Biology subjects

Dietrich, J.

Publications and source records attributed to Dietrich, J..

9 recordsLinked to original sources

bioRxiv2025

A multicenter spatial transcriptomics atlas of human tuberculosis and non-tuberculous mycobacterial disease

Granulomas are the hallmark of mycobacterial (MB) infections, forming structured immune environments that contain bacteria but also drive disease persistence. However, their spatial and functional organization remains unclear. Using spatial RNA sequencing on 38 patient samples, we identified five distinct granuloma niches: a necrotic core, an immune-activated inner niche, an inflammatory and an extracellular matrix (ECM)-remodeling middle niche, an outer structural niche, and a tertiary lymphoid structure niche supporting antigen presentation. Immune activity peaks in the inner niche, transitioning to fibrosis at the periphery. Lymph node granulomas display reduced fibroblast involvement but stronger JAK-STAT activation. Mycobacterium tuberculosis (MTB) granulomas exhibit heightened JAK-STAT and IFN-{gamma} signaling, while non-tuberculous mycobacteria (NTM) granulomas show increased hypoxia signatures. Compared to sarcoidosis, MB granulomas feature a structured adaptive immune response, marked by the clustering of plasma cells. Our findings, accessible via https://lab-li.ciim-hannover.de/mb-granuloma/, define key disease signatures, guiding biomarker discovery and therapeutic targeting in granuloma-related diseases.

bioinformatics↗

bioRxiv2025

Breaking the culture habit: metagenomic diagnosis of companion animal skin infections

BackgroundSkin infections have been described as the primary cause for presentation in veterinary small animal practices and they frequently result in prescription of both topical and systemic antibiotics. Because such infections are often secondary complications of other underlying pathologies, recurrent infections are common and can lead to multiple antibiotic exposures. This scenario creates steady selection pressure toward antibiotic resistance at the confluence of the skin (the largest mammalian organ), the bloodstream, and shared human and animal environments. This case study compares metagenomic (MGX) data with aerobic culture to evaluate diagnostic utility for simultaneous identification and characterization of pathogens, microbiomes, and resistomes of companion animal skin infections. ResultsOne feline and eight canine skin swabs were analyzed with aerobic culture and traditional antimicrobial susceptibility testing (AST) and compared with MGX profiling. Veterinary laboratory diagnostic (VDL) culture and AST identified Staphylococcus aureus, S. pseudintermedius, S. schleiferi, methicillin resistant (MR) S. schleiferi (MRSS), MR S. pseudintermedius (MRSP) and Pseudomonas aeruginosa from skin swabs. MGX data described the identical bacterial pathogens recovered by aerobic culture and methicillin resistance genes mecA, mecI, mecR1 in samples for which AST confirmed MRSP and MRSS. MGX data also identified mec genes in samples without culture-based confirmation of MR phenotypes. MGX data also described multi-domain composition of microbiomes of infected skin including bacteria, fungi, viruses, phages, AMR, plasmids, and metabolic features associated with skin infections. ConclusionsMGX data identified the identical pathogens and inferred AMR phenotypes as culture-based diagnostic testing, and additionally characterizedo multi-domain microbiota, mobile AMR elements, and metabolic features. Efforts to accelerate cures by precision medical responses depend on accelerated precision diagnostics. Challenges remain for the implementation of MGX data into veterinary diagnostic laboratory investigation and response. We demonstrate with a small case study, that MGX data can be used to complement current state of the art VDL results and potentially advance a judicious veterinary medical response regarding antibiotic administration for companion animal skin infections. In the future, simultaneous description of the polymicrobial ecology of skin infections (bacterial, viruses, phages, fungi, and even functional metabolomic features) provided by MGX data can advance epidemiology, develop new treatment strategies, accelerate diagnostics and provide data for artificial intelligence (AI) models focused on advancing veterinary diagnostics and medical treatments.

microbiology↗

bioRxiv2025

Large-scale discovery and annotation of hidden substructure patterns in mass spectrometry profiles

