System wide analyses have underestimated protein abundances and the importance of transcription in mammals

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Replication Study of the Association of GAS6 and PROS1 Polymorphisms with Behçet's Disease in a Japanese Population

PURPOSE

To investigate whether polymorphisms of GAS6 and PROS1, which each encode protein ligands for a family of tyrosine kinase receptors, are associated with Behçet's disease (BD) in a Japanese population.

METHODS

We recruited 734 Japanese patients with BD and 1789 Japanese healthy controls. In all participants, we genotyped two single-nucleotide polymorphisms (SNPs) reportedly associated with BD: rs9577873 in GAS6 and rs4857037 in PROS1.

RESULTS

We found that GAS6 rs9577873 was not significantly associated with BD. In contrast, PROS1 rs4857037, specifically the A allele, was associated with increased risk for BD. The A allele was also significantly associated with BD under additive and recessive genetic models. Expression analysis revealed that this allele was significantly associated with increased PROS1 expression.

CONCLUSIONS

Our findings suggest that increased PROS1 expression related to the A risk allele of rs4857037 affects tyrosine kinase receptor signaling, contributing to the development of BD.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BACKGROUND

Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.

RESULTS

Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.

CONCLUSIONS

Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

MiREx: mRNA levels prediction from gene sequence and miRNA target knowledge

Messenger RNA (mRNA) has an essential role in the protein production process. Predicting mRNA expression levels accurately is crucial for understanding gene regulation, and various models (statistical and neural network-based) have been developed for this purpose. A few models predict mRNA expression levels from the DNA sequence, exploiting the DNA sequence and gene features (e.g., number of exons/introns, gene length). Other models include information about long-range interaction molecules (i.e., enhancers/silencers) and transcriptional regulators as predictive features, such as transcription factors (TFs) and small RNAs (e.g., microRNAs - miRNAs). Recently, a convolutional neural network (CNN) model, called Xpresso, has been proposed for mRNA expression level prediction leveraging the promoter sequence and mRNAs' half-life features (gene features). To push forward the mRNA level prediction, we present miREx, a CNN-based tool that includes information about miRNA targets and expression levels in the model. Indeed, each miRNA can target specific genes, and the model exploits this information to guide the learning process. In detail, not all miRNAs are included, only a selected subset with the highest impact on the model. MiREx has been evaluated on four cancer primary sites from the genomics data commons (GDC) database: lung, kidney, breast, and corpus uteri. Results show that mRNA level prediction benefits from selected miRNA targets and expression information. Future model developments could include other transcriptional regulators or be trained with proteomics data to infer protein levels.

Discordant skeletal muscle gene and protein responses to exercise

The ability of skeletal muscle to adapt to repeated contractile stimuli is one of the most intriguing aspects of physiology. The molecular bases underpinning these adaptations involve increased protein activity and/or expression, mediated by an array of pre- and post-transcriptional processes, as well as translational and post-translational control. A longstanding dogma assumes a direct relationship between exercise-induced increases in mRNA levels and subsequent changes in the abundance of the proteins they encode. Drawing on the results of recent studies, we dissect and question the common assumption of a direct relationship between changes in the skeletal muscle transcriptome and proteome induced by repeated muscle contractions (e.g., exercise).

Gene expression flux analysis reveals specific regulatory modalities of gene expression

The level of a given protein is determined by the synthesis and degradation rates of its mRNA and protein. While several studies have quantified the contribution of different gene expression steps in regulating protein levels, these are limited by using equilibrium approximations in out-of-equilibrium biological systems. Here, we introduce gene expression flux analysis to quantitatively dissect the dynamics of the expression level for specific proteins and use it to analyze published transcriptomics and proteomics datasets. Our analysis reveals distinct regulatory modalities shared by sets of genes with clear functional signatures. We also find that protein degradation plays a stronger role than expected in the adaptation of protein levels. These findings suggest that shared regulatory strategies can lead to versatile responses at the protein level and highlight the importance of going beyond equilibrium approximations to dissect the quantitative contribution of different steps of gene expression to protein dynamics.

Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

Highlights

At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.

An integrated proteome and transcriptome of B cell maturation defines poised activation states of transitional and mature B cells

During B cell maturation, transitional and mature B cells acquire cell-intrinsic features that determine their ability to exit quiescence and mount effective immune responses. Here we use label-free proteomics to quantify the proteome of B cell subsets from the mouse spleen and map the differential expression of environmental sensing, transcription, and translation initiation factors that define cellular identity and function. Cross-examination of the full-length transcriptome and proteome identifies mRNAs related to B cell activation and antibody secretion that are not accompanied by detection of the encoded proteins. In addition, proteomic data further suggests that the translational repressor PDCD4 restrains B cell responses, in particular those from marginal zone B cells, to a T-cell independent antigen. In summary, our molecular characterization of B cell maturation presents a valuable resource to further explore the mechanisms underpinning the specialized functions of B cell subsets, and suggest the presence of 'poised' mRNAs that enable expedited B cell responses.

