Hematopoietic Differentiation from Single Codon Perspective
Authors: Külp Marius , Jolly Adrien , Schmachtel Tessa , Häupl Björn , Oellerich Thomas , Bonig Halvard , Rieger Michael A. 
Published: December 2024

The genetic code determines RNA and protein structure and function through a versatile composition of only 64 codons. Despite representing the code of life as a central dogma of biology, codon quantities remain mostly unappreciated by the scientific community. We established a precision biology approach to follow the genetic code from genome to proteome, considering the coding transcriptome (ORFeome), the transfer RNA (tRNA) transcriptome, and the proteome. These different layers of information are integrated and compared between cell populations at different stages of hematopoiesis to characterize hematopoietic differentiation at single codon resolution.

As tRNAs deliver amino acids to the protein translation machinery and thereby link the ORFeome with the proteome of a cell, differential tRNA expression needs to be considered to reconstruct translational efficiency. Our integrative approach calculates the demand and supply of tRNAs based on matched mRNA- and tRNA-sequencing and quantitative proteomics of FACS-sorted hematopoietic stem cells (HSCs), oligopotent progenitors, and T cells derived from healthy human donors.

To quantify the tRNA demand at codon and gene level, the transcriptional codon usage (tCU) was established by integrating mRNA abundance with the ORFeome for each cell population. The tCU describes codons per million mRNA transcripts (cpmt) and was defined codon-wise and gene-wise to enable uncoupling of gene and codon annotation while preserving their assignment.

Analysis of the global tCU distribution demonstrated that in hematopoietic stem and progenitor cells (HSPCs), not differentially expressed genes exhibit the highest tCUs, while in T cells, proteomically and transcriptionally upregulated genes are among those with highest tCUs. This indicates that the tRNA demands of both cell types are highest for different cellular processes.

The tRNA supply was assessed using low input tRNA-sequencing. Strikingly, principal component analysis solely based on tRNA gene expression enabled HSPC and T cell cluster separation, characterizing the tRNA profile to be indicative for cell identity.

Differential expression analysis identified HSC-, progenitor- and T cell-exclusive tRNA isotypes. Importantly, of 25 differentially expressed isotypes between progenitors and T cells were three tRNA-Lys-CTT isodecoders and three tRNA-Ala-AGC isodecoders, both strongly upregulated in progenitors. We integrated the expression of these recurrent isodecoders with the tCU of their corresponding codons AAG and GCT, respectively, and found no significant difference, meaning that for these codons, T cells possess a disadvantageous ratio of tRNA and codon abundance compared to progenitors.

Aminoacyl-tRNA-synthetases (ARS) charge tRNAs with their cognate amino acid prior to their delivery to the translation machinery. Subsequently, tRNAs can be re-charged by ARS enzymes to induce further rounds of amino acid transport. To take this process into account, we assessed the ARSome of HSPCs and T cells using differential protein expression analysis. Unexpectedly, we found 13 of 20 cytoplasmic ARS enzymes differentially expressed, three T cell-enriched and 10 HSPC-enriched, adding another layer of regulation to our analysis.

As the lysine transferring enzyme KARS and the alanine charger AARS were not differentially expressed, potential compensation in T cells where lower tRNA abundance could be counterbalanced by increased charging rates, can thus be excluded. Vice versa, this mechanism could affect translational fidelity for codons whose tRNAs are not differentially expressed along hematopoietic differentiation.

Thus, integration of the human ORFeome, mRNA- and tRNA transcriptomes, proteome and ARSome identified the codons AAG and GCT as differentiation-dependent bottlenecks within the hematopoietic system. We are now establishing a machine learning approach to identify further codon patterns with translational consequences.

In conclusion, our original study describes differential tRNA expression within the hematopoietic hierarchy, presents a novel approach to track codons from transcription to translation, and sheds light on previously unrecognized mechanisms of translational control potentially driving cell state and specification.