Untargeted mass spectrometry measures many spectra of unknown molecules. To annotate them, tandem mass spectrometry (MS/MS) generates fragmentation patterns representing common substructures. MS2LDA discovers these patterns via unsupervised topic modelling as Mass2Motifs. However, MS/MS-based substructure identification is limited by computational efficiency and interpretability. Here, we report an up to 14x speed improvement through improved algorithmic efficiency. Furthermore, the new automated Mass2Motif Annotation Guidance (MAG) aids in structurally identifying Mass2Motifs. Using three chemically diverse curated MotifDB-MotifSets for benchmarking, MAG achieved median substructure overlap scores of 0.75 up to 0.93, demonstrating robust substructural annotations. We further validated MS2LDA 2.0 in experimental data by identifying substructures of pesticides spiked into a biological matrix and demonstrated its discovery potential by annotating previously uncharacterized fungal natural products. Together with the new visualization app and MassQL-searchable MotifDB, we anticipate that MS2LDA 2.0 will boost the identification of novel chemistry and hidden patterns in mass spectrometry profiles.

biochemistry↗

bioRxiv2025

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗

bioRxiv2024

Characterization of a new lacrimal gland cell line in 2D and 3D cell culture models

The lacrimal gland produces the tear films aqueous component, which moistens and nourishes the ocular surface to maintain eye health. Reduced production of this component leads to dry eye disease, which affects over 250 million people worldwide. Despite the impact on patients, the availability of primary human material to study underlying disease mechanisms is severely limited and there is no cell model available for human lacrimal gland epithelial cells. After insertion of an SV40 antigen into primary human lacrimal gland epithelial cells, we selected, expanded, and characterized three epithelial cell clones from a female lacrimal gland donor. We show their epithelial character at genomic (PCR and RNAseq) and protein (immunofluorescence) levels and grow these cells in a 3D cell spheroid model. Here, we report the development of an immortalized human lacrimal gland epithelial cell line that improves accessibility to study the molecular pathogenesis mechanisms of dry eye disease and link them to causal treatments. We show the expression of typical lacrimal gland epithelial cell marker genes (e.g. PAX6, FOXC1, AQP5, CSTB, and CST6) and describe the feasibility of the cells to form 2D cell sheets and 3D cell spheroids. We successfully established immortalized human lacrimal gland cells with epithelial character. In the future, the integration of these cells into larger studies holds great potential for advancing our understanding of dry eye disease and its underlying cellular mechanisms.

cell biology↗

bioRxiv2023

Multimodal mucosal and systemic immune characterization of a novel non-human primate trachoma model highlights the critical role of local immunity during acute phase disease