Environmental Enrichment Increased Bdnf Transcripts in the Prefrontal Cortex: Implications for an Epigenetically Controlled Mechanism

Environmental enrichment (EE) is a condition characterized by its complexity regarding social contact, exposure to novelty, tactile stimuli and voluntary exercise, also is considered as a eustress model. The impact of EE on brain physiology and behavioral outcomes may be at least partly underpinned by mechanisms involving the modulation of the brain-derived neurotrophic factor (BDNF), but the connection between specific Bdnf exon expression and their epigenetic regulation remain poorly understood. This study aimed to dissect the transcriptional and epigenetic regulatory effect of 54-day exposure to EE on BDNF by analysing individual BDNF exons mRNA expression and the DNA methylation profile of a key transcriptional regulator of the Bdnf gene, exon IV, in the prefrontal cortex (PFC) of C57BL/6 male mice (sample size = 33). Bdnf exons II, IV, VI and IX mRNA expression were upregulated and methylation levels at two CpG sites of exon IV were reduced in the PFC of EE mice. As deficit in exon IV expression has also been causally implicated in stress-related psychopathologies, we also assessed anxiety-like behavior and plasma corticosterone levels in these mice to determine any potential correlation. However, no changes were observed in EE mice. The findings may suggest an EE-induced epigenetic control of BDNF exon expression via a mechanism involving exon IV methylation. The findings of this study contribute to the current literature by dissecting the Bdnf gene topology in the PFC where transcriptional and epigenetic regulatory effect of EE takes place.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

SPECK: an unsupervised learning approach for cell surface receptor abundance estimation for single-cell RNA-sequencing data

Summary

The rapid development of single-cell transcriptomics has revolutionized the study of complex tissues. Single-cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, these data are uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing data can provide the best performance, these training data are limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. Therefore, we developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and primarily evaluated its performance against unsupervised approaches for at least 25 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction of scRNA-seq data are effective for receptor abundance estimation, with SPECK providing the best overall performance.

Availability and implementation

SPECK is freely available at https://CRAN.R-project.org/package=SPECK.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Uncovering biology by single-cell proteomics

Recent technological advances have opened the door to single-cell proteomics that can answer key biological questions regarding how protein expression, post-translational modifications, and protein interactions dictate cell state in health and disease.

Micro and macroevolution of sea anemone venom phenotype

Venom is a complex trait with substantial inter- and intraspecific variability resulting from strong selective pressures acting on the expression of many toxic proteins. However, understanding the processes underlying toxin expression dynamics that determine the venom phenotype remains unresolved. By interspecific comparisons we reveal that toxin expression in sea anemones evolves rapidly and that in each species different toxin family dictates the venom phenotype by massive gene duplication events. In-depth analysis of the sea anemone, Nematostella vectensis, revealed striking variation of the dominant toxin (Nv1) diploid copy number across populations (1-24 copies) resulting from independent expansion/contraction events, which generate distinct haplotypes. Nv1 copy number correlates with expression at both the transcript and protein levels with one population having a near-complete loss of Nv1 production. Finally, we establish the dominant toxin hypothesis which incorporates observations in other venomous lineages that animals have convergently evolved a similar strategy in shaping their venom.

Evaluating data integrity in ribosome footprinting datasets through modelled polysome profiles

The assessment of transcriptome-wide ribosome binding to mRNAs is useful for studying the dynamic regulation of protein synthesis. Two methods frequently applied in eukaryotic cells that operate at different levels of resolution are polysome profiling, which reveals the distribution of ribosome loads across the transcriptome, and ribosome footprinting (also termed ribosome profiling or Ribo-Seq), which when combined with appropriate data on mRNA expression can reveal ribosome densities on individual transcripts. In this study we develop methods for relating the information content of these two methods to one another, by reconstructing theoretical polysome profiles from ribosome footprinting data. Our results validate both approaches as experimental tools. Although we show that both methods can yield highly consistent data, some published ribosome footprinting datasets give rise to reconstructed polysome profiles with non-physiological features. We trace these aberrant features to inconsistencies in RNA and Ribo-Seq data when compared to datasets yielding physiological polysome profiles, thereby demonstrating that modelled polysomes are useful for assessing global dataset properties such as its quality in a simple, visual approach. Aside from using polysome profile reconstructions on published datasets, we propose that this also provides a useful tool for validating new ribosome footprinting datasets in early stages of analyses.

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes. Proteins with more reproducible measurements tend to have a higher mRNA-protein correlation, suggesting that measurement reproducibility accounts for a substantial fraction of the unexplained variation between mRNA and protein abundances. The reproducibility of individual proteins is somewhat consistent across studies, and we exploit this to develop an aggregate reproducibility score that explains a substantial amount of the variation in mRNA-protein correlations across multiple studies. Finally, we show that pathways previously reported to have a higher-than-average mRNA-protein correlation may simply contain members that can be more reproducibly quantified.

False-positive IRESes from Hoxa9 and other genes resulting from errors in mammalian 5' UTR annotations

Hyperconserved genomic sequences have great promise for understanding core biological processes. It has been recently proposed that scores of hyperconserved 5' untranslated regions (UTRs), also known as transcript leaders (hTLs), encode internal ribosome entry sites (IRESes) that drive cap-independent translation, in part, via interactions with ribosome expansion segments. However, the direct functional significance of such interactions has not yet been definitively demonstrated. We provide evidence that the putative IRESes previously reported in Hox gene hTLs are rarely included in transcript leaders. Instead, these regions function independently as transcriptional promoters. In addition, we find the proposed RNA structure of the putative Hoxa9 IRES is not conserved. Instead, sequences previously shown to be essential for putative IRES activity encode a hyperconserved transcription factor binding site (E-box) that contributes to its promoter activity and is bound by several transcription factors, including USF1 and USF2. Similar E-box sequences enhance the promoter activities of other putative Hoxa gene IRESes. Moreover, we provide evidence that the vast majority of hTLs with putative IRES activity overlap transcriptional promoters, enhancers, and 3' splice sites that are most likely responsible for their reported IRES activities. These results argue strongly against recently reported widespread IRES-like activities from hTLs and contradict proposed interactions between ribosomal expansion segment ES9S and putative IRESes. Furthermore, our work underscores the importance of accurate transcript annotations, controls in bicistronic reporter assays, and the power of synthesizing publicly available data from multiple sources.

