Monitoring mammalian mitochondrial translation with MitoRiboSeq

Advances in ribosome profiling technologies

Ribosome profiling (or Ribo-seq) has emerged as a powerful approach for revealing the regulatory mechanisms of protein synthesis, on the basis of deep sequencing of ribosome footprints. Recent innovations in Ribo-seq technologies have significantly enhanced their sensitivity, specificity, and resolution. In this review, we outline emerging Ribo-seq derivatives that overcome barriers in low inputs, rRNA contamination, data calibration, and single-cell applications. These advances enable detailed insights into translational control across diverse biological contexts.

Identification of mitochondrial energy metabolism genes associated with obstructive sleep apnea syndrome: integrated bioinformatics analysis

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder, which is closely related to abnormal mitochondrial energy metabolism in recent years. By analyzing gene expression data of OSAS, we identified differentially expressed genes (DEGs) related to mitochondrial energy metabolism, and further explored the function of NDUFA10 in OSAS and its potential diagnostic value. In this paper, we download OSAS related expression data from a public database and preprocess the data set using a standardized process. Gene set enrichment analysis (GSEA) was used to assess pathways associated with mitochondrial metabolism, and diagnostic models were constructed to assess the expression of key genes. ROC curve analysis was performed for common mitochondrial energy metabolism-related differentially expressed genes (Co-MEMRDEGs) and gene interaction networks were constructed. After data standardization, significantly differentially expressed genes were identified, among which NDUFA10 was identified as one of the genes most associated with OSAS. The constructed diagnostic model showed good prediction accuracy, and ROC curve analysis further verified its clinical diagnostic potential.

Mitochondrial genetics, signalling and stress responses

Mitochondria are multifaceted organelles with crucial roles in energy generation, cellular signalling and a range of synthesis pathways. The study of mitochondrial biology is complicated by its own small genome, which is matrilineally inherited and not subject to recombination, and present in multiple, possibly different, copies. Recent methodological developments have enabled the analysis of mitochondrial DNA (mtDNA) in large-scale cohorts and highlight the far-reaching impact of mitochondrial genetic variation. Genome-editing techniques have been adapted to target mtDNA, further propelling the functional analysis of mitochondrial genes. Mitochondria are finely tuned signalling hubs, a concept that has been expanded by advances in methodologies for studying the function of mitochondrial proteins and protein complexes. Mitochondrial respiratory complexes are of dual genetic origin, requiring close coordination between mitochondrial and nuclear gene-expression systems (transcription and translation) for proper assembly and function, and recent findings highlight the importance of the mitochondria in this bidirectional signalling.

Unraveling the roles and mechanisms of mitochondrial translation in normal and malignant hematopoiesis

Due to spatial and genomic independence, mitochondria possess a translational mechanism distinct from that of cytoplasmic translation. Several regulators participate in the modulation of mitochondrial translation. Mitochondrial translation is coordinated with cytoplasmic translation through stress responses. Importantly, the inhibition of mitochondrial translation leads to the inhibition of cytoplasmic translation and metabolic disruption. Therefore, defects in mitochondrial translation are closely related to the functions of hematopoietic cells and various immune cells. Finally, the inhibition of mitochondrial translation is a potential therapeutic target for treating multiple hematologic malignancies. Collectively, more in-depth insights into mitochondrial translation not only facilitate our understanding of its functions in hematopoiesis, but also provide a basis for the discovery of new treatments for hematological malignancies and the modulation of immune cell function.

Calibrated ribosome profiling assesses the dynamics of ribosomal flux on transcripts

Ribosome profiling, which is based on deep sequencing of ribosome footprints, has served as a powerful tool for elucidating the regulatory mechanism of protein synthesis. However, the current method has substantial issues: contamination by rRNAs and the lack of appropriate methods to measure ribosome numbers in transcripts. Here, we overcome these hurdles through the development of "Ribo-FilterOut", which is based on the separation of footprints from ribosome subunits by ultrafiltration, and "Ribo-Calibration", which relies on external spike-ins of stoichiometrically defined mRNA-ribosome complexes. A combination of these approaches estimates the number of ribosomes on a transcript, the translation initiation rate, and the overall number of translation events before its decay, all in a genome-wide manner. Moreover, our method reveals the allocation of ribosomes under heat shock stress, during aging, and across cell types. Our strategy of modified ribosome profiling measures kinetic and stoichiometric parameters of cellular translation across the transcriptome.

