Tissue-specific reprogramming of host tRNA transcriptome by the microbiome

Antibiotic-Induced Gut Microbiota Dysbiosis Modulates Host Transcriptome and m6A Epitranscriptome via Bile Acid Metabolism

Gut microbiota can influence host gene expression and physiology through metabolites. Besides, the presence or absence of gut microbiome can reprogram host transcriptome and epitranscriptome as represented by N6-methyladenosine (m6A), the most abundant mammalian mRNA modification. However, which and how gut microbiota-derived metabolites reprogram host transcriptome and m6A epitranscriptome remain poorly understood. Here, investigation is conducted into how gut microbiota-derived metabolites impact host transcriptome and m6A epitranscriptome using multiple mouse models and multi-omics approaches. Various antibiotics-induced dysbiotic mice are established, followed by fecal microbiota transplantation (FMT) into germ-free mice, and the results show that bile acid metabolism is significantly altered along with the abundance change in bile acid-producing microbiota. Unbalanced gut microbiota and bile acids drastically change the host transcriptome and the m6A epitranscriptome in multiple tissues. Mechanistically, the expression of m6A writer proteins is regulated in animals treated with antibiotics and in cultured cells treated with bile acids, indicating a direct link between bile acid metabolism and m6A biology. Collectively, these results demonstrate that antibiotic-induced gut dysbiosis regulates the landscape of host transcriptome and m6A epitranscriptome via bile acid metabolism pathway. This work provides novel insights into the interplay between microbial metabolites and host gene expression.

Comprehensive characterization of small noncoding RNA profiles in hypoxia-induced pulmonary hypertension (HPH) rat tissues

Hypoxia-induced pulmonary hypertension (HPH) is a fatal cardiovascular disease characterized by an elevation in pulmonary artery pressure, resulting in right ventricular dysfunction and eventual heart failure. Exploring the pathogenesis of HPH is crucial, and small noncoding RNAs (sncRNAs) are gaining recognition as potential regulators of cellular responses to hypoxia. In this study, we conducted a comprehensive analysis of sncRNA profiles in eight tissues of male HPH rats using high-throughput sequencing. Our study unveiled several sncRNAs, with the brain, kidney, and spleen exhibiting the highest abundance of microRNA (miRNA), tRNA-derived small RNA (tDR), and small nucleolar RNA (snoRNA), respectively. Moreover, we identified numerous tissue-specific and hypoxia-responsive sncRNAs, particularly miRNAs and tDRs. Interestingly, we observed arm switching in miRNAs under hypoxic conditions and a significant increase in the abundance of 5' tRNA-halves among the total tDRs during hypoxia. Overall, our study provides a comprehensive characterization of the sncRNA profiles in HPH rats.

Tyrosine transfer RNA levels and modifications during blood-feeding and vitellogenesis in the mosquito, Aedes aegypti

Mosquitoes such as Aedes aegypti must consume a blood meal for the nutrients necessary for egg production. Several transcriptome and proteome changes occur post blood meal that likely corresponds with codon usage alterations. Transfer RNA (tRNA) is the adapter molecule that reads messenger RNA (mRNA) codons to add the appropriate amino acid during protein synthesis. Chemical modifications to tRNA enhance codons' decoding, improving the accuracy and efficiency of protein synthesis. Here, we examined tRNA modifications and transcripts associated with the blood meal and subsequent periods of vitellogenesis in A. aegypti. More specifically, we assessed tRNA transcript abundance and modification levels in the fat body at critical times post blood-feeding. Based on a combination of alternative codon usage and identification of particular modifications, we identified that increased transcription of tyrosine tRNAs is likely critical during the synthesis of egg yolk proteins in the fat body following a blood meal. Altogether, changes in both the abundance and modification of tRNA are essential factors in the process of vitellogenin production after blood-feeding in mosquitoes.

Ushering in the era of tRNA medicines

Long viewed as an intermediary in protein translation, there is a growing awareness that tRNAs are capable of myriad other biological functions linked to human health and disease. These emerging roles could be tapped to leverage tRNAs as diagnostic biomarkers, therapeutic targets, or even as novel medicines. Furthermore, the growing array of tRNA-derived fragments, which modulate an increasingly broad spectrum of cellular pathways, is expanding this opportunity. Together, these molecules offer drug developers the chance to modulate the impact of mutations and to alter cell homeostasis. Moreover, because a single therapeutic tRNA can facilitate readthrough of a genetic mutation shared across multiple genes, such medicines afford the opportunity to define patient populations not based on their clinical presentation or mutated gene but rather on the mutation itself. This approach could potentially transform the treatment of patients with rare and ultrarare diseases. In this review, we explore the diverse biology of tRNA and its fragments, examining the past and present challenges to provide a comprehensive understanding of the molecules and their therapeutic potential.

Gut Dysbiosis in Experimental Kidney Disease: A Meta-Analysis of Rodent Repository Data

SIGNIFICANCE STATEMENT

Alterations in gut microbiota contribute to the pathophysiology of a diverse range of diseases, leading to suggestions that chronic uremia may cause intestinal dysbiosis that contributes to the pathophysiology of CKD. Various small, single-cohort rodent studies have supported this hypothesis. In this meta-analysis of publicly available repository data from studies of models of kidney disease in rodents, cohort variation far outweighed any effect of experimental kidney disease on the gut microbiota. No reproducible changes in animals with kidney disease were seen across all cohorts, although a few trends observed in most experiments may be attributable to kidney disease. The findings suggest that rodent studies do not provide evidence for the existence of "uremic dysbiosis" and that single-cohort studies are unsuitable for producing generalizable results in microbiome research.