Vitamin D boosts immune response of macrophages through a regulatory network of microRNAs and mRNAs

Vitamin D is associated with the stimulation of innate immunity, inflammation, and host defense against pathogens. Macrophages express receptors of Vitamin D, regulating transcription of genes related to immune processes. However, the transcriptional and post-transcriptional strategies controlling gene expression in differentiated macrophages, and how they are influenced by Vitamin D are not well understood. We studied whether Vitamin D enhances immune response by regulating the expression of microRNAs and mRNAs. Analysis of the transcriptome showed differences in expression of 199 genes, of which 68% were up-regulated, revealing the cell state of monocyte-derived macrophages differentiated with Vitamin D (D3-MDMs) as compared to monocyte-derived macrophages (MDMs). The differentially expressed genes appear to be associated with pathophysiological processes, including inflammatory responses, and cellular stress. Transcriptional motifs in promoter regions of up- or down-regulated genes showed enrichment of VDR motifs, suggesting possible roles of transcriptional activator or repressor in gene expression. Further, microRNA-Seq analysis indicated that there were 17 differentially expressed miRNAs, of which, 7 were up-regulated and 10 down-regulated, suggesting that Vitamin D plays a critical role in the regulation of miRNA expression during macrophages differentiation. The miR-6501-3p, miR-1273h-5p, miR-665, miR-1972, miR-1183, miR-619-5p were down-regulated in D3-MDMs compared to MDMs. The integrative analysis of miRNA and mRNA expression profiles predict that miR-1972, miR-1273h-5p, and miR-665 regulate genes PDCD1LG2, IL-1B, and CD274, which are related to the inflammatory response. Results suggest an essential role of Vitamin D in macrophage differentiation that modulates host response against pathogens, inflammation, and cellular stress.

Quantification of Intracellular Proteins in Single Cells Based on Engineered Picoliter Droplets

Unlike conventional bulk measurements, single-cell protein analysis permits quantification of protein expression in individual cells. This has shed light on the cell-to-cell variation in heterogeneous biological systems, such as solid tumors, brain tissues, and developing embryos. Herein, a microfluidic method is developed to profile protein expression in individual cells by performing single-cell intracellular protein immunoassay in picoliter paired droplets. The high sensitivity of single-cell protein analysis on a chip is achieved by the confined reaction volume of picoliter droplets, efficient kinetic characteristics of the immunoassay through active mixing, and minimum single-cell protein loss by integrated operations. The abundance of an intracellular prostate specific antigen at the single-cell level is measured, and then the platform is applied to identify cell types and investigate heterogeneity within cell populations. Overall, a paired chip for single-cell immunoassay establishes a foundation for parallel, sensitive, and integrated protein quantification at the single-cell level and will find wide applications in the field of single-cell proteomics.

The interplay between translational efficiency, poly(A) tails, microRNAs, and neuronal activation

Neurons provide a rich setting for studying post-transcriptional control. Here, we investigate the landscape of translational control in neurons and search for mRNA features that explain differences in translational efficiency (TE), considering the interplay between TE, mRNA poly(A)-tail lengths, microRNAs, and neuronal activation. In neurons and brain tissues, TE correlates with tail length, and a few dozen mRNAs appear to undergo cytoplasmic polyadenylation upon light or chemical stimulation. However, the correlation between TE and tail length is modest, explaining <5% of TE variance, and even this modest relationship diminishes when accounting for other mRNA features. Thus, tail length appears to affect TE only minimally. Accordingly, miRNAs, which accelerate deadenylation of their mRNA targets, primarily influence target mRNA levels, with no detectable effect on either steady-state tail lengths or TE. Larger correlates with TE include codon composition and predicted mRNA folding energy. When combined in a model, the identified correlates explain 38%-45% of TE variance. These results provide a framework for considering the relative impact of factors that contribute to translational control in neurons. They indicate that when examined in bulk, translational control in neurons largely resembles that of other types of post-embryonic cells. Thus, detection of more specialized control might require analyses that can distinguish translation occurring in neuronal processes from that occurring in cell bodies.

How thermal challenges change gene regulation in the songbird brain and gonad: implications for sexual selection in our changing world

In a rapidly warming world, exposure to high temperatures may impact fitness, but the gene regulatory mechanisms that link sublethal heat to sexually selected traits are not well understood, particularly in endothermic animals. Our experiment used zebra finches (Taeniopygia guttata), songbirds that experience extreme temperature fluctuations in their native Australia. We exposed captive males to an acute thermal challenge (43°C) compared with thermoneutral (35°C) and lower (27°C) temperatures. We found significantly more heat dissipation behaviors at 43°C, a temperature previously shown to reduce song production and fertility, and more heat retention behaviors at 27°C. Next, we characterized transcriptomic responses in tissues important for mating effort - the posterior telencephalon, for its role in song production, and the testis, for its role in fertility and hormone production. Differential expression of hundreds of genes in the testes, but few in the brain, suggest the brain is less responsive to extreme temperatures. Nevertheless, gene network analyses revealed that expression related to dopaminergic signaling in the brain co-varied with heat dissipation behaviors, providing a mechanism by which temporary thermal challenges may alter motivational circuits for song production. In both brain and testis, we observed correlations between thermally sensitive gene networks and individual differences in thermoregulatory behavior. Although we cannot directly relate these gene regulatory changes to mating success, our results suggest that individual variation in response to thermal challenges could impact sexually selected traits in a warming world.