A novel mitochondrial metabolism-related gene signature for predicting the prognosis of oesophageal squamous cell carcinoma

Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers worldwide. Due to the important role of mitochondrial metabolism in cancer progression, a clinical prognostic model based on mitochondrial metabolism and clinical features was constructed in this study to predict the prognosis of ESCC. Firstly, the mitochondrial metabolism scores (MMs) were calculated based on 152 mitochondrial metabolism-related genes (MMRGs) by single sample gene set enrichment analysis (ssGSEA). Subsequently, univariate Cox regression and LASSO algorithm were used to identify prognosis-associated MMRG and risk-stratify patients. Functional enrichment, interaction network and immune-related analyses were performed to explore the features differences in patients at different risks. Finally, a prognostic nomogram incorporating clinical factors was constructed to assess the prognosis of ESCC. Our results found there were differences in clinical features between the MMs-high group and the MMs-low group in the TCGA-ESCC dataset (P<0.05). Afterwards, we identified 6 MMRGs (COX10, ACADVL, IDH3B, AKR1A1, LIAS, and NDUFB8) signature that could accurately distinguish high-risk and low-risk ESCC patients. A predictive nomogram that combined the 6 MMRGs with sex and N stage to predict the prognosis of ESCC was constructed, and the areas under the receiver operating characteristic (ROC) curve at 1, 2 and 3 years were 0.948, 0.927 and 0.848, respectively. Finally, we found that COX10, one of 6 MMRGs, could inhibit the malignant progression of ESCC in vitro. In summary, we constructed a clinical prognosis model based on 6 MMRGs and clinical features which can accurately predict the prognosis of ESCC patients.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Immune cell infiltration and prognostic index in cervical cancer: insights from metabolism-related differential genes

Background

Cervical cancer remains a significant gynecologic malignancy in both China and the United States, posing a substantial threat to women's lives and health due to its high morbidity and mortality rates. Altered energy metabolism and dysregulated mitochondrial function play crucial roles in the development, growth, metastasis, and recurrence of malignant tumors. In this study, we aimed to predict prognosis and assess efficacy of anti-tumor therapy in cervical cancer patients based on differential genes associated with mitochondrial metabolism.

Methods

Transcriptomic data and clinical profiles of cervical cancer patients were retrieved from the TCGA and GEO databases. Differential gene-related cellular pathways were identified through GO, KEGG, and GSEA analyses. Prognostic indices were constructed using LASSO regression analysis. Immune cell infiltration was assessed using CIBERSORT and ssGSEA, and the correlation between immune checkpoint inhibitor genes and differential genes was examined. Tumor mutation load (TMB) and its association with prognostic indices were analyzed using nucleotide variant data from the TCGA database. Patient response to immunotherapy and sensitivity to antitumor drugs were determined using the TIDE algorithm and the oncoPredic algorithm, respectively.

Results

A prognostic index based on metabolism-related differential genes was developed to predict the clinical outcome of cervical cancer patients, enabling their classification into two distinct subtypes. The prognostic index emerged as an independent risk factor for unfavorable prognosis. The high-index group exhibited a significantly worse overall prognosis, along with elevated tumor mutation burden (TMB), increased immune cell infiltration, and lower TIDE scores, indicating a potential benefit from immunotherapy. Conversely, the low-index group demonstrated increased sensitivity to metabolism-related antitumor agents, specifically multikinase inhibitors.

Conclusion

The aim of this study was to develop a prognostic index based on differential genes associated with mitochondrial metabolism, which could be used to predict cervical cancer patients' prognoses. When combined with TIDE and TMB analyses, this prognostic index offers insights into the immune cell infiltration landscape, as well as the potential efficacy of immunotherapy and targeted therapy. Our analysis suggests that the Iron-Sulfur Cluster Assembly Enzyme (ISCU) gene holds promise as a biomarker for cervical cancer immunotherapy.

Advances and opportunities in methods to study protein translation - A review

It is generally believed that the regulation of gene expression involves protein translation occurring before RNA transcription. Therefore, it is crucial to investigate protein translation and its regulation. Recent advancements in biological sciences, particularly in the field of omics, have revolutionized protein translation research. These studies not only help characterize changes in protein translation during specific biological or pathological processes but also have significant implications in disease prevention and treatment. In this review, we summarize the latest methods in ribosome-based translation omics. We specifically focus on the application of fluorescence imaging technology and omics technology in studying overall protein translation. Additionally, we analyze the advantages, disadvantages, and application of these experimental methods, aiming to provide valuable insights and references to researchers studying translation.

