Biomedical subjects

Anne Kupczok

Publications and source records attributed to Anne Kupczok.

3 recordsLinked to original sources

PubMed2026

Integrating multi-omics technologies to decipher microbiome functions.

Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science.

Multiomics↗

PubMed2026

Identification and Classification of Expressed Orphan Genes, Spurious Orphan Genes, and Conserved Genes in the Human Gut Microbiome.

Orphan genes (OGs)-genes lacking detectable homologs outside a species-are widespread in microbial genomes and are thought to contribute to their adaptation and molecular innovation. However, not all predicted OGs may represent novel functional coding sequences. False positive OGs, also called spurious OGs, can arise from gene prediction errors. We reason that OGs lacking detectable expression are more likely to be spurious. To test this, we combined large-scale metatranscriptomic profiling of the human gut microbiome with machine learning to distinguish expressed OGs from spurious ones and compare them with conserved genes (CGs) found in multiple species. Using nearly 5,000 metatranscriptome libraries, we identified ∼218,000 OGs supported by expression evidence, while ∼330,000 predicted OGs lacked detectable expression and were classified as spurious. We extracted 154 features for sequence, structural, and evolutionary properties for each gene and trained XGBoost classifiers while accounting for genomic representation. The models achieved an area under the receiver operating characteristic curve (AUC) of 0.82 in distinguishing expressed OGs from spurious OGs and an AUC of 0.93 in distinguishing expressed OGs from CGs. Interpretation based on SHAP (SHapley Additive exPlanations) revealed clear biological signals. Particularly, expressed orphans were present in more genomes than spurious ones, and expressed OGs were shorter than CGs. This work improves OG discovery and suggests that expressed OGs differ systematically from CGs and spurious OGs in sequence composition, structural constraints, and evolutionary signals.

Humans↗

PubMed2005

Determinants of simulated RNA evolution.

Models of RNA secondary structure folding are widely used to study evolution in theory and simulation. However, systematic studies of the parameters involved are rare. In this paper, we study by simulation how RNA evolution is influenced by three different factors, namely the mutation rate, scaling of the fitness function, and distance measure. We found that for low mutation rates the qualitative evolutionary behavior is robust with respect to the scaling of the fitness function. For efficient mutation rates, which are close to the error threshold, scaling and distance measure have a strong influence on the evolutionary behavior. A global distance measure that takes sequence information additively into account lowers the error threshold. When using a local sequence-structure alignment for the distance, we observed a smoother evolution of the fitness over time. Finally, in addition to the well known error threshold, we identify another threshold of the mutation rate, called divergence threshold, where the qualitative transient behavior changes from a localized to an exploratory search.

Animals↗