Auxin responsive elements (AuxRE) were found in upstream regions of target genes for ARFs (Auxin response factors). While Chip-seq data for most of ARFs are still unavailable, prediction of potential AuxRE is restricted by consensus models that detect too many false positive sites. Using sequence analysis of experimentally proven AuxREs, we revealed both an extended nucleotide context pattern for AuxRE itself and three distinct types of its coupling motifs (Y-patch, AuxRE-like, and ABRE-like), which together with AuxRE may form the composite elements. Computational analysis of the genome-wide distribution of the predicted AuxREs and their impact on auxin responsive gene expression allowed us to conclude that: (1) AuxREs are enriched around the transcription start site with the maximum density in 5'UTR; (2) AuxREs mediate auxin responsive up-regulation, not down-regulation. (3) Directly oriented single AuxREs and reverse multiple AuxREs are mostly associated with auxin responsiveness. In the composite AuxRE elements associated with auxin response, ABRE-like and Y-patch are 5'-flanking or overlapping AuxRE, whereas AuxRE-like motif is 3'-flanking. The specificity in location and orientation of the coupling elements suggests them as potential binding sites for ARFs partners.

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.

Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies GO terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g. weakly, moderately or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, SEA and GSEA. These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server https://webfsgor.sysbio.cytogen.ru/ .

Dioxins are one of the most potent anthropogenic poisons, causing systemic disorders in embryonic development and pathologies in adults. The mechanism of dioxin action requires an aryl hydrocarbon receptor (AhR), but the downstream mechanisms are not yet precisely clear. Here, we performed a meta-analysis of all available transcriptome datasets taken from human cell cultures exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Differentially expressed genes from different experiments overlapped partially, but there were a number of those genes that were systematically affected by TCDD. Some of them have been linked to toxic dioxin effects, but we also identified other attractive targets. Among the genes that were affected by TCDD, there are functionally related gene groups that suggest an interplay between retinoic acid, AhR, and Wnt signaling pathways. Next, we analyzed the upstream regions of differentially expressed genes and identified potential transcription factor (TF) binding sites overrepresented in the genes responding to TCDD. Intriguingly, the dioxin-responsive element (DRE), the binding site of AhR, was not overrepresented as much as other cis-elements were. Bioinformatics analysis of the AhR binding profile unveils potential cooperation of AhR with E2F2, CTCFL, and ZBT14 TFs in the dioxin response. We discuss the potential implication of these predictions for further dioxin studies.

The problem of isolating high-quality total RNA from intervertebral discs has no recognized solution yet. This is due to the extremely low content of live cells in the samples and the voluminous intercellular matrix. A variety of published protocols focused on isolating RNA from articular cartilage have recommended the use of expensive equipment, enzymatic matrix cleavage, or cell culture. In our study, we used a combination of the traditional QIAzol protocol (Qiagen, Germany) and RNEasy column purification (Qiagen, Germany) to obtain high-quality RNA from post-surgical intervertebral disc fragments. Only a mortar and a pestle were used for grinding, making our method particularly accessible. The isolated RNA with a RIN of ~7 is suitable for studying the expression profile of chondrocytes in situ. RNA-seq analysis of three samples demonstrated cell type ratios to be mostly relevant to intervertebral disc tissues, with over 70% of the chondrocytes of the three subtypes having an admixture of blood-related cells.

Germinal center (GC)-derived lymphomas arise within a specialized immune ecosystem, yet whether malignant cells direct its remodeling remains unclear. Integrating genomic, transcriptomic, spatial, and functional analyses across follicular lymphoma and diffuse large B-cell lymphoma, we define a developmental framework linking malignant B-cell differentiation state to tumor microenvironment (TME) organization. Rather than segregating by histology, lymphomas align along a shared differentiation continuum in which proliferative dark zone (DZ) states associate with immune-depleted TMEs, whereas post-GC/memory B-cell (MBC) states drive inflamed but immunosuppressed TMEs. Spatial profiling reveals collapse of GC architecture and emergence of suppressive myeloid niches along this trajectory. Single-cell lineage reconstruction uncovers a DZ-to-MBC developmental axis underlying lymphoma evolution. Genetic reprogramming in vivo demonstrates that malignant differentiation state is sufficient to instruct TME remodeling. Collectively, these findings establish malignant differentiation state as a causal determinant of immune architecture across GC-derived lymphomas and identify lineage-directed immune reprogramming as a potential therapeutic strategy.