Principles, challenges, and advances in ribosome profiling: from bulk to low-input and single-cell analysis

Ribosome profiling has revolutionized our understanding of gene expression regulation by providing a snapshot of global translation in vivo. This powerful technique enables the investigation of the dynamics of translation initiation, elongation, and termination, and has provided insights into the regulation of protein synthesis under various conditions. Despite its widespread adoption, challenges persist in obtaining high-quality ribosome profiling data. In this review, we discuss the fundamental principles of ribosome profiling and related methodologies, including selective ribosome profiling and translation complex profiling. We also delve into quality control to assess the reliability of ribosome profiling datasets, and the efforts to improve data quality by modifying the standard procedures. Additionally, we highlight recent advancements in ribosome profiling that enable the transition from bulk to low-input and single-cell applications. Single-cell ribosome profiling has emerged as a crucial tool for exploring translation heterogeneity within specific cell populations. However, the challenges of capturing mRNAs efficiently and the sparse nature of footprint reads in single-cell ribosome profiling present ongoing obstacles. The need to refine ribosome profiling techniques remains, especially when used at the single-cell level.

Dissecting the alternation landscape of mitochondrial metabolism-related genes in lung adenocarcinoma and their latent mechanisms

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer with high incidence and unsatisfactory prognosis. The majority of LUAD patients eventually succumb to local and/or distinct metastatic recurrence. Genomic research of LUAD has broadened our understanding of this disease's biology and improved target therapies. However, the alternation landscape and characteristics of mitochondrial metabolism-related genes (MMRGs) in LUAD progression remain poorly understood. We performed a comprehensive analysis to identify the function and mechanism of MMRGs in LUAD based on the TCGA and GEO databases, which might offer therapeutic values for clinical researchers. Then, we figured out three hub prognosis-associated MMRGs (also termed as PMMRGs: ACOT11, ALDH2, and TXNRD1) that were engaged in the evolution of LUAD. To investigate the correlation between clinicopathological characteristics and MMRGs, we divided LUAD samples into two clusters (C1 and C2) based on key MMRGs. In addition, important pathways and the immune infiltration landscape affected by LUAD clusters were also delineated. Further, we nominated potential regulatory mechanisms underlying the MMRGs in LUAD development and progression. In conclusion, our integrative analysis enables a more comprehensive understanding of the mutation landscape of MMRGs in LUAD and provides an opportunity for more precise treatment.

High-throughput Assessment of Mitochondrial Protein Synthesis in Mammalian Cells Using Mito-FUNCAT FACS

In addition to cytosolic protein synthesis, mitochondria also utilize another translation system that is tailored for mRNAs encoded in the mitochondrial genome. The importance of mitochondrial protein synthesis has been exemplified by the diverse diseases associated with in organello translation deficiencies. Various methods have been developed to monitor mitochondrial translation, such as the classic method of labeling newly synthesized proteins with radioisotopes and the more recent ribosome profiling. However, since these methods always assess the average cell population, measuring the mitochondrial translation capacity in individual cells has been challenging. To overcome this issue, we recently developed mito-fluorescent noncanonical amino acid tagging (FUNCAT) fluorescence-activated cell sorting (FACS), which labels nascent peptides generated by mitochondrial ribosomes with a methionine analog, L-homopropargylglycine (HPG), conjugates the peptides with fluorophores by an in situ click reaction, and detects the signal in individual cells by FACS equipment. With this methodology, the hidden heterogeneity of mitochondrial translation in cell populations can be addressed.

Human mitochondria require mtRF1 for translation termination at non-canonical stop codons

The mitochondrial translation machinery highly diverged from its bacterial counterpart. This includes deviation from the universal genetic code, with AGA and AGG codons lacking cognate tRNAs in human mitochondria. The locations of these codons at the end of COX1 and ND6 open reading frames, respectively, suggest they might function as stop codons. However, while the canonical stop codons UAA and UAG are known to be recognized by mtRF1a, the release mechanism at AGA and AGG codons remains a debated issue. Here, we show that upon the loss of another member of the mitochondrial release factor family, mtRF1, mitoribosomes accumulate specifically at AGA and AGG codons. Stalling of mitoribosomes alters COX1 transcript and protein levels, but not ND6 synthesis. In addition, using an in vitro reconstituted mitochondrial translation system, we demonstrate the specific peptide release activity of mtRF1 at the AGA and AGG codons. Together, our results reveal the role of mtRF1 in translation termination at non-canonical stop codons in mitochondria.

