Biology subjects

Rahmanian, S.

Publications and source records attributed to Rahmanian, S..

7 recordsLinked to original sources

bioRxiv2026

Tree diversity and mycorrhizal associations jointly regulate seasonal canopy cooling in forests

The increasing frequency and intensity of heatwaves under climate change highlight the need to understand how biodiversity regulates forest canopy thermal dynamics. Although tree diversity can buffer the microclimate, its effects on canopy temperature and the role of mycorrhizal symbioses remain unclear. We addressed this question in the MyDiv tree diversity experiment in Germany, where tree species richness (1-, 2-, and 4-species mixtures) and mycorrhizal types (arbuscular, ectomycorrhizal, and mixed) are factorially manipulated. During the 2024 growing season, we conducted nine uncrewed aerial vehicle (UAV) surveys using integrated thermal and LiDAR sensors to quantify canopy temperature and structural complexity, together with measurements of leaf water content, specific leaf area, soil moisture, and vapour pressure deficit (VPD). Increasing tree diversity generally reduced canopy temperature, although the strength of this relationship varied seasonally and among mycorrhizal types. Cooling effects were strongest during peak summer heat and were more pronounced in arbuscular mycorrhizal (AM) communities than in ectomycorrhizal (EM) and mixed (AM+EM) communities. In contrast, EM communities exhibited greater canopy structural complexity, whereas AM communities maintained higher soil and leaf water content. Structural complexity increased with tree diversity but did not necessarily result in greater canopy cooling. Structural equation modelling revealed that forest thermal buffering emerged through complementary structural and hydraulic pathways, whose relative importance shifted seasonally, with hydraulic regulation becoming increasingly important under hotter and drier conditions. By linking canopy temperature, canopy structure, and plant water relations, our study provides mechanistic insights for understanding how multiple facets of biodiversity regulate forest thermal buffering under climate warming.

ecology↗

bioRxiv2023

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3 end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3 processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

genomics↗

bioRxiv2023

The ENCODE Uniform Analysis Pipelines

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses. Database URL: https://www.encodeproject.org/

bioinformatics↗

bioRxiv2020

Long-TUC-seq is a robust method for quantification of metabolically labeled full-length isoforms

The steady state expression of each gene is the result of a dynamic transcription and degradation of that gene. While regular RNA-seq methods only measure steady state expression levels, RNA-seq of metabolically labeled RNA identifies transcripts that were transcribed during the window of metabolic labeling. Whereas short-read RNA sequencing can identify metabolically labeled RNA at the gene level, long-read sequencing provides much better resolution of isoform-level transcription. Here we combine thiouridine-to-cytosine conversion (TUC) with PacBio long-read sequencing to study the dynamics of mRNA transcription in the GM12878 cell line. We show that using long-TUC-seq, we can detect metabolically labeled mRNA of distinct isoforms more reliably than using short reads. Long-TUC-seq holds the promise of capturing isoform dynamics robustly and without the need for enrichment.

genomics↗

bioRxiv2020

The biomedical sensor Cell-Fit-HD4D, reveals individual tumor cell fate in response to microscopic ion deposition.

Here we present the biomedical sensor cell-fluorescent ion track hybrid detector4D (Cell-Fit-HD4D) to reveal individual tumor cell fate in response to microscopic ion deposition in ion beam therapy. The sensor enables long-term monitoring of single tumor cells after clinical ion beam irradiation in combination with single-cell dosimetry. Cell-Fit-HD4D is read out in-situ by conventional optical microscopy. Direct visualization of a clinical ion beam is hereby possible for the first time. The possibility to reveal fate of individual cells from a cell cohort demonstrates that our biomedical sensor clearly differs from conventional experiments that characterize cellular response after radiation on a population level. Cell-Fit-HD4D is therefore used to mimics the clinical situation of a defined tumor depth during tumor treatment by ion beam therapy. Our biomedical sensor is able to provide crucial input for current mechanistic approaches to biophysical modelling of the effect of ionizing radiation on biological matter. In the clinical context, obtaining multi-dimensional physical and biological information on individual tumor cells is an important step to further transform ion beam therapy into a highly precise discipline within oncology.

cancer biology↗

bioRxiv2019

A technology-agnostic long-read analysis pipeline for transcriptome discovery and quantification

Alternative splicing is widely acknowledged to be a crucial regulator of gene expression and is a key contributor to both normal developmental processes and disease states. While cost-effective and accurate for quantification, short-read RNA-seq lacks the ability to resolve full-length transcript isoforms despite increasingly sophisticated computational methods. Long-read sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) bypass the transcript reconstruction challenges of short reads. Here we introduce TALON, the ENCODE4 pipeline for platform-independent analysis of long-read transcriptomes. We apply TALON to the GM12878 cell line and show that while both PacBio and ONT technologies perform well at full-transcript discovery and quantification, each displayed distinct technical artifacts. We further apply TALON to mouse hippocampus and cortex transcriptomes and find that 422 genes found in these regions have more reads associated with novel isoforms than with annotated ones. We demonstrate that TALON is a capable of tracking both known and novel transcript models as well as their expression levels across datasets for both simple studies and in larger projects. These properties will enable TALON users to move beyond the limitations of short-read data to perform isoform discovery and quantification in a uniform manner on existing and future long-read platforms.

genomics↗

bioRxiv2018

Dynamics of microRNA expression during mouse prenatal development

MicroRNAs (miRNAs) play a critical role as post-transcriptional regulators of gene expression. The ENCODE project profiled the expression of miRNAs in a comprehensive set of tissues during a time-course of mouse embryonic development and captured the expression dynamics of 785 miRNAs. We found distinct tissue and developmental stage specific miRNA expression clusters, with an overall pattern of increasing tissue specific expression as development proceeds. Comparative analysis of conserved miRNAs in mouse and human revealed stronger clustering of expression patterns by tissue types rather than by species. An analysis of messenger RNA gene expression clusters compared with miRNA expression clusters identifies the potential role of specific miRNA expression clusters in suppressing the expression of mRNAs specific to other developmental programs in the tissue where these microRNAs are expressed during embryonic development. Our results provide the most comprehensive timecourse of miRNA expression as an integrated part of the ENCODE reference dataset for mouse embryonic development.

genomics↗