MatK impacts differential chloroplast translation by limiting spliced tRNA-K(UUU) abundance.

The protein levels of chloroplast photosynthetic genes and genes related to the chloroplast genetic apparatus vary to adapt to different conditions. However, the underlying mechanisms governing these variations remain unclear. The chloroplast intron Maturase K is encoded within the trnK intron and has been suggested to be required for splicing several group IIA introns, including the trnK intron. In this study, we used RNA immunoprecipitation followed by high-throughput sequencing (RIP-Seq) to identify MatK's preference for binding to group IIA intron domains I and VI within target transcripts. Importantly, these domains are crucial for splice site selection, and we discovered alternative 5'-splice sites in three MatK target introns. The resulting alternative trnK lariat structure showed increased accumulation during heat acclimation. The cognate codon of tRNA-K(UUU) is highly enriched in mRNAs encoding ribosomal proteins and a trnK-matK over-expressor exhibited elevated levels of the spliced tRNA-K(UUU). Ribosome profiling analysis of the overexpressor revealed a significant up-shift in the translation of ribosomal proteins compared to photosynthetic genes. Our findings suggest the existence of a novel regulatory mechanism linked to the abundance of tRNA-K(UUU), enabling the differential expression of functional chloroplast gene groups.

Liam tackles complex multimodal single-cell data integration challenges.

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Genome-wide screening identifies Trim33 as an essential regulator of dendritic cell differentiation.

The development of dendritic cells (DCs), including antigen-presenting conventional DCs (cDCs) and cytokine-producing plasmacytoid DCs (pDCs), is controlled by the growth factor Flt3 ligand (Flt3L) and its receptor Flt3. We genetically dissected Flt3L-driven DC differentiation using CRISPR-Cas9-based screening. Genome-wide screening identified multiple regulators of DC differentiation including subunits of TSC and GATOR1 complexes, which restricted progenitor growth but enabled DC differentiation by inhibiting mTOR signaling. An orthogonal screen identified the transcriptional repressor Trim33 (TIF-1γ) as a regulator of DC differentiation. Conditional targeting in vivo revealed an essential role of Trim33 in the development of all DCs, but not of monocytes or granulocytes. In particular, deletion of Trim33 caused rapid loss of DC progenitors, pDCs, and the cross-presenting cDC1 subset. Trim33-deficient Flt3+ progenitors up-regulated pro-inflammatory and macrophage-specific genes but failed to induce the DC differentiation program. Collectively, these data elucidate mechanisms that control Flt3L-driven differentiation of the entire DC lineage and identify Trim33 as its essential regulator.

How to find genomic regions relevant for gene regulation.

Genetic variants associated with human diseases are often located outside the protein coding regions of the genome. Identification and functional characterization of the regulatory elements in the non-coding genome is therefore of crucial importance for understanding the consequences of genetic variation and the mechanisms of disease. The past decade has seen rapid progress in high-throughput analysis and mapping of chromatin accessibility, looping, structure, and occupancy by transcription factors, as well as epigenetic modifications, all of which contribute to the proper execution of regulatory functions in the non-coding genome. Here, we review the current technologies for the definition and functional validation of non-coding regulatory regions in the genome.