A custom library construction method for super-resolution ribosome profiling in Arabidopsis

BACKGROUND

Ribosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol.

RESULTS

Here we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method.

CONCLUSIONS

We established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species.

An integrated bioinformatic investigation of mitochondrial energy metabolism genes in colon adenocarcinoma followed by preliminary validation of CPT2 in tumor immune infiltration

Background

The prognosis for colon adenocarcinoma (COAD) today remains poor. Changes in mitochondria-related genes and metabolic reprogramming are related to tumor growth, metastasis, and immune evasion and are key factors in tumor genesis and development.

Methods

TCGA database was used to analyze the differentially expressed mitochondrial energy metabolism pathway-related genes (MMRGs) in COAD patients, and the mutation of MMRG in tumor cells, the biological processes involved, and the correlation with tumor immunity were also analyzed. Then, MMRG and MMRG-related genes were used to divide COAD patients into different subtypes, and immunocorrelation analysis and survival analysis were performed. Finally, univariate regression analysis and LASSO regression analysis were used to construct a prognostic risk model for COAD patients, which was verified by the GEO database and evaluated by Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves, and the correlation between the risk model and immunity and clinical subtypes based on MMRG was analyzed.

Results

In this study, the MMRG patterns and tumor immune microenvironment characteristics in COAD patients were systematically evaluated by clustering the expression of 188 MMRGs. We identified two subtypes of COAD with different clinical and immunological characteristics. Eight of the 28 differentially expressed MMRG genes were used to construct risk scores. ROC and K-M curves suggested that the risk model could well predict the prognosis of COAD patients, and the risk model was related to immune cell infiltration and immune function.

Conclusions

The two COAD subtypes identified by MMRG are helpful for the clinical differentiation of patients with different prognoses and tumor progressions, and the risk score can assist the clinical evaluation of patient prognosis. Our results suggest that CPT2 contributes to the recruitment and regulation of neutrophils in COAD. CPT2 may act as a valuable biomarker for COAD immunotherapy.

Balanced mitochondrial and cytosolic translatomes underlie the biogenesis of human respiratory complexes

BACKGROUND

Oxidative phosphorylation (OXPHOS) complexes consist of nuclear and mitochondrial DNA-encoded subunits. Their biogenesis requires cross-compartment gene regulation to mitigate the accumulation of disproportionate subunits. To determine how human cells coordinate mitochondrial and nuclear gene expression processes, we tailored ribosome profiling for the unique features of the human mitoribosome.

RESULTS

We resolve features of mitochondrial translation initiation and identify a small ORF in the 3' UTR of MT-ND5. Analysis of ribosome footprints in five cell types reveals that average mitochondrial synthesis levels correspond precisely to cytosolic levels across OXPHOS complexes, and these average rates reflect the relative abundances of the complexes. Balanced mitochondrial and cytosolic synthesis does not rely on rapid feedback between the two translation systems, and imbalance caused by mitochondrial translation deficiency is associated with the induction of proteotoxicity pathways.

CONCLUSIONS

Based on our findings, we propose that human OXPHOS complexes are synthesized proportionally to each other, with mitonuclear balance relying on the regulation of OXPHOS subunit translation across cellular compartments, which may represent a proteostasis vulnerability.

Mito-FUNCAT-FACS reveals cellular heterogeneity in mitochondrial translation

Mitochondria possess their own genome that encodes components of oxidative phosphorylation (OXPHOS) complexes, and mitochondrial ribosomes within the organelle translate the mRNAs expressed from the mitochondrial genome. Given the differential OXPHOS activity observed in diverse cell types, cell growth conditions, and other circumstances, cellular heterogeneity in mitochondrial translation can be expected. Although individual protein products translated in mitochondria have been monitored, the lack of techniques that address the variation in overall mitochondrial protein synthesis in cell populations poses analytic challenges. Here, we adapted mitochondrial-specific fluorescent noncanonical amino acid tagging (FUNCAT) for use with fluorescence-activated cell sorting (FACS) and developed mito-FUNCAT-FACS. The click chemistry-compatible methionine analog L-homopropargylglycine (HPG) enabled the metabolic labeling of newly synthesized proteins. In the presence of cytosolic translation inhibitors, HPG was selectively incorporated into mitochondrial nascent proteins and conjugated to fluorophores via the click reaction (mito-FUNCAT). The application of in situ mito-FUNCAT to flow cytometry allowed us to separate changes in net mitochondrial translation activity from those of the organelle mass and detect variations in mitochondrial translation in cancer cells. Our approach provides a useful methodology for examining mitochondrial protein synthesis in individual cells.

