Unifying Gene Expression Complexity and Cellular Operational Efficiency: Application of the Locality and Caching Principles via a Cell-to-Computer Analogy

Gene expression is time-consuming, and the delay from transcription activation to produced proteins is sequentially longer from bacteria to yeast and to humans. How human cells bypass the delay and attain operational efficiency, i.e., quick proteomic response to signals, is not well understood. The computer has endured the same system latency issue due to much slower information retrieval (hard drive (HD) to memory and to CPU) than CPU execution, and mitigated it via efficient memory management, namely, the spatiotemporal locality principles that control specialized user functions and the permanent caching of core system functions, the operating system (OS) kernel. Thus, in this study, we unified gene expression and HD-memory-CPU information flow as instances of the Shannon information theory, both supporting the respective system operations and consisting of three components: information storage, the execution/decoding step, and the channel for the dynamic storage-to-execution information flow; the gene expression machinery and their regulators, and the OS kernel, were deemed as the respective channels. This abstraction prompted a multi-omic comparative analysis, generating experimental evidence that transcriptome regulation shares the computer memory management principles. First, the temporal locality principle explains the mRNA stabilization-by-translation regulatory mechanism and controls specialized cellular functions. Second, the caching principle explains cytoplasmic mRNA sequestration and the defiance of the locality principle by highly sequestered mRNAs. Third, strikingly, in both systems, the caching principle controls the information channels; similar to permanent caching of OS kernel, basic translation/transcription machinery and their regulators are the top most sequestered mRNAs. Summarily, the locality and the caching principles differentially regulate specialized functions and core system functions, respectively, integrating the complexity of transcriptome regulation with cellular operational latency mitigation.

Effects of the Expression of Random Sequence Clones on Growth and Transcriptome Regulation in Escherichia coli

Comparative genomic analyses have provided evidence that new genetic functions can emerge out of random nucleotide sequences. Here, we apply a direct experimental approach to study the effects of plasmids harboring random sequence inserts under the control of an inducible promoter. Based on data from previously described experiments dealing with the growth of clones within whole libraries, we extracted specific clones that had shown either negative, neutral or positive effects on relative cell growth. We analyzed these individually with respect to growth characteristics and the impact on the transcriptome. We find that candidate clones for negative peptides lead to growth arrest by eliciting a general stress response. Overexpression of positive clones, on the other hand, does not change the exponential growth rates of hosts, and they show a growth advantage over a neutral clone when tested in direct competition experiments. Transcriptomic changes in positive clones are relatively moderate and specific to each clone. We conclude from our experiments that random sequence peptides are indeed a suitable source for the de novo evolution of genetic functions.

Effects of the expression of random sequence clones on growth and transcriptome regulation in Escherichia coli

Comparative genomic analyses have provided evidence that new genetic functions can emerge out of random nucleotide sequences. Here, we apply a direct experimental approach to study the effects of plasmids harbouring random sequence inserts under the control of an inducible promoter. Based on data from previously described experiments dealing with the growth of clones within whole libraries, we extracted specific clones that had shown either negative or positive effects on relative cell growth. We analysed these individually with respect to growth characteristics and impact on the transcriptome. We find that candidate clones for negative peptides lead to growth arrest by eliciting a general stress response. Overexpression of positive clones, on the other hand, does not change the exponential growth rates of hosts, but they show a growth advantage over a neutral candidate clone when tested in direct competition experiments. Transcriptomic changes in positive clones are relatively moderate and specific to each clone. We conclude from our experiments that random sequence peptides are in-deed a suitable source for the de novo evolution of genetic functions.

NF1-Dependent Transcriptome Regulation in the Melanocyte Lineage and in Melanoma

The precise role played by the tumor suppressor gene NF1 in melanocyte biology and during the transformation into melanoma is not completely understood. In particular, understanding the interaction during melanocyte development between NF1 and key signaling pathways, which are known to be reactivated in advanced melanoma, is still under investigation. Here, we used RNAseq datasets from either situation to better understand the transcriptomic regulation mediated by an NF1 partial loss of function. We found that NF1 mutations had a differential impact on pluripotency and on melanoblast differentiation. In addition, major signaling pathways such as VEGF, senescence/secretome, endothelin, and cAMP/PKA are likely to be upregulated upon NF1 loss of function in both melanoblasts and metastatic melanoma. In sum, these data bring new light on the transcriptome regulation of the NF1-mutated melanoma subgroup and will help improve the possibilities for specific treatment.

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

Nuclear m6A reader YTHDC1 regulates the scaffold function of LINE1 RNA in mouse ESCs and early embryos

N6-methyladenosine (m6A) on chromosome-associated regulatory RNAs (carRNAs), including repeat RNAs, plays important roles in tuning the chromatin state and transcription, but the intrinsic mechanism remains unclear. Here, we report that YTHDC1 plays indispensable roles in the self-renewal and differentiation potency of mouse embryonic stem cells (ESCs), which highly depends on the m6A-binding ability. Ythdc1 is required for sufficient rRNA synthesis and repression of the 2-cell (2C) transcriptional program in ESCs, which recapitulates the transcriptome regulation by the LINE1 scaffold. Detailed analyses revealed that YTHDC1 recognizes m6A on LINE1 RNAs in the nucleus and regulates the formation of the LINE1-NCL partnership and the chromatin recruitment of KAP1. Moreover, the establishment of H3K9me3 on 2C-related retrotransposons is interrupted in Ythdc1-depleted ESCs and inner cell mass (ICM) cells, which consequently increases the transcriptional activities. Our study reveals a role of m6A in regulating the RNA scaffold, providing a new model for the RNA-chromatin cross-talk.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

The pharmacodynamic and differential gene expression analysis of PPAR α/δ agonist GFT505 in CDAHFD-induced NASH model

Peroxisome proliferator-activated receptor α/δ (PPAR α/δ), regulating glucolipid metabolism and immune inflammation, has been identified as an effective therapeutic target in non-alcoholic steatohepatitis (NASH). Dual PPAR α/δ agonist, such as GFT505 (also known as elafibranor), demonstrated potential therapeutic effect for NASH in clinical trials. To profile the regulatory network of PPAR α/δ agonist in NASH, the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) induced NASH model was used to test the pharmacodynamics and transcriptome regulation of GFT505 in this study. The results showed that GFT505 ameliorated hepatic steatosis, inflammation and fibrosis in CDAHFD mice model. RNA-sequencing yielded 3995 up-regulated and 3576 down-regulated genes with GFT505 treatment. And the most significant differentialy expressed genes involved in glucolipid metabolism (Pparα, Acox1, Cpt1b, Fabp4, Ehhadh, Fabp3), inflammation (Ccl6, Ccl9, Cxcl14) and fibrosis (Timp1, Lamc3, Timp2, Col3a1, Col1a2, Col1a1, Hapln4, Timp3, Pik3r5, Pdgfα, Pdgfβ, Tgfβ1, Tgfβ2) were confirmed by RT-qPCR. The down-regulated genes were enriched in cytokine-cytokine receptor interaction pathway and ECM-receptor interaction pathway, while the up-regulated genes were enriched in PPAR signaling pathway and fatty acid degradation pathway. This study provides clues and basis for further understanding on the mechanism of PPAR α/δ agonist on NASH.

Temozolomide-Induced RNA Interactome Uncovers Novel LncRNA Regulatory Loops in Glioblastoma

Resistance to chemotherapy by temozolomide (TMZ) is a major cause of glioblastoma (GBM) recurrence. So far, attempts to characterize factors that contribute to TMZ sensitivity have largely focused on protein-coding genes, and failed to provide effective therapeutic targets. Long noncoding RNAs (lncRNAs) are essential regulators of epigenetic-driven cell diversification, yet, their contribution to the transcriptional response to drugs is less understood. Here, we performed RNA-seq and small RNA-seq to provide a comprehensive map of transcriptome regulation upon TMZ in patient-derived GBM stem-like cells displaying different drug sensitivity. In a search for regulatory mechanisms, we integrated thousands of molecular associations stored in public databases to generate a background “RNA interactome”. Our systems-level analysis uncovered a coordinated program of TMZ response reflected by regulatory circuits that involve transcription factors, mRNAs, miRNAs, and lncRNAs. We discovered 22 lncRNAs involved in regulatory loops and/or with functional relevance in drug response and prognostic value in gliomas. Thus, the investigation of TMZ-induced gene networks highlights novel RNA-based predictors of chemosensitivity in GBM. The computational modeling used to identify regulatory circuits underlying drug response and prioritizing gene candidates for functional validation is applicable to other datasets.

Transcriptome Regulation of Carotenoids in Five Flesh-Colored Watermelon (Citrullus lanatus)

Background: Fruit flesh color in watermelon (Citrullus lanatus) is a great index for evaluation of the appearance quality and a key contributor influencing consumers preferences, but the molecular mechanisms of this intricate trait remain largely unknown. Here, the carotenoids and transcriptome dynamics during fruit development in watermelon cultivars with 5 different flesh colors were analyzed.Results: A total of 13 carotenoids and 16,781 differentially expressed genes (DEGs) including 1,295 transcription factors (TFs) were detected during the development of five watermelon genotypes. A number of structural genes and transcription factors were found to be involved in the carotenoid biosynthesis pathway using comparative transcriptome analysis. Furthermore, we performed weighted gene co-expression network analysis and predicted hub genes in 6 main modules determining carotenoids contents. Cla018406 (a Chaperone protein dnaJ-like protein) maybe a candidate gene for β-carotene and highly expressed in orange flesh colored fruit. Cla007686 (a zinc finger CCCH domain-containing protein) was highly expressed in the red color watermelon, maybe a key regulator for lycopene accumulation. Cla003760 (merbrane protein) and Cla007686 (photosystenI reaction center subunit II) are predicted to be hub genes and play an important role in yellow flesh color formation.Conclusions: These results provide an important resource for dissecting the molecular basis and candidate genes governing flesh color formation in watermelon fruit.