Consequences of Chromosome Loss: Why Do Cells Need Each Chromosome Twice?

Aneuploidy is a cellular state with an unbalanced chromosome number that deviates from the usual euploid status. During evolution, elaborate cellular mechanisms have evolved to maintain the correct chromosome content over generations. The rare errors often lead to cell death, cell cycle arrest, or impaired proliferation. At the same time, aneuploidy can provide a growth advantage under selective conditions in a stressful, frequently changing environment. This is likely why aneuploidy is commonly found in cancer cells, where it correlates with malignancy, drug resistance, and poor prognosis. To understand this “aneuploidy paradox”, model systems have been established and analyzed to investigate the consequences of aneuploidy. Most of the evidence to date has been based on models with chromosomes gains, but chromosome losses and recurrent monosomies can also be found in cancer. We summarize the current models of chromosome loss and our understanding of its consequences, particularly in comparison to chromosome gains.

Chemerin Impact on Alternative mRNA Transcription in the Porcine Luteal Cells

Chemerin participates in the regulation of processes related to physiological and disorder mechanisms in mammals, including metabolism, obesity, inflammation, and reproduction. In this study, we have investigated chemerin influence on alternative mRNA transcription within the porcine luteal cell transcriptome, such as differential expression of long non-coding RNAs (DELs) and their interactions with differentially expressed genes (DEGs), differences in alternative splicing of transcripts (DASs), and allele-specific expression (ASEs) related to the single nucleotide variants (SNVs) frequency. Luteal cells were collected from gilts during the mid-luteal phase of the oestrous cycle. After in vitro culture of cells un-/treated with chemerin, the total RNA was isolated and sequenced using the high-throughput method. The in silico analyses revealed 24 DELs cis interacting with 6 DEGs and trans-correlated with 300 DEGs, 137 DASs events, and 18 ASEs. The results enabled us to analyse metabolic and signalling pathways in detail, providing new insights into the effects of chemerin on the corpus luteum functions related to inflammatory response, leukocyte infiltration, the occurrence of luteotropic and luteolytic signals (leading to apoptosis and/or necroptosis). Validation of the results using qPCR confirmed the predicted expression changes. Chemerin at physiological concentrations significantly modifies the transcription processes in the porcine luteal cells.

The Involvement of RAGE and Its Ligands during Progression of ALS in SOD1 G93A Transgenic Mice

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by a progressive degeneration of upper and lower motor neurons that causes paralysis and muscle atrophy. The pathogenesis of the disease is still not elucidated. Receptor for Advanced Glycation End Product (RAGE) is a major component of the innate immune system and has implications in ALS pathogenesis. Multiple studies suggest the role of RAGE and its ligands in ALS. RAGE and its ligands are overexpressed in human and murine ALS motor neurons, astrocytes, and microglia. Here, we demonstrated the expression of RAGE and its ligands during the progression of the disease in the transgenic SOD1 G93A mouse lumbar spinal cord. We observed the highest expression of HMGB1 and S100b proteins at ALS onset. Our results highlight the potential role of RAGE and its ligands in ALS pathogenesis and suggest that some of the RAGE ligands might be used as biomarkers in early ALS diagnosis and potentially be useful in targeted therapeutic interventions at the early stage of this devastating disease.

Interpretation of exercise-induced changes in human skeletal muscle mRNA expression depends on the timing of the post-exercise biopsies

BACKGROUND

Exercise elicits a range of adaptive responses in skeletal muscle, which include changes in mRNA expression. To better understand the health benefits of exercise training, it is important to investigate the underlying molecular mechanisms of skeletal muscle adaptation to exercise. However, most studies have assessed the molecular events at only a few time-points within a short time frame post-exercise, and the variations of gene expression kinetics have not been addressed systematically.

METHODS

We assessed the mRNA expression of 23 gene isoforms implicated in the adaptive response to exercise at six time-points (0, 3, 9, 24, 48, and 72 h post exercise) over a 3-day period following a single session of high-intensity interval exercise.

RESULTS

The temporal patterns of target gene expression were highly variable and the expression of mRNA transcripts detected was largely dependent on the timing of muscle sampling. The largest fold change in mRNA expression of each tested target gene was observed between 3 and 72 h post-exercise.

DISCUSSION AND CONCLUSIONS

Our findings highlight an important gap in knowledge regarding the molecular response to exercise, where the use of limited time-points within a short period post-exercise has led to an incomplete understanding of the molecular response to exercise. Muscle sampling timing for individual studies needs to be carefully chosen based on existing literature and preliminary analysis of the molecular targets of interest. We propose that a comprehensive time-course analysis on the exercise-induced transcriptional response in humans will significantly benefit the field of exercise molecular biology.

