Biology subjects

Lampe, J.

Publications and source records attributed to Lampe, J..

4 recordsLinked to original sources

bioRxiv2026

Small stream restoration increases habitat and plant diversity across scales

A majority of stream restoration efforts in central Europe focus on streams that are less than five meters wide. Restoration aims to increase structural complexity, thereby enhancing habitat heterogeneity, promoting biodiversity, and reestablishing aquatic-terrestrial linkages that can drive responses in adjacent terrestrial communities. However, the effects of small stream restoration on terrestrial biodiversity remain poorly understood because research focuses mainly on large rivers. Here, we investigated the effects of restoration on the structural complexity of the stream channel as well as the terrestrial habitat and plant diversity on a local and landscape scale across 55 small streams in an agricultural landscape. We compared restored stream sections with non-restored ones that were similar to the conditions before restoration. We also assessed how restored sections changed over time since restoration. Restored stream sections showed a higher stream structural complexity and habitat diversity, both of which are targets of active restoration measures. Restoration also increased riparian plant diversity, both directly and indirectly through structural complexity and habitat diversity. Although time since restoration did not influence structural complexity, it drove successional changes in plant communities that became increasingly associated with wetland habitat conditions. Our results demonstrate that small stream restoration effectively increases floodplain habitat and plant diversity in agricultural landscapes, primarily by enhancing water availability in the floodplain. Restoration actions on small streams support biodiversity if they improve stream channel complexity, connect the stream with its floodplain, and create floodplain habitats.

ecology↗

bioRxiv2025

Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.

Absolute bacterial biomass estimation in the human gut is crucial for understanding microbiome dynamics and host-microbe interactions. Current methods for quantifying bacterial biomass in stool, such as flow cytometry, qPCR, or spike-ins (i.e., adding cells or DNA from an organism not normally found in a sample), can be labor-intensive, costly, and confounded by factors like water content, DNA extraction efficiency, PCR inhibitors, and other technical challenges that add bias and noise. We propose a simple, cost-effective approach that circumvents some of these technical challenges: directly estimating bacterial biomass from metagenomes using bacterial-to-host (B:H) read ratios. We compare B:H ratios to the standard methods outlined above, demonstrating that B:H ratios are useful proxies for bacterial biomass in stool and possibly in other host-associated substrates. We show how B:H ratios can be used to track antibiotic treatment response and recovery in both mice and humans, which showed 403-fold and 45-fold reductions in bacterial biomass during antibiotic treatment, respectively. Our results indicate that host and bacterial metagenomic DNA fractions in human stool fluctuate longitudinally around a stable mean in healthy individuals, and the average host read fraction varies across healthy individuals by < 8-9 fold. B:H ratios offer a convenient alternative to other absolute biomass quantification methods, without the need for additional measurements, experimental design considerations, or machine learning algorithms, enabling retrospective absolute biomass estimates from existing stool metagenomic data.

microbiology↗

bioRxiv2023

Microbial community-scale metabolic modeling predicts personalized short-chain-fatty-acid production profiles in the human gut.

Microbially-derived short chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation, and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. We present a microbial community-scale metabolic modeling (MCMM) approach to predict individual-specific SCFA production profiles. We assess the quantitative accuracy of our MCMMs using in vitro, ex vivo, and in vivo data. Next, we show how MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic, and probiotic interventions that optimize SCFA production in the gut. Our results represent an important advance in engineering gut microbiome functional outputs for precision health and nutrition.

systems biology↗

bioRxiv2018

Bayesian Classification of Microbial Communities Based on 16S rRNA Metagenomic Data

We propose a Bayesian method for the classification of 16S rRNA metagenomic profiles of bacterial abundance, by introducing a Poisson-Dirichlet-Multinomial hierarchical model for the sequencing data, constructing a prior distribution from sample data, calculating the posterior distribution in closed form; and deriving an Optimal Bayesian Classifier (OBC). The proposed algorithm is compared to state-of-the-art classification methods for 16S rRNA metagenomic data, including Random Forests and the phylogeny-based Metaphyl algorithm, for varying sample size, classification difficulty, and dimensionality (number of OTUs), using both synthetic and real metagenomic data sets. The results demonstrate that the proposed OBC method, with either noninformative or constructed priors, is competitive or superior to the other methods. In particular, in the case where the ratio of sample size to dimensionality is small, it was observed that the proposed method can vastly outperform the others.\n\nAuthor summaryRecent studies have highlighted the interplay between host genetics, gut microbes, and colorectal tumor initiation/progression. The characterization of microbial communities using metagenomic profiling has therefore received renewed interest. In this paper, we propose a method for classification, i.e., prediction of different outcomes, based on 16S rRNA metagenomic data. The proposed method employs a Bayesian approach, which is suitable for data sets with small ration of number of available instances to the dimensionality. Results using both synthetic and real metagenomic data show that the proposed method can outperform other state-of-the-art metagenomic classification algorithms.

bioinformatics↗