BackgroundTrachoma -the leading cause of blindness worldwide as a result of infection-is caused by repeated Chlamydia trachomatis (Ct) conjunctival infections. Disease develops in two phases: i) active (acute trachoma, characterized by follicular conjunctivitis), then long-term ii) scarring (chronic trachoma, characterized by conjunctival fibrosis, corneal opacification and eyelid malposition). Scarring trachoma is driven by the number and the severity of reinfections. The immune system is a pivotal aspect of disease, involved in disease aggravation, but also key for exploitation in development of a trachoma vaccine. Therefore, we characterized clinical and local immune response kinetics in a non-human primate model of acute conjunctival Ct infection and disease. Methodology/Principal FindingsThe conjunctiva of non-human primate (NHP, Cynomolgus monkeys -Macaca fascicularis-) were inoculated with Ct (B/Tunis-864 strain, B serovar). Clinical ocular monitoring was performed using a standardized photographic grading system, and local immune responses were assessed using multi-parameter flow cytometry of conjunctival cells, tear fluid cytokines, immunoglobulins, and Ct quantification. Clinical findings were similar to those observed during acute trachoma in humans, with the development of typical follicular conjunctivitis from the 4th week post-exposure to the 11th week. Immunologic analysis revealed an early phase influx of T cells in the conjunctiva and elevated interleukins 4, 8, and 5, before a later phase monocytic influx accompanied by a decrease in other immune cells, and tear fluid cytokines returning to initial levels. Conclusion/SignificanceOur NHP model accurately reproduces acute trachoma clinical signs, allowing for the precise assessment of the local immune responses in infected eyes. A progressing immune response occurred for weeks after exposure to Ct, which subsided into persistence of innate immune responses. Understanding these local responses is the first step towards using the model to assess new vaccine and therapeutic strategies to prevent disease. Author SummaryChlamydia trachomatis is the leading infectious cause of blindness worldwide. The pathogenesis of trachoma is more complicated than other types of bacterial conjunctivitis: clinical signs of trachoma are rooted in repeated Chlamydia trachomatis infections of the inner eyelid surfaces, which roughens the skin. This lead to eyelid deformation and lashes rubbing on the cornea, which across multiple years of abrasion, ends with corneal opacification. The immune system is a pivotal aspect of disease, involved in disease aggravation, but also key for exploitation in development of a trachoma vaccine. Here we describe a non-human primate model of trachoma that accurately reproduces acute human eye disease, allowing for the precise assessment of the local immune responses in infected eyes. A progressing immune response occurred 4 weeks after exposure to Ct, which subsided into persistence of innate immune responses. Understanding these local responses is the first step towards using the model to assess new vaccine and therapeutic strategies to prevent disease.

microbiology↗

bioRxiv2023

HSQC2STRUC: A Machine Learning Model for Protein Secondary Structure Prediction using Unassigned NMR Spectra

Dynamic changes in the secondary structure content of proteins can provide valuable insights into protein function or dysfunction. Predicting these dynamic changes is still a significant challenge but is of paramount importance for basic research as well as drug development. Here, we present a machine learning-based model that predicts the secondary structure content of proteins based on their un assigned1H,15N-HSQC NMR spectra with an RMSE of 0.11 for -helix, 0.08 for {beta}-sheet and 0.12 for random coil content. Our model has been implemented into an easy-to-use and publicly available web service that estimates secondary structure content based on a provided peak list. Furthermore, a Python version is provided, ready to be integrated into Brukers TopSpin software or own scripts.

bioinformatics↗

bioRxiv2023

Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy

Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.

cancer biology↗

bioRxiv2023

A Systems Serology Approach to the Investigation of Infection-Induced Antibody Responses and Protection in Trachoma

BackgroundOcular infections with Chlamydia trachomatis serovars A-C cause the neglected tropical disease trachoma. As infection does not confer complete immunity, repeated infections are common, leading to long term sequelae such as scarring and blindness. Here we apply a systems serology approach to investigate whether systemic antibody features are associated with susceptibility to infection. MethodsSera from children in five trachoma endemic villages in The Gambia were assayed for 23 antibody features: IgG responses towards two Chlamydia trachomatis antigens and three serovars (elementary bodies and major outer membrane protein MOMP, serovars A-C), IgG responses towards five MOMP peptides (serovars A-C), neutralization and antibody-dependent phagocytosis. Participants were considered resistant if they subsequently developed infection only when over 70% of other children in the same compound were infected. ResultsThe antibody features assayed were not associated with resistance to infection (false discovery rate < 0.05). Anti-MOMP SvA IgG and neutralization titer were higher in susceptible individuals (p < 0.05 before multiple testing adjustment). Classification using partial least squares performed only slightly better than chance in distinguishing between susceptible and resistant participants based on systemic antibody profile (specificity 71%, sensitivity 36%). ConclusionsSystemic infection-induced IgG and functional antibody responses do not appear to be protective against subsequent infection. This may be due to confounding factors increasing both past and future exposure to C. trachomatis, or antibody-dependent enhancement. Ocular responses, IgA, avidity or cell-mediated responses may play a greater role in protective immunity than systemic IgG.

immunology↗