Into the matrix: current methods for mitochondrial translation studies

In addition to the cytoplasmic translation system, eukaryotic cells house additional protein synthesis machinery in mitochondria. The importance of this in organello translation is exemplified by clinical pathologies associated with mutations in mitochondrial translation factors. Although a detailed understanding of mitochondrial translation has long been awaited, quantitative, comprehensive, and spatiotemporal measurements have posed analytic challenges. The recent development of novel approaches for studying mitochondrial protein synthesis has overcome these issues and expands our understanding of the unique translation system. Here, we review the current technologies for the investigation of mitochondrial translation and the insights provided by their application.

Comprehensive Analysis of Alteration Landscape and Its Clinical Significance of Mitochondrial Energy Metabolism Pathway-Related Genes in Lung Cancers

Background

Mitochondria are the energy factories of cells. The abnormality of mitochondrial energy metabolism pathways is closely related to the occurrence and development of lung cancer. The abnormal genes in mitochondrial energy metabolism pathways might be the novel targets and biomarkers to diagnose and treat lung cancers.

Method

Genes in major mitochondrial energy metabolism pathways were obtained from the KEGG database. The transcriptomic, mutation, and clinical data of lung cancers were obtained from The Cancer Genome Atlas (TCGA) database. Genes and clinical biomarkers were mined that affected lung cancer survival. Gene enrichment analysis was performed with ClusterProfiler and the gene set enrichment analysis (GSEA). STRING database and Cytoscape were used for protein-protein interaction (PPI) analysis. The diagnostic biomarker pattern of lung cancer was optimized, and its accuracy was verified with 10-fold cross-validation. The four genes screened by logistic regression model were verified by western blot in 5 pairs of lung cancer specimens collected in hospital.

Results

In total, 188 mitochondrial energy metabolism pathway-related genes (MMRGs) were included in this study. GSEA analysis found that MMRGs in the lung cancer group were mainly enriched in the metabolic pathway of oxidative phosphorylation and electron respiratory transport chain compared to the control group. Age did not affect the mutation frequency of MMRGs. Comparative analysis of these 188 MMRGs identified 43 differentially expressed MMRGs (24 upregulated and 19 downregulated) in the lung cancer group compared to the control group. The survival analysis of these 43 differentially expressed MMRGs found that the survival time was better in the low-expressed GAPDHS group than that in the high-expressed GAPDHS group of lung cancers. The advanced age, high expression of GAPDHS, low expressions of ACSBG1 and CYP4A11, and ACOX3 mutation were biomarkers of poor prognosis in lung cancers. PPI analysis showed that proteins such as GAPDH and GAPDHS interacted with many proteins in mitochondrial metabolic pathways. A four-MMRG-signature model (y = 0.0069∗ACADL - 0.001∗ALDH18A1 - 0.0405∗CPT1B + 0.0008∗PPARG - 1.625) was established to diagnose lung cancer with the accuracy up to 98.74%, AUC value up to 0.992, and a missed diagnosis rate of only 0.6%. Western blotting showed that ALDH18A1 and CPT1B proteins were significantly overexpressed in the lung cancer group (p < 0.05), and ACADL and PPARG proteins were slightly underexpressed in the lung cancer group (p < 0.05), which were consistent with the results of their corresponding mRNA expressions.

Conclusion

Mitochondrial energy metabolism pathway alterations are the important hallmarks of lung cancer. Age did not increase the risk of MMRG mutation. High expression of GAPDHS, low expression of ACSBG1, low expression of CYP4A11, mutated ACOX3, and old age predict a poor prognosis of lung cancer. Four differentially expressed MMRGs (ACADL, ALDH18A1, CPT1B, and PPARG) established a logistic regression model, which could effectively diagnose lung cancer. At the protein level, ALDH18A1 and CPT1B were significantly upregulated, and ACADL and PPARG were slightly underexpressed, in the lung cancer group compared to the control group, which were consistent with the results of their corresponding mRNA expressions.