Defining the consequences of genetic variation on a proteome-wide scale

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for sarcopenia

BACKGROUND

There are no effective pharmacological treatments for sarcopenia. We aim to identify potential therapeutic targets for sarcopenia by integrating various publicly available datasets.

METHODS

We integrated druggable genome data, cis-eQTL/cis-pQTL from human blood and skeletal muscle tissue, and GWAS summary data of sarcopenia-related traits to analyse the potential causal relationships between drug target genes and sarcopenia using the Mendelian Randomization (MR) method. Sensitivity analyses and Bayesian colocalization were employed to validate the causal relationships. We also assessed the side effects or additional indications of the identified drug targets using a phenome-wide MR (Phe-MR) approach and investigated actionable drugs for target genes using available databases.

RESULTS

MR analysis identified 17 druggable genes with potential causation to sarcopenia in human blood or skeletal muscle tissue. Six of them (HP, HLA-DRA, MAP 3K3, MFGE8, COL15A1, and AURKA) were further confirmed by Bayesian colocalization (PPH4 > 90%). The up-regulation of HP [higher ALM (beta: 0.012, 95% CI: 0.007-0.018, P = 1.2*10-5) and higher grip strength (OR: 0.96, 95% CI: 0.94-0.98, P = 4.2*10-5)], MAP 3K3 [higher ALM (beta: 0.24, 95% CI: 0.21-0.26, P = 1.8*10-94), higher grip strength (OR: 0.82, 95% CI: 0.75-0.90, P = 2.1*10-5), and faster walking pace (beta: 0.03, 95% CI: 0.02-0.05, P = 8.5*10-6)], and MFGE8 [higher ALM (muscle eQTL, beta: 0.09, 95% CI: 0.06-0.11, P = 6.1*10-13; blood pQTL, beta: 0.05, 95% CI: 0.03-0.07, P = 3.8*10-09)], as well as the down-regulation of HLA-DRA [lower ALM (beta: -0.09, 95% CI: -0.11 to -0.08, P = 5.4*10-36) and lower grip strength (OR: 1.13, 95% CI: 1.07-1.20, P = 1.8*10-5)] and COL15A1 [higher ALM (muscle eQTL, beta: -0.07, 95% CI: -0.10 to -0.04, P = 3.4*10-07; blood pQTL, beta: -0.05, 95% CI: -0.06 to -0.03, P = 1.6*10-07)], decreased the risk of sarcopenia. AURKA in blood (beta: -0.16, 95% CI: -0.22 to -0.09, P = 2.1*10-06) and skeletal muscle (beta: 0.03, 95% CI: 0.02 to 0.05, P = 5.3*10-05) tissues showed an inverse relationship with sarcopenia risk. The Phe-MR indicated that the six potential therapeutic targets for sarcopenia had no significant adverse effects. Drug repurposing analysis supported zinc supplementation and collagenase clostridium histolyticum might be potential therapeutics for sarcopenia by activating HP and inhibiting COL15A1, respectively.

CONCLUSIONS

Our research indicated MAP 3K3, MFGE8, COL15A1, HP, and HLA-DRA may serve as promising targets for sarcopenia, while the effectiveness of zinc supplementation and collagenase clostridium histolyticum for sarcopenia requires further validation.

Instrumental variable and colocalization analyses identify endotrophin and HTRA1 as potential therapeutic targets for coronary artery disease

Coronary artery disease (CAD) remains a leading cause of disease burden globally, and there is a persistent need for new therapeutic targets. Instrumental variable (IV) and genetic colocalization analyses can help identify novel therapeutic targets for human disease by nominating causal genes in genome-wide association study (GWAS) loci. We conducted cis-IV analyses for 20,125 genes and 1,746 plasma proteins with CAD using molecular trait quantitative trait loci variant (QTLs) data from three different studies. 19 proteins and 119 genes were significantly associated with CAD risk by IV analyses and demonstrated evidence of genetic colocalization. Notably, our analyses validated well-established targets such as PCSK9 and ANGPTL4 while also identifying HTRA1 and endotrophin (a cleavage product of COL6A3) as proteins whose levels are causally associated with CAD risk. Further experimental studies are needed to confirm the causal role of the genes and proteins identified through our multiomic cis-IV analyses on human disease.

