Population-scale proteome variation in human induced pluripotent stem cells

A primate-specific endogenous retroviral envelope protein sequesters SFRP2 to regulate human cardiomyocyte development

Endogenous retroviruses (ERVs) occupy a significant part of the human genome, with some encoding proteins that influence the immune system or regulate cell-cell fusion in early extra-embryonic development. However, whether ERV-derived proteins regulate somatic development is unknown. Here, we report a somatic developmental function for the primate-specific ERVH48-1 (SUPYN/Suppressyn). ERVH48-1 encodes a fragment of a viral envelope that is expressed during early embryonic development. Loss of ERVH48-1 led to impaired mesoderm and cardiomyocyte commitment and diverted cells to an ectoderm-like fate. Mechanistically, ERVH48-1 is localized to sub-cellular membrane compartments through a functional N-terminal signal peptide and binds to the WNT antagonist SFRP2 to promote its polyubiquitination and degradation, thus limiting SFRP2 secretion and blocking repression of WNT/β-catenin signaling. Knockdown of SFRP2 or expression of a chimeric SFRP2 with the ERVH48-1 signal peptide rescued cardiomyocyte differentiation. This study demonstrates how ERVH48-1 modulates WNT/β-catenin signaling and cell type commitment in somatic development.

Promises and Challenges of populational Proteomics in Health and Disease

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

On-Chip Neural Induction Boosts Neural Stem Cell Commitment: Toward a Pipeline for iPSC-Based Therapies

The clinical translation of induced pluripotent stem cells (iPSCs) holds great potential for personalized therapeutics. However, one of the main obstacles is that the current workflow to generate iPSCs is expensive, time-consuming, and requires standardization. A simplified and cost-effective microfluidic approach is presented for reprogramming fibroblasts into iPSCs and their subsequent differentiation into neural stem cells (NSCs). This method exploits microphysiological technology, providing a 100-fold reduction in reagents for reprogramming and a ninefold reduction in number of input cells. The iPSCs generated from microfluidic reprogramming of fibroblasts show upregulation of pluripotency markers and downregulation of fibroblast markers, on par with those reprogrammed in standard well-conditions. The NSCs differentiated in microfluidic chips show upregulation of neuroectodermal markers (ZIC1, PAX6, SOX1), highlighting their propensity for nervous system development. Cells obtained on conventional well plates and microfluidic chips are compared for reprogramming and neural induction by bulk RNA sequencing. Pathway enrichment analysis of NSCs from chip showed neural stem cell development enrichment and boosted commitment to neural stem cell lineage in initial phases of neural induction, attributed to a confined environment in a microfluidic chip. This method provides a cost-effective pipeline to reprogram and differentiate iPSCs for therapeutics compliant with current good manufacturing practices.

Patient-derived induced pluripotent stem cells with a MERTK mutation exhibit cell junction abnormalities and aberrant cellular differentiation potential

BACKGROUND

Human induced pluripotent stem cell (hiPSC) technology is a valuable tool for generating patient-specific stem cells, facilitating disease modeling, and investigating disease mechanisms. However, iPSCs carrying specific mutations may limit their clinical applications due to certain inherent characteristics.

AIM

To investigate the impact of MERTK mutations on hiPSCs and determine whether hiPSC-derived extracellular vesicles (EVs) influence anomalous cell junction and differentiation potential.

METHODS

We employed a non-integrating reprogramming technique to generate peripheral blood-derived hiPSCs with and hiPSCs without a MERTK mutation. Chromosomal karyotype analysis, flow cytometry, and immunofluorescent staining were utilized for hiPSC identification. Transcriptomics and proteomics were employed to elucidate the expression patterns associated with cell junction abnormalities and cellular differentiation potential. Additionally, EVs were isolated from the supernatant, and their RNA and protein cargos were examined to investigate the involvement of hiPSC-derived EVs in stem cell junction and differentiation.

RESULTS

The generated hiPSCs, both with and without a MERTK mutation, exhibited normal karyotype and expressed pluripotency markers; however, hiPSCs with a MERTK mutation demonstrated anomalous adhesion capability and differentiation potential, as confirmed by transcriptomic and proteomic profiling. Furthermore, hiPSC-derived EVs were involved in various biological processes, including cell junction and differentiation.

CONCLUSION

HiPSCs with a MERTK mutation displayed altered junction characteristics and aberrant differentiation potential. Furthermore, hiPSC-derived EVs played a regulatory role in various biological processes, including cell junction and differentiation.