Day and night isotope labelling reveal metabolic pathway specific regulation of protein synthesis rates in Arabidopsis

Plants have a diurnal separation of metabolic fluxes and a need for differential maintenance of protein machinery in the day and night. To directly assess the output of the translation process and to estimate the ATP investment involved, the individual rates of protein synthesis and degradation of hundreds of different proteins need to be measured simultaneously. We quantified protein synthesis and degradation through pulse labelling with heavy hydrogen in Arabidopsis thaliana rosettes to allow such an assessment of ATP investment in leaf proteome homeostasis on a gene-by-gene basis. Light-harvesting complex proteins were synthesised and degraded much faster in the day (approximately 10:1), while carbon metabolism and vesicle trafficking components were translated at similar rates day or night. Few leaf proteins changed in abundance between the day and the night despite reduced protein synthesis rates at night, indicating that protein degradation rates are tightly coordinated. The data reveal how the pausing of photosystem synthesis and degradation at night allows the redirection of a decreased energy budget to a selective night-time maintenance schedule.

Sex differences in immune gene expression in the brain of a small shorebird

Males and females often exhibit differences in behaviour, life histories, and ecology, many of which are typically reflected in their brains. Neuronal protection and maintenance include complex processes led by the microglia, which also interacts with metabolites such as hormones or immune components. Despite increasing interest in sex-specific brain function in laboratory animals, the significance of sex-specific immune activation in the brain of wild animals along with the variables that could affect it is widely lacking. Here, we use the Kentish plover (Charadrius alexandrinus) to study sex differences in expression of immune genes in the brain of adult males and females, in two wild populations breeding in contrasting habitats: a coastal sea-level population and a high-altitude inland population in China. Our analysis yielded 379 genes associated with immune function. We show a significant male-biased immune gene upregulation. Immune gene expression in the brain did not differ in upregulation between the coastal and inland populations. We discuss the role of dosage compensation in our findings and their evolutionary significance mediated by sex-specific survival and neuronal deterioration. Similar expression profiles in the coastal and inland populations suggest comparable genetic control by the microglia and possible similarities in pathogen pressures between habitats. We call for further studies on gene expression of males and females in wild population to understand the implications of immune function for life-histories and demography in natural systems.

A critical period of translational control during brain development at codon resolution

Translation modulates the timing and amplification of gene expression after transcription. Brain development requires uniquely complex gene expression patterns, but large-scale measurements of translation directly in the prenatal brain are lacking. We measure the reactants, synthesis and products of mRNA translation spanning mouse neocortex neurogenesis, and discover a transient window of dynamic regulation at mid-gestation. Timed translation upregulation of chromatin-binding proteins like Satb2, which is essential for neuronal subtype differentiation, restricts protein expression in neuronal lineages despite broad transcriptional priming in progenitors. In contrast, translation downregulation of ribosomal proteins sharply decreases ribosome biogenesis, coinciding with a major shift in protein synthesis dynamics at mid-gestation. Changing activity of eIF4EBP1, a direct inhibitor of ribosome biogenesis, is concurrent with ribosome downregulation and affects neurogenesis of the Satb2 lineage. Thus, the molecular logic of brain development includes the refinement of transcriptional programs by translation. Modeling of the developmental neocortex translatome is provided as an open-source searchable resource at https://shiny.mdc-berlin.de/cortexomics .

Application of Domain- and Genotype-Specific Models to Infer Post-Transcriptional Regulation of Segmentation Gene Expression in Drosophila

Unlike transcriptional regulation, the post-transcriptional mechanisms underlying zygotic segmentation gene expression in early Drosophila embryo have been insufficiently investigated. Condition-specific post-transcriptional regulation plays an important role in the development of many organisms. Our recent study revealed the domain- and genotype-specific differences between mRNA and the protein expression of Drosophila hb, gt, and eve genes in cleavage cycle 14A. Here, we use this dataset and the dynamic mathematical model to recapitulate protein expression from the corresponding mRNA patterns. The condition-specific nonuniformity in parameter values is further interpreted in terms of possible post-transcriptional modifications. For hb expression in wild-type embryos, our results predict the position-specific differences in protein production. The protein synthesis rate parameter is significantly higher in hb anterior domain compared to the posterior domain. The parameter sets describing Gt protein dynamics in wild-type embryos and Kr mutants are genotype-specific. The spatial discrepancy between gt mRNA and protein posterior expression in Kr mutants is well reproduced by the whole axis model, thus rejecting the involvement of post-transcriptional mechanisms. Our models fail to describe the full dynamics of eve expression, presumably due to its complex shape and the variable time delays between mRNA and protein patterns, which likely require a more complex model. Overall, our modeling approach enables the prediction of regulatory scenarios underlying the condition-specific differences between mRNA and protein expression in early embryo.

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

Multiplexed Single-Cell Plasmonic Immunoassay of Intracellular Signaling Proteins Enables Non-Destructive Monitoring of Cell Fate

It is of significant importance in cancer biology to identify signaling pathways that play key roles in cell fate determination. Dissecting cellular signaling pathways requires the measurement of a large number of signaling proteins. However, tools for simultaneously monitoring multiple signaling pathway components in single living cells remain limited at present. Herein, we describe an approach, termed multiplexed single-cell plasmonic immunosandwich assay (mxscPISA), for simultaneous detection of multiple signaling proteins in individual living cells. This approach enabled simultaneous non-destructive monitoring of multiple (up to five, currently the highest multiplexing capacity in living cells) cytoplasmic and nucleus signaling proteins in individual cells with ultrahigh detection sensitivity. As a proof of principle, the epidermal growth factor receptor (EGFR) pathway, which plays a central role in cell fate determination, was investigated using this approach in this study. We found that there were differential attenuation rate of pro-survival and accumulation rate of pro-death signaling protein of the EGFR pathway in response to EGFR inactivation. These findings implicate that, after EGFR inactivation, a transient imbalance between survival and apoptotic signaling outputs contributed to the final cell fate of death. The mxscPISA approach can be a promising tool to reveal a signaling dynamic pattern at the single-cell level and to identify key components of signaling pathways that contribute to the final cell fate using only a limited number of cells.

Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma

MicroRNA (miRNA)-based therapies are an emerging class of targeted therapeutics with many potential applications. Ewing Sarcoma patients could benefit dramatically from personalized miRNA therapy due to inter-patient heterogeneity and a lack of druggable (to this point) targets. However, because of the broad effects miRNAs may have on different cells and tissues, trials of miRNA therapies have struggled due to severe toxicity and unanticipated immune response. In order to overcome this hurdle, a network science-based approach is well-equipped to evaluate and identify miRNA candidates and combinations of candidates for the repression of key oncogenic targets while avoiding repression of essential housekeeping genes. We first characterized 6 Ewing sarcoma cell lines using mRNA sequencing. We then estimated a measure of tumor state, which we term network potential, based on both the mRNA gene expression and the underlying protein-protein interaction network in the tumor. Next, we ranked mRNA targets based on their contribution to network potential. We then identified miRNAs and combinations of miRNAs that preferentially act to repress mRNA targets with the greatest influence on network potential. Our analysis identified TRIM25, APP, ELAV1, RNF4, and HNRNPL as ideal mRNA targets for Ewing sarcoma therapy. Using predicted miRNA-mRNA target mappings, we identified miR-3613-3p, let-7a-3p, miR-300, miR-424-5p, and let-7b-3p as candidate optimal miRNAs for preferential repression of these targets. Ultimately, our work, as exemplified in the case of Ewing sarcoma, describes a novel pipeline by which personalized miRNA cocktails can be designed to maximally perturb gene networks contributing to cancer progression.

Regulation of mRNA translation in stem cells; links to brain disorders

Translational control of gene expression is emerging as a cardinal step in the regulation of protein abundance. Especially for embryonic (ESC) and neuronal stem cells (NSC), regulation of mRNA translation is involved in the maintenance of pluripotency but also differentiation. For neuronal stem cells this regulation is linked to the various neuronal subtypes that arise in the developing brain and is linked to numerous brain disorders. Herein, we review translational control mechanisms in ESCs and NSCs during development and differentiation, and briefly discuss their link to brain disorders.

Regulation of gene expression via translational buffering

Translation of an mRNA represents a critical step during the expression of protein-coding genes. As mechanisms governing post-transcriptional regulation of gene expression are progressively unveiled, it is becoming apparent that transcriptional programs are not fully reflected in the proteome. Herein, we highlight a previously underappreciated post-transcriptional mode of regulation of gene expression termed translational buffering. In principle, translational buffering opposes the impact of alterations in mRNA levels on the proteome. We further describe three types of translational buffering: compensation, which maintains protein levels e.g. across species or individuals; equilibration, which retains pathway stoichiometry; and offsetting, which acts as a reversible mechanism that maintains the levels of selected subsets of proteins constant despite genetic alteration and/or stress-induced changes in corresponding mRNA levels. While mechanisms underlying compensation and equilibration have been reviewed elsewhere, the principal focus of this review is on the less-well understood mechanism of translational offsetting. Finally, we discuss potential roles of translational buffering in homeostasis and disease.

Combinatorial peptides: A library that continuously probes low-abundance proteins

After a decade of experimental applications, it is the objective of this review to make a point on combinatorial peptide ligand libraries dedicated to low-abundance proteins from animals to plants and to microorganism proteomics. It is, thus, at the light of the recent technical developments and applications that we will examine the state of the art, its usage within the scientific community, and its openness to unexplored fields. The improvements of the methodology and its implementation in connection with analytical determinations of combinatorial peptide ligand library (CPLL)-treated samples are extensively reviewed and commented upon. Relevant examples covering few critical aspects describe the performance of the technology. Finally, a reflection on the technological future is attempted in particular by involving new concepts adapted to the limited availability of certain biological samples.

Measuring expression heterogeneity of single-cell cytoskeletal protein complexes

Multimeric cytoskeletal protein complexes orchestrate normal cellular function. However, protein-complex distributions in stressed, heterogeneous cell populations remain unknown. Cell staining and proximity-based methods have limited selectivity and/or sensitivity for endogenous multimeric protein-complex quantification from single cells. We introduce micro-arrayed, differential detergent fractionation to simultaneously detect protein complexes in hundreds of individual cells. Fractionation occurs by 60 s size-exclusion electrophoresis with protein complex-stabilizing buffer that minimizes depolymerization. Proteins are measured with a ~5-hour immunoassay. Co-detection of cytoskeletal protein complexes in U2OS cells treated with filamentous actin (F-actin) destabilizing Latrunculin A detects a unique subpopulation (~2%) exhibiting downregulated F-actin, but upregulated microtubules. Thus, some cells may upregulate other cytoskeletal complexes to counteract the stress of Latrunculin A treatment. We also sought to understand the effect of non-chemical stress on cellular heterogeneity of F-actin. We find heat shock may dysregulate filamentous and globular actin correlation. In this work, our assay overcomes selectivity limitations to biochemically quantify single-cell protein complexes perturbed with diverse stimuli.

