Biology subjects

Arıkan, M.

Publications and source records attributed to Arıkan, M..

3 recordsLinked to original sources

bioRxiv2025

Gut Microbiota Alterations Across REM Sleep Behavior Disorder and Parkinson's Disease: A Machine Learning-Based Meta-Analysis

Recent studies have examined the relationship between rapid eye movement sleep behavior disorder (RBD), Parkinsons disease (PD), and the gut microbiota, but no consensus exists on the shared and distinct gut microbiota changes. This study aimed to identify consistent and divergent gut microbiota changes across RBD and PD and to evaluate the performance of machine learning (ML) models in distinguishing PD, RBD, and healthy controls (HC). A meta-analysis of four gut microbiota studies involving PD, RBD, and HC groups was conducted, comprising a total of 973 samples (379 PD, 251 RBD, and 343 HC). ML models could differentiate PD from HC (cross study validation (CSV) AUC 0.61 {+/-} 0.06) and RBD from HC (CSV AUC 0.58 {+/-} 0.03). However, distinguishing between PD and RBD was ineffective (CSV AUC 0.51 {+/-} 0.03). ML models distinguished PD and RBD from HC with weak to moderate predictive accuracy but failed to differentiate PD from RBD.

microbiology↗

bioRxiv2023

gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

BackgroundOver the past few years, the rise of omics technologies has offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to offer a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline specifically tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. ResultsHere, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from two to four distinct omics data types including 16S rRNA gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration and visualization approaches, all aimed at providing a more comprehensive toolkit and insightful analysis of microbiomes. The functionality of these new features is showcased through the use of four microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. ConclusionsgNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, opening up avenues for novel insights in the field of both host associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.

bioinformatics↗

bioRxiv2023

Characterization of the root-associated microbiome provides insights into endemism of Thymus species growing in the Kazdagi National Park

Plant associated microbiomes have a large impact on the fitness of the plants in the particular environmental conditions. The root associated microbiomes are shaped by the interactions between the microbial community members, their plant host, and environmental factors. Hence, further understanding of the composition and functions of the plant root associated microbiomes can pave the way for the development of more effective conservation strategies for endangered endemic plants. Here, we characterized the bacterial and fungal microbiomes in bulk and rhizosphere soil of an endemic and a non-endemic Thymus species from Kazdagi National Park, Turkiye, Thymus pulvinatus and Thymus longicaulis subsp. chaubardii, respectively, by 16S rRNA gene and ITS amplicon sequencing. Our findings revealed no significant differences in alpha diversity between plant species and soil types. However, we found that the bacterial microbiome profiles differentiate not only Thymus species but also soil types while fungal microbiome profiles show distinct profiles particularly between the species in beta diversity. Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi members form the core bacterial microbiome while the fungal core microbiome consists of Ascomycota and Basidiomycota members in both Thymus species. Moreover, we identified the association of the bacterial taxa contributing to the biogeochemical cycles of carbon and nitrogen and providing the stress resistance with the rhizosphere soil of endemic T. pulvinatus. In addition, functional predictions suggested distinct enriched functions in rhizosphere soil samples of the two plant species. Also, employing an exploratory integrative analysis approach, we determined the plant species-specific nature of transkingdom interactions in two Thymus species.

ecology↗