Causal inference of the effect of blood proteome on the risk of head and neck cancer: two-sample Mendelian randomization

Early diagnosis of head and neck cancer can improve therapeutic outcomes but remains a challenge. The blood proteome can comprise a key source of biomarkers that enable the early diagnosis and precision medicine in head and neck cancer, but blood protein biomarkers of head and neck cancer are not well delineated. Here we applied two-sample Mendelian randomization to a GWAS dataset of 1478 blood proteins and large dataset of head and neck cancer cases and controls to identify blood proteome traits associated with head and neck cancer. Multiple two-sample Mendelian randomization (MR) methods were used to assess causal effects of the exposures, including: Inverse-variance weighted (IVW), Mendelian randomization-Egger method, Weight Median method, simple mode, weight mode. Sensitivity analysis was performed by using heterogeneity test, pleiotropy test and one-by-one exclusion test. Multivariable MR analyses were performed to assess the effects of obesity, diabetes mellitus, and smoking. A significant causal association between A Disintegrin and metalloproteinase domain-containing protein 23 (ADAM23) and head and neck cancer was noted. The sensitivity analysis indicated no significant bias. Multivariate analysis showed that the effect for ADAM23 remained significant after adjusting for the indirect effects of obesity, diabetes mellitus and smoking. In sum, this study showed a significant causal role of genetically dysregulated ADAM23 protein with head and neck cancer risk. The specific mechanisms underlying the role of ADAM23 in mediating head and neck cancer risk, and its role as a potential therapeutic target and biomarker, need further investigation.

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Screening of Poria cocos polysaccharide with immunomodulatory activity and its activation effects on TLR4/MD2/NF-κB pathway

PCP-W1, the Poria cocos polysaccharide with the strong immunomodulatory activity, was isolated through column chromatography and screened for in vitro immune activity in RAW 264.7 cells in this study. The structure analysis results revealed that the PCP-W1 were composed of galactose, glucose, fucose and mannose in a molar percentage of 35.87: 28.56: 21.77: 13.64. And it exhibited a random coil and branched conformational features with a molecular weight of 18.38 kDa. The main chain consisted of residues→3)-β-D-Glcp-(1 → 3,6)-β-D-Glcp-(1 → 3)-β-D-Glcp-(1 → 6)-β-D-Glcp-(1 → 6)-α-D-Galp-(1 → 6)-α-D-Galp-(1 → 2,6)-α-D-Galp-(1→6)-α-D-Galp-(1 → 6)-α-D-Galp-(1 → , while branching occurred at β-D-Glcp-(1→, α-D-Manp-(1→, and α-L-Fucp-(1 → 3)- α-L-Fucp-(1→. The pharmacodynamic studies demonstrated that PCP-W1 activated the release of NO, IL-6, IL-β, TNF-α, CD86, and ROS to induce polarization of RAW 264.7 murine macrophages towards M1-type through modulation of the TLR4/MD2/NF-κB pathway. The molecular docking results showed that PCP-W1 could primarily dock onto the hydrophobic binding site of TLR4/MD2 complex via its galactose chain. Furthermore, molecular dynamics simulation displayed stable modeling for TLR4-MD2-PCP-W1 complex. Overall, we screened the most immunoactive components of the polysaccharide, analyzed its structure, demonstrated its impact on TLR4/MD2/NF-kB pathway, and studied the interaction between TLR4/MD2 and the polysaccharide fragments. These results provide further support for the structure-activity relationship study of the immunomodulatory effects of Poria cocos polysaccharide.

Ferroptosis regulates hemolysis in stored murine and human red blood cells

Red blood cell (RBC) metabolism regulates hemolysis during aging in vivo and in the blood bank. Here, we leveraged a diversity outbred mouse population to map the genetic drivers of fresh/stored RBC metabolism and extravascular hemolysis upon storage and transfusion in 350 mice. We identify the ferrireductase Steap3 as a critical regulator of a ferroptosis-like process of lipid peroxidation. Steap3 polymorphisms were associated with RBC iron content, in vitro hemolysis, and in vivo extravascular hemolysis both in mice and 13,091 blood donors from the Recipient Epidemiology and Donor evaluation Study. Using metabolite Quantitative Trait Loci analyses, we identified a network of gene products (FADS1/2, EPHX2 and LPCAT3) - enriched in donors of African descent - associated with oxylipin metabolism in stored human RBCs and related to Steap3 or its transcriptional regulator, the tumor protein TP53. Genetic variants were associated with lower in vivo hemolysis in thousands of single-unit transfusion recipients.

Highlights

Steap3 regulates lipid peroxidation and extravascular hemolysis in 350 diversity outbred miceSteap3 SNPs are linked to RBC iron, hemolysis, vesiculation in 13,091 blood donorsmQTL analyses of oxylipins identified ferroptosis-related gene products FADS1/2, EPHX2, LPCAT3Ferroptosis markers are linked to hemoglobin increments in transfusion recipients.

Graphical abstract

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

An eQTL-based approach reveals candidate regulators of LINE-1 RNA levels in lymphoblastoid cells

Long interspersed element 1 (LINE-1; L1) are a family of transposons that occupy ~17% of the human genome. Though a small number of L1 copies remain capable of autonomous transposition, the overwhelming majority of copies are degenerate and immobile. Nevertheless, both mobile and immobile L1s can exert pleiotropic effects (promoting genome instability, inflammation, or cellular senescence) on their hosts, and L1's contributions to aging and aging diseases is an area of active research. However, because of the cell type-specific nature of transposon control, the catalogue of L1 regulators remains incomplete. Here, we employ an eQTL approach leveraging transcriptomic and genomic data from the GEUVADIS and 1000Genomