Biology subjects

Adamov, A.

Publications and source records attributed to Adamov, A..

5 recordsLinked to original sources

bioRxiv2025

Target-driven optimization of feature representation and model selection for microbiome sequencing data with ritme

Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured, and predictive modeling from them typically relies on ad hoc feature representation choices that obscure their impact on performance and interpretation. We present ritme, an open-source Python package that jointly optimizes microbiome-specific feature representation and model selection - combined algorithm selection and hyperparameter optimization - tailored to these data. ritme systematically searches taxonomic aggregation, sparsity-aware selection, compositional transforms, and metadata enrichment together with model class and hyperparameters, using state-of-the-art optimizers that scale from a laptop to a compute cluster. Across three real-world use cases, ritme outperformed the original study pipelines by 7-29% on the primary task metric and surpassed three AutoML baselines in six of seven comparisons, while selecting substantially fewer features and exposing how feature and model choices drive performance. Open-source and modular, ritme supports reproducible, parsimonious predictive modeling, downstream biological investigation, and extension to other multi-omics modalities.

bioinformatics↗

bioRxiv2025

The gut microbiota and sleep in infants: a focus on diurnal rhythmicity patterns

Emerging evidence supports a bidirectional relationship between the gut microbiome and sleep, partly mediated by the microbiota-gut-brain axis. Circadian rhythms appear to influence early developmental processes and may play a role in shaping this relationship. While infancy is a critical window for both gut microbiota establishment and the maturation of sleep regulation, the role of gut bacteria in modulating infant sleep remains poorly understood. In this longitudinal study, we performed continuous within-subject sampling (monitoring across 48 hours) in 20 infants at 2, 4, and 6 months of age to examine associations between the establishment of gut microbiota and sleep patterns. We hypothesized that microbial diversity and sleep rhythmicity would be linked across both short (diurnal) and long (monthly) time scales. Gut microbiota profiles were characterized using 16S rRNA gene sequencing; gut melatonin concentrations were measured; and sleep metrics were quantified through wearable actimetry, parent-reported 24-hour sleep diaries, and the Brief Infant Sleep Questionnaire (BISQ). Parents additionally completed the Baby Care Questionnaire (BCQ), assessing parenting style, and the Ages and Stages Questionnaire (ASQ), evaluating infant behavioral development. In some infants, alpha diversity followed diurnal rhythmic patterns. Although bacterial rhythmicity was not significantly associated with sleep rhythmicity, as quantified with the Circadian Function Index, infants with higher alpha diversity had more robust sleep rhythmicity ({beta}Faith PD= 0.0721, SE = 0.0337). Infant age emerged as the strongest predictor of both gut microbiota diversity (observed features: {beta}age = 0.101, SE = 0.0172) and gut melatonin concentrations ({beta}age = 0.638, SE = 0.162). For the same age, gut microbiota temporal volatility - an indicator for bacterial community instability - was not associated with sleep patterns. This study demonstrates promising short- and long-term links between gut microbiota diversity and sleep-wake rhythm maturation in infancy. Further research is needed to elucidate the mechanistic role of the gut microbiome in sleep development.

microbiology↗

bioRxiv2022

Reproducible acquisition, management, and meta-analysis of nucleotide sequence (meta)data using q2-fondue

The volume of public nucleotide sequence data has blossomed over the past two decades, enabling novel discoveries via re-analysis, meta-analyses, and comparative studies for uncovering general biological trends. However, reproducible re-use and management of sequence datasets remains a challenge. We created the software plugin q2-fondue to enable user-friendly acquisition, re-use, and management of public nucleotide sequence (meta)data while adhering to open data principles. The software allows fully provenance-tracked programmatic access to and management of data from the Sequence Read Archive (SRA). Sequence data and accompanying metadata retrieved with q2-fondue follow a validated format, which is interoperable with the QIIME 2 ecosystem and its multiple user interfaces. To highlight the manifold capabilities of q2-fondue, we present several demonstration analyses using amplicon, whole genome, and shotgun metagenome datasets. These use cases demonstrate how q2-fondue increases analysis reproducibility and transparency from data download to final visualizations by including source details in the integrated provenance graph. We believe q2-fondue will lower existing barriers to comparative analyses of nucleotide sequence data, enabling more transparent, open, and reproducible conduct of meta-analyses. q2-fondue is a Python 3 package released under the BSD 3-clause license at https://github.com/bokulich-lab/q2-fondue.

bioinformatics↗

bioRxiv2021

Single-cell transcriptome analysis of embryonic and adult endothelial cells allows to rank the hemogenic potential of post-natal endothelium

Hematopoietic stem cells are crucial for the continuous production of blood cells during life. The transplantation of these cells is one of the most common treatments to cure patient suffering of blood diseases. However, the lack of suitable donors is a major limitation. One option to get hematopoietic stem cells matching perfectly a patient is cellular reprogramming. Hematopoietic stem cells emerge from endothelial cells in blood vessels during embryogenesis through the endothelial to hematopoietic transition. Here, we used single-cell transcriptomics analysis to compare embryonic and post-natal endothelial cells to investigate the potential of adult vasculature to be reprogrammed in hematopoietic stem cells. Although transcriptional similarities have been found between embryonic and adult endothelial cells, we found some key differences in term of transcription factors expression. There is a deficit of expression of Runx1, Tal1, Lyl1 and Cbfb in adult endothelial cells compared to their embryonic counterparts. Using a combination of gene expression profiling and gene regulatory network analysis, we found that endothelial cells from the pancreas, brain, kidney and liver appear to be the most suitable targets for cellular reprogramming into hematopoietic stem cells. Overall, our work provides an important resource for the rational design of a reprogramming strategy for the generation of hematopoietic stem cells.

developmental biology↗

bioRxiv2019

Single-cell atlas of major haematopoietic tissues sheds light on blood cell formation from embryonic endothelium

The Yolk Sac (YS) and Aorta-Gonad-Mesonephros (AGM) are two major haematopoietic regions during embryonic development. Interestingly, AGM is the only one generating haematopoietic stem cells (HSCs). To identify the difference between AGM and YS, we compared them using single-cell RNA sequencing between 9.5 and 11.5 days of mouse embryonic development and identified cell populations using CONCLUS, a new computational tool. The AGM was the only one containing neurons and a specific mesenchymal population, while the YS major component was an epithelial population expressing liver marker genes. In addition, the YS contained a major endothelial population expressing Stab2, a hyaluronan receptor, also highly expressed by liver endothelium. We demonstrated that the YS haematopoietic potential was restricted to Stab2-negative cells and that ectopic expression of Stab2 could reduce blood cell formation from endothelium. Our results indicate that the AGM is a tissue more favourable to HSCs development than the YS because of its microenvironment and the nature of its endothelial cells.

developmental biology↗