Transcription factors: Bridge between cell signaling and gene regulation

Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional program. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications (PTMs) of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multi-omics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.

Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells

BACKGROUND

There is interest in deriving megakaryocytes (MKs) from pluripotent stem cells (iPSC) for biological studies. We previously found that genomic structural integrity and genotype concordance is maintained in iPSC-derived MKs.

OBJECTIVE

To establish a comprehensive dataset of genes and proteins expressed in iPSC-derived MKs.

METHODS

iPSCs were reprogrammed from peripheral blood mononuclear cells (MNCs) and MKs were derived from the iPSCs in 194 healthy European American and African American subjects. mRNA was isolated and gene expression measured by RNA sequencing. Protein expression was measured in 62 of the subjects using mass spectrometry.

RESULTS AND CONCLUSIONS

MKs expressed genes and proteins known to be important in MK and platelet function and demonstrated good agreement with previous studies in human MKs derived from CD34+ progenitor cells. The percent of cells expressing the MK markers CD41 and CD42a was consistent in biological replicates, but variable across subjects, suggesting that unidentified subject-specific factors determine differentiation of MKs from iPSCs. Gene and protein sets important in platelet function were associated with increasing expression of CD41/42a, while those related to more basic cellular functions were associated with lower CD41/42a expression. There was differential gene expression by the sex and race (but not age) of the subject. Numerous genes and proteins were highly expressed in MKs but not known to play a role in MK or platelet function; these represent excellent candidates for future study of hematopoiesis, platelet formation, and/or platelet function.

Resistance Exercise, Aging, Disuse, and Muscle Protein Metabolism

Skeletal muscle is the organ of locomotion, its optimal function is critical for athletic performance, and is also important for health due to its contribution to resting metabolic rate and as a site for glucose uptake and storage. Numerous endogenous and exogenous factors influence muscle mass. Much of what is currently known regarding muscle protein turnover is owed to the development and use of stable isotope tracers. Skeletal muscle mass is determined by the meal- and contraction-induced alterations of muscle protein synthesis and muscle protein breakdown. Increased loading as resistance training is the most potent nonpharmacological strategy by which skeletal muscle mass can be increased. Conversely, aging (sarcopenia) and muscle disuse lead to the development of anabolic resistance and contribute to the loss of skeletal muscle mass. Nascent omics-based technologies have significantly improved our understanding surrounding the regulation of skeletal muscle mass at the gene, transcript, and protein levels. Despite significant advances surrounding the mechanistic intricacies that underpin changes in skeletal muscle mass, these processes are complex, and more work is certainly needed. In this article, we provide an overview of the importance of skeletal muscle, describe the influence that resistance training, aging, and disuse exert on muscle protein turnover and the molecular regulatory processes that contribute to changes in muscle protein abundance. © 2021 American Physiological Society. Compr Physiol 11:2249-2278, 2021.

Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks

Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.

Proteomic/transcriptomic analysis of erythropoiesis

PURPOSE OF REVIEW

Erythropoiesis is a hierarchical process by which hematopoietic stem cells give rise to red blood cells through gradual cell fate restriction and maturation. Deciphering this process requires the establishment of dynamic gene regulatory networks (GRNs) that predict the response of hematopoietic cells to signals from the environment. Although GRNs have historically been derived from transcriptomic data, recent proteomic studies have revealed a major role for posttranscriptional mechanisms in regulating gene expression during erythropoiesis. These new findings highlight the need to integrate proteomic data into GRNs for a refined understanding of erythropoiesis.

RECENT FINDINGS

Here, we review recent proteomic studies that have furthered our understanding of erythropoiesis with a focus on quantitative mass spectrometry approaches to measure the abundance of transcription factors and cofactors during differentiation. Furthermore, we highlight challenges that remain in integrating transcriptomic, proteomic, and other omics data into a predictive model of erythropoiesis, and discuss the future prospect of single-cell proteomics.

SUMMARY

Recent proteomic studies have considerably expanded our knowledge of erythropoiesis beyond the traditional transcriptomic-centric perspective. These findings have both opened up new avenues of research to increase our understanding of erythroid differentiation, while at the same time presenting new challenges in integrating multiple layers of information into a comprehensive gene regulatory model.

ALKBH4 promotes tumourigenesis with a poor prognosis in non-small-cell lung cancer

The human AlkB homolog family (ALKBH) of proteins play a critical role in some types of cancer. However, the expression and function of the lysine demethylase ALKBH4 in cancer are poorly understood. Here, we examined the expression and function of ALKBH4 in non-small-cell lung cancer (NSCLC) and found that ALKBH4 was highly expressed in NSCLC, as compared to that in adjacent normal lung tissues. ALKBH4 knockdown significantly induced the downregulation of NSCLC cell proliferation via cell cycle arrest at the G1 phase of in vivo tumour growth. ALKBH4 knockdown downregulated E2F transcription factor 1 (E2F1) and its target gene expression in NSCLC cells. ALKBH4 and E2F1 expression was significantly correlated in NSCLC clinical specimens. Moreover, patients with high ALKBH4 expression showed a poor prognosis, suggesting that ALKBH4 plays a pivotal tumour-promoting role in NSCLC.

Feasibility of Developing Radiotracers for MDM2: Synthesis and Preliminary Evaluation of an 18F-Labeled Analogue of the MDM2 Inhibitor SP-141

Murine double minute 2 (MDM2), a negative regulator of the p53 tumor suppressor protein, is overexpressed in several human cancers. Herein we investigate the feasibility of developing ¹⁸F-labeled compounds based on the small molecule inhibitor SP-141 for imaging tumor MDM2 expression levels with positron emission tomography (PET). Three nonradioactive fluorinated SP-141 analogues, 1–3, were synthesized, and their binding to the MDM2 protein was analyzed by surface plasmon resonance (SPR). One of these, a fluoroethoxy analogue, was labeled with fluorine-18 (¹⁸F) using ¹⁸F-fluorethyl bromide to provide [¹⁸F]1 and evaluated in vitro and in vivo. SPR analysis confirmed the binding of the fluorinated analogues to MDM2 at 1.25–20 µM concentrations. Cell uptake studies revealed high uptake (67.5–71.4%/mg protein) and specificity of [¹⁸F]1 in MCF7 and HepG2 cells. The uptake of [¹⁸F]1 in these cells could be modulated using 100 µM SP-141, potentially reflecting changes in MDM2 expression because of p53 activation by SP-141. [¹⁸F]1 exhibited stable uptake and retention in HepG2 tumor xenografts (~3 %ID/g) in vivo, but poor clearance from blood and other normal tissues, yielding low tumor-to-background ratios (<2) at 2 h post injection. Our results suggest that [¹⁸F]1 has suboptimal characteristics for in vivo evaluation as a PET tracer for MDM2, but warrant radiolabeling and assessment of the other fluorinated analogues synthesized in this work, 2 and 3, and potentially other molecular scaffolds for developing MDM2 targeted radiotracers.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

Midkine: The Who, What, Where, and When of a Promising Neurotrophic Therapy for Perinatal Brain Injury

Midkine (MK) is a small secreted heparin-binding protein highly expressed during embryonic/fetal development which, through interactions with multiple cell surface receptors promotes growth through effects on cell proliferation, migration, and differentiation. MK is upregulated in the adult central nervous system (CNS) after multiple types of experimental injury and has neuroprotective and neuroregenerative properties. The potential for MK as a therapy for developmental brain injury is largely unknown. This review discusses what is known of MK's expression and actions in the developing brain, areas for future research, and the potential for using MK as a therapeutic agent to ameliorate the effects of brain damage caused by insults such as birth-related hypoxia and inflammation.

Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes

While the field of microbiology has adapted to the study of complex microbiomes via modern meta-omics techniques, we have not updated our basic knowledge regarding the quantitative levels of DNA, RNA and protein molecules within a microbial cell, which ultimately control cellular function. Here we report the temporal measurements of absolute RNA and protein levels per gene within a mixed bacterial-archaeal consortium. Our analysis of this data reveals an absolute protein-to-RNA ratio of 10²–10⁴ for bacterial populations and 10³–10⁵ for an archaeon, which is more comparable to Eukaryotic representatives’ humans and yeast. Furthermore, we use the linearity between the metaproteome and metatranscriptome over time to identify core functional guilds, hence using a fundamental biological feature (i.e., RNA/protein levels) to highlight phenotypical complementarity. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.

Here, the authors perform a temporal multi-omic analysis of a minimalistic cellulose-degrading and methane-producing consortium at the strain level and estimate protein-to-RNA ratios and RNA-protein dynamics of the community simultaneously over time.

How Do the Different Proteomic Strategies Cope with the Complexity of Biological Regulations in a Multi-Omic World? Critical Appraisal and Suggestions for Improvements

In this second decade of the 21st century, we are lucky enough to have different types of proteomic analyses at our disposal. Furthermore, other functional omics such as transcriptomics have also undergone major developments, resulting in mature tools. However, choice equals questions, and the major question is how each proteomic strategy is fit for which purpose. The aim of this opinion paper is to reposition the various proteomic strategies in the frame of what is known in terms of biological regulations in order to shed light on the power, limitations, and paths for improvement for the different proteomic setups. This should help biologists to select the best-suited proteomic strategy for their purposes in order not to be driven by raw availability or fashion arguments but rather by the best fitness for purpose. In particular, knowing the limitations of the different proteomic strategies helps in interpreting the results correctly and in devising the validation experiments that should be made downstream of the proteomic analyses.

The Membrane Protein Sortilin Can Be Targeted to Inhibit Pancreatic Cancer Cell Invasion

Pancreatic cancer has a dismal prognosis, and there is no targeted therapy against this malignancy. The neuronal membrane protein sortilin is emerging as a regulator of cancer cell development, but its expression and impact in pancreatic cancer are unknown. This study found that sortilin expression was higher in pancreatic cell lines versus normal pancreatic ductal epithelial cells, as shown by Western blot analysis and mass spectrometry. The increased sortilin level in pancreatic cancer cells was confirmed by immunohistochemistry in a series of 99 human pancreatic adenocarcinomas versus 48 normal pancreatic tissues (P = 0.0014). Sortilin inhibition by siRNA and the pharmacologic inhibitor AF38469 strongly reduced the adhesion and invasion of pancreatic cancer cells without affecting cell survival and viability. Sortilin inhibition also decreased the phosphorylation of the focal adhesion kinase in Tyr925. Together, these data show that sortilin contributes to pancreatic cancer invasion and could eventually be targeted in therapy.