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.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Heritability of Gene Expression Measured from Peripheral Blood in Older Adults

The contributions of genetic variation and the environment to gene expression may change across the lifespan. However, few studies have investigated the heritability of blood gene expression in older adults. The current study therefore aimed to investigate this question in a community sample of older adults. A total of 246 adults (71 MZ and 52 DZ twins, 69.91% females; mean age-75.79 ± 5.44) were studied. Peripheral blood gene expression was assessed using Illumina microarrays. A heritability analysis was performed using structural equation modelling. There were 5269 probes (19.9%) from 4603 unique genes (23.9%) (total 26,537 probes from 19,256 genes) that were significantly heritable (mean h2 = 0.40). A pathway analysis of the top 10% of significant genes showed enrichment for the immune response and ageing-associated genes. In a comparison with two other gene expression twin heritability studies using adults from across the lifespan, there were 38 out of 9479 overlapping genes that were significantly heritable. In conclusion, our study found ~24% of the available genes for analysis were heritable in older adults, with only a small number common across studies that used samples from across adulthood, indicating the importance of examining gene expression in older age groups.

Proteomics applications in next generation induced pluripotent stem cell models

INTRODUCTION

Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations.

AREAS COVERED

We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems.

EXPERT OPINIONS

We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.

Reliable multiplex generation of pooled induced pluripotent stem cells

Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.

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.

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

iPSC-derived retinal pigmented epithelial cells from patients with macular telangiectasia show decreased mitochondrial function

Patient-derived induced pluripotent stem cells (iPSCs) provide a powerful tool for identifying cellular and molecular mechanisms of disease. Macular telangiectasia type 2 (MacTel) is a rare, late-onset degenerative retinal disease with an extremely heterogeneous genetic architecture, lending itself to the use of iPSCs. Whole-exome sequencing screens and pedigree analyses have identified rare causative mutations that account for less than 5% of cases. Metabolomic surveys of patient populations and GWAS have linked MacTel to decreased circulating levels of serine and elevated levels of neurotoxic 1-deoxysphingolipids (1-dSLs). However, retina-specific, disease-contributing factors have yet to be identified. Here, we used iPSC-differentiated retinal pigmented epithelial (iRPE) cells derived from donors with or without MacTel to screen for novel cell-intrinsic pathological mechanisms. We show that MacTel iRPE cells mimicked the low serine levels observed in serum from patients with MacTel. Through RNA-Seq and gene set enrichment pathway analysis, we determined that MacTel iRPE cells are enriched in cellular stress pathways and dysregulation of central carbon metabolism. Using respirometry and mitochondrial stress testing, we functionally validated that MacTel iRPE cells had a reduction in mitochondrial function that was independent of defects in serine biosynthesis and 1-dSL accumulation. Thus, we identified phenotypes that may constitute alternative disease mechanisms beyond the known serine/sphingolipid pathway.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Genetic dissection of the pluripotent proteome through multi-omics data integration

Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

Most human genes are co-expressed with a nearby gene. Previous studies have revealed this local gene co-expression to be widespread across chromosomes and across dozens of tissues. Yet, so far these studies used bulk RNA-seq, averaging gene expression measurements across millions of cells, thus being unclear if this co-expression stems from transcription events in single cells. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell-type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover >3800 co-expressed gene pairs in two human cell types, induced pluripotent stem cells (iPSCs) and lymphoblastoid cell lines (LCLs) and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally related genes and (iii) using proteomics data, provide evidence that their co-expression is maintained up to the protein level. Finally, using single cell RNA-sequencing (scRNA-seq) and single cell ATAC-sequencing (scATAC-seq) data for the same single cells, we identify gene-enhancer associations and reveal that >95% of co-expressed gene pairs share regulatory elements. These results elucidate the potential reasons for co-expression in single cell gene regulatory networks and warrant a deeper study of shared regulatory elements, in view of explaining disease comorbidity due to affecting several genes. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

Using single-cell data from cell lines, the co-expression of genes and co-activity of regulatory elements is analyzed, providing insight into shared architecture and regulation between genes.

Plant proteostasis: a proven and promising target for crop improvement

The Green Revolution of the 1960s accomplished dramatic increases in crop yields through genetic improvement, chemical fertilisers, irrigation, and mechanisation. However, the current trajectory of population growth, against a backdrop of climate change and geopolitical unrest, predicts that agricultural production will be insufficient to ensure global food security in the next three decades. Improvements to crops that go beyond incremental gains are urgently needed. Plant biology has also undergone a revolution in recent years, through the development and application of powerful technologies including genome sequencing, a pantheon of 'omics techniques, precise genome editing, and step changes in structural biology and microscopy. Proteostasis - the collective processes that control the protein complement of the cell, comprising synthesis, modification, localisation, and degradation - is a field that has benefitted from these advances. This special issue presents a selection of the latest research in this vibrant field, with a particular focus on protein degradation. In the current article, we highlight the diverse and widespread contributions of plant proteostasis to agronomic traits, suggest opportunities and strategies to manipulate different elements of proteostatic mechanisms for crop improvement, and discuss the challenges involved in bringing these ideas into practice.

Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration

There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.

Cellular and Engineered Organoids for Cardiovascular Models

An ensemble of in vitro cardiac tissue models has been developed over the past several decades to aid our understanding of complex cardiovascular disorders using a reductionist approach. These approaches often rely on recapitulating single or multiple clinically relevant end points in a dish indicative of the cardiac pathophysiology. The possibility to generate disease-relevant and patient-specific human induced pluripotent stem cells has further leveraged the utility of the cardiac models as screening tools at a large scale. To elucidate biological mechanisms in the cardiac models, it is critical to integrate physiological cues in form of biochemical, biophysical, and electromechanical stimuli to achieve desired tissue-like maturity for a robust phenotyping. Here, we review the latest advances in the directed stem cell differentiation approaches to derive a wide gamut of cardiovascular cell types, to allow customization in cardiac model systems, and to study diseased states in multiple cell types. We also highlight the recent progress in the development of several cardiovascular models, such as cardiac organoids, microtissues, engineered heart tissues, and microphysiological systems. We further expand our discussion on defining the context of use for the selection of currently available cardiac tissue models. Last, we discuss the limitations and challenges with the current state-of-the-art cardiac models and highlight future directions.

Diverse impact of N-acetylcysteine or alpha-lipoic acid supplementation during high-fat diet regime on fatty acid transporters in visceral and subcutaneous adipose tissue

PURPOSE

Adipose tissue's (AT) structural changes accompanying obesity may alter lipid transport protein expression and, thus, the fatty acids (FAs) transport and lipid balance of the body. Metabolic abnormalities within AT contribute to the elevated production of reactive oxygen species and increased oxidative/nitrosative stress. Although compounds such as N-acetylcysteine (NAC) and α-lipoic acid (ALA), which restore redox homeostasis, may improve lipid metabolism in AT, the mechanism of action of these antioxidants on lipid metabolism in AT is still unknown. This study aimed to examine the impact of NAC and ALA on the level and FA composition of the lipid fractions, and the expression of FA transporters in the visceral and subcutaneous AT of high-fat diet-fed rats.

MATERIALS AND METHODS

Male Wistar rats were randomly divided into four groups. The mRNA levels and protein expression of FA transporters were assessed using real-time PCR and Western Blot analyses. The collected samples were subjected to histological evaluation. The level of lipids (FFA, DAG, and TAG) was measured using gas-liquid chromatography.

RESULTS

We found that antioxidants affect FA transporter expressions at both the transcript and protein levels, and, therefore, they promote changes in AT's lipid pools. One of the most remarkable findings of our research is that different antioxidant molecules may have a varying impact on AT phenotype.

CONCLUSION

NAC and ALA exert different influences on AT, which is reflected in histopathological images, FA transport proteins expression patterns, or even the lipid storage capacity of adipocytes.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Decoding the cellular effects of genetic variation through interaction proteomics

It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.

Coding and regulatory variants are associated with serum protein levels and disease

Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases.

Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation

Atrial fibrillation (AF) is an abnormal heart rhythm related to an increased risk of heart failure, dementia, and stroke. The distinction between valvular and non-valvular AF remains a debate. In this study, proteomics and metabolomics were integrated to describe the dysregulated metabolites and proteins of AF patients relative to sinus rhythm (SR) patients. Totally 47 up-regulated and 41 down-regulated proteins in valvular AF, and 59 up-regulated and 149 down-regulated proteins in non-valvular AF were recognized in comparison to SR patients. Moreover, 58 up-regulated and 49 significantly down-regulated metabolites in valvular AF, and 47 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF patients were identified in comparison to SR patients. Based on analysis of differential levels of metabolites and proteins, 15 up-regulated and 22 down-regulated proteins, and 13 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF were identified relative to valvular AF. KEGG pathway enrichment analysis showed the altered proteins and metabolites were significantly related to multiple metabolic pathways, such as Glycolysis/Gluconeogenesis. Interestingly, the enrichment pathways related to non-valvular AF were obviously different from those in valvular AF. For example, valvular AF was significantly related to Glycolysis/Gluconeogenesis, but non-valvular AF was more related to Citrate cycle (TCA cycle). Correlation analysis between the differentially expressed proteins and metabolites was also performed. Several hub proteins with metabolites were identified in valvular AF and non-valvular AF. For example, Taurine, D-Threitol, L-Rhamnose, and DL-lactate played crucial roles in valvular AF, while Glycerol-3-phosphate dehydrogenase, Inorganic pyrophosphatase 2, Hydroxymethylglutaryl-CoAlyase, and Deoxyuridine 5-triphosphate nucleotidohydrolase were crucial in non-valvular AF. Then two hub networks were recognized as potential biomarkers, which can effectively distinguish valvular AF and non-valvular persistent AF from SR samples, with areas under curve of 0.75 and 0.707, respectively. Hence, these metabolites and proteins can be used as potential clinical molecular markers to discriminate two types of AF from SR samples. In summary, this study provides novel insights to understanding the mechanisms of AF progression and identifying novel biomarkers for prognosis of non-valvular AF and valvular AF by using metabolomics and proteomics analyses.

Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation

Quantitative analysis of human induced pluripotent stem cell (iPSC) lines from healthy donors is a powerful tool for uncovering the relationship between genetic variants and cellular behavior. We previously identified rare, deleterious non-synonymous single nucleotide variants (nsSNVs) in cell adhesion genes that are associated with outlier iPSC phenotypes in the pluripotent state. Here, we generated micropatterned colonies of iPSCs to test whether nsSNVs influence patterning of radially ordered germ layers. Using a custom-built image analysis pipeline, we quantified the differentiation phenotypes of 13 iPSC lines that harbor nsSNVs in genes related to cell adhesion or germ layer development. All iPSC lines differentiated into the three germ layers; however, there was donor-specific variation in germ layer patterning. We identified one line that presented an outlier phenotype of expanded endodermal differentiation, which was associated with a nsSNV in ITGB1. Our study establishes a platform for investigating the impact of nsSNVs on differentiation.

Transposable element sequence fragments incorporated into coding and noncoding transcripts modulate the transcriptome of human pluripotent stem cells

Transposable elements (TEs) occupy nearly 40% of mammalian genomes and, whilst most are fragmentary and no longer capable of transposition, they can nevertheless contribute to cell function. TEs within genes transcribed by RNA polymerase II can be copied as parts of primary transcripts; however, their full contribution to mature transcript sequences remains unresolved. Here, using long and short read (LR and SR) RNA sequencing data, we show that 26% of coding and 65% of noncoding transcripts in human pluripotent stem cells (hPSCs) contain TE-derived sequences. Different TE families are incorporated into RNAs in unique patterns, with consequences to transcript structure and function. The presence of TE sequences within a transcript is correlated with TE-type specific changes in its subcellular distribution, alterations in steady-state levels and half-life, and differential association with RNA Binding Proteins (RBPs). We identify hPSC-specific incorporation of endogenous retroviruses (ERVs) and LINE:L1 into protein-coding mRNAs, which generate TE sequence-derived peptides. Finally, single cell RNA-seq reveals that hPSCs express ERV-containing transcripts, whilst differentiating subpopulations lack ERVs and express SINE and LINE-containing transcripts. Overall, our comprehensive analysis demonstrates that the incorporation of TE sequences into the RNAs of hPSCs is more widespread and has a greater impact than previously appreciated.

Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with “healthy” or “diseased” states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions.

How do can one improve the classification of genetic variants as harmful or harmless? This study uses a robust statistical analysis to exploit the interplay between protein structure, proteomic measurements and functional pathways to enable better discrimination between missense variants in health and disease.

Erosion of human X chromosome inactivation causes major remodeling of the iPSC proteome

X chromosome inactivation (XCI) is a dosage compensation mechanism in female mammals whereby transcription from one X chromosome is repressed. Analysis of human induced pluripotent stem cells (iPSCs) derived from female donors identified that low levels of XIST RNA correlated strongly with erosion of XCI. Proteomic analysis, RNA sequencing (RNA-seq), and polysome profiling showed that XCI erosion resulted in amplified RNA and protein expression from X-linked genes, providing a proteomic characterization of skewed dosage compensation. Increased protein expression was also detected from autosomal genes without an mRNA increase, thus altering the protein-RNA correlation between the X chromosome and autosomes. XCI-eroded lines display an ∼13% increase in total cell protein content, with increased ribosomal proteins, ribosome biogenesis and translation factors, and polysome levels. We conclude that XCI erosion in iPSCs causes a remodeling of the proteome, affecting the expression of a much wider range of proteins and disease-linked loci than previously realized.

Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

Haplotype-resolved diverse human genomes and integrated analysis of structural variation

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation

Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.