of new therapies.

cell biology↗

bioRxiv2018

Spatial Chromatin Architecture Alteration by Structural Variations in Human Genomes at Population Scale

This genome-wide study is focused on the impact of structural variants identified in individuals from 26 human populations onto three-dimensional structures of their genomes. We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design a high-resolution computational algorithm to model the 3D conformational changes resulted by structural variations. We show that differential gene transcription is closely linked to variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across population, but enriched with disease-associated SNPs. Altogether, this study assesses the critical impact of structural variants on the higher order organization of chromatin folding and provides unique insight into the mechanisms regulating gene transcription at the population scale, among which the local arrangement of chromatin loops seems to be the leading one. It is the first insight into the variability of the human 3D genome at the population scale.

genomics↗

bioRxiv2018

Cell type specific profiling of alternative translation identifies novel protein isoforms in the mouse brain

Translation canonically begins at a single AUG and terminates at the stop codon, generating one protein species per transcript. However, some transcripts may use alternative initiation sites or sustain translation past their stop codon, generating multiple protein isoforms. Through other mechanisms such as alternative splicing, both neurons and glia exhibit remarkable transcriptional diversity, and these other forms of post-transcriptional regulation are impacted by neural activity and disease. Here, using ribosome footprinting, we demonstrate that alternative translation is likewise abundant in the central nervous system and modulated by stimulation and disease. First, in neuron/glia mixed cultures we identify hundreds of transcripts with alternative initiation sites and confirm the protein isoforms corresponding to a subset of these sites by mass spectrometry. Many of them modulate their alternative initiation in response to KCl stimulation, indicating activity-dependent regulation of this phenomenon. Next, we detect several transcripts undergoing stop codon readthrough thus generating novel C-terminally-extended protein isoforms in vitro. Further, by coupling Translating Ribosome Affinity Purification to ribosome footprinting to enable cell-type specific analysis in vivo, we find that several of both neuronal and astrocytic transcripts undergo readthrough in the mouse brain. Functional analyses of one of these transcripts, Aqp4, reveals readthrough confers perivascular localization, indicating readthrough can be a conserved mechanism to modulate protein function. Finally, we show that AQP4 readthrough is disrupted in multiple gliotic disease models. Our study demonstrates the extensive and regulated use of alternative translational events in the brain and indicates that some of these events alter key protein properties.

neuroscience↗