BACKGROUND

Rodent studies have popularized the notion that uremia may induce pathological changes in the gut microbiota that contribute to kidney disease progression. Although single-cohort rodent studies have yielded insights into host-microbiota relationships in various disease processes, their relevance is limited by cohort and other effects. We previously reported finding metabolomic evidence that batch-to-batch variations in the microbiome of experimental animals are significant confounders in an experimental study.

METHODS

To attempt to identify common microbial signatures that transcend batch variability and that may be attributed to the effect of kidney disease, we downloaded all data describing the molecular characterization of the gut microbiota in rodents with and without experimental kidney disease from two online repositories comprising 127 rodents across ten experimental cohorts. We reanalyzed these data using the DADA2 and Phyloseq packages in R, a statistical computing and graphics system, and analyzed data both in a combined dataset of all samples and at the level of individual experimental cohorts.

RESULTS

Cohort effects accounted for 69% of total sample variance ( P <0.001), substantially outweighing the effect of kidney disease (1.9% of variance, P =0.026). We found no universal trends in microbial population dynamics in animals with kidney disease, but observed some differences (increased alpha diversity, a measure of within-sample bacterial diversity; relative decreases in Lachnospiraceae and Lactobacillus ; and increases in some Clostridia and opportunistic taxa) in many cohorts that might represent effects of kidney disease on the gut microbiota .

CONCLUSIONS

These findings suggest that current evidence that kidney disease causes reproducible patterns of dysbiosis is inadequate. We advocate meta-analysis of repository data as a way of identifying broad themes that transcend experimental variation.

Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality

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Developed tissue-specific codon optimality in 29 human tissues.

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Applied these to analyze synonymous mutations in ∼10,000 tumor and normal samples.

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Synonymous mutations frequently increase optimal codons in most cancer types.

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Synonymous mutations frequently increase optimal codons cell cycle-related genes.

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Frequency of optimal codon gain relates to proliferation, DDR deficiency, and survival.

Codon optimality has been demonstrated to be an important determinant of mRNA stability and expression levels in multiple model organisms and human cell lines. However, tissue-specific codon optimality has not been developed to investigate how codon optimality is usually perturbed by somatic synonymous mutations in human cancers. Here, we determined tissue-specific codon optimality in 29 human tissues based on mRNA expression data from the Genotype-Tissue Expression project. We found that optimal codons were associated with differentiation, whereas non-optimal codons were correlated with proliferation. Furthermore, codons biased toward differentiation displayed greater tissue specificity in codon optimality, and the tissue specificity of codon optimality was primarily present in amino acids with high degeneracy of the genetic code. By applying tissue-specific codon optimality to somatic synonymous mutations in 8532 tumor samples across 24 cancer types and to those in 416 normal cells across six human tissues, we found that synonymous mutations frequently increased optimal codons in tumor cells and cancer-related genes (e.g., genes involved in cell cycle). Furthermore, an elevated frequency of optimal codon gain was found to promote tumor cell proliferation in three cancer types characterized by DNA damage repair deficiency and could act as a prognostic biomarker for patients with triple-negative breast cancer. In summary, this study profiled tissue-specific codon optimality in human tissues, revealed alterations in codon optimality caused by synonymous mutations in human cancers, and highlighted the non-negligible role of optimal codon gain in tumorigenesis and therapeutics.

Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Simple Summary

Probiotics are a group of beneficial microorganisms that are symbionts of the human gut microbiome. The identification of new probiotics is therefore of paramount importance from both public health and commercial perspectives. In this study, we show for the first time that Artificial Intelligence algorithms can identify novel probiotics and also discriminate them from pathogenic organisms in the human gut. We were also able to determine the information content within tRNA sequences as the key genomic features capable of characterizing probiotics.

Abstract

Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

Cell type-specific analysis by single-cell profiling identifies a stable mammalian tRNA-mRNA interface and increased translation efficiency in neurons

The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using available mouse and human single-cell RNA-sequencing atlases. After showing the robustness of ATAC-sequencing measurements for the analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in both mouse and human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons, which clustered separately from other cell types. Integration of these data sets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of tRNA^Ala (AGC) anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell type–specific ribosome profiling, suggesting that the reduction of tRNA^Ala (AGC) anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency resolved at the cell-type level with single-cell information, identifies a conserved landscape of translation elongation across mammalian cellular diversity and evolution.

5' Half of specific tRNAs feeds back to promote corresponding tRNA gene transcription in vertebrate embryos

5′tRFls are small transfer RNA (tRNA) fragments derived from 5′ half of mature tRNAs. However, it is unknown whether 5′tRFls could feed back to regulate tRNA biogenesis. Here, we show that 5′tRFl^Gly/GCC and 5′tRFl^Glu/CTC function to promote transcription of corresponding tRNA genes and are essential for vertebrate early embryogenesis. During zebrafish embryogenesis, dynamics of 5′tRFl^Gly/GCC and 5′tRFl^Glu/CTC levels correlates with that of tRNA^Gly/GCC and tRNA^Glu/CTC levels. Morpholino-mediated knockdown of 5′tRFl^Gly/GCC or 5′tRFl^Glu/CTC down-regulates tRNA^Gly/GCC or tRNA^Glu/CTC levels, respectively, and causes embryonic lethality that is efficiently rescued by coinjection of properly refolded corresponding tRNA. In zebrafish embryos, tRNA:DNA and 5′tRFl:DNA hybrids commonly exist on the template strand of tRNA genes. Mechanistically, unstable 5′tRFl:DNA hybrid may prevent the formation of transcriptionally inhibitory stable tRNA:DNA hybrids on the same tRNA loci so as to facilitate tRNA gene transcription. The uncovered mechanism may be implicated in other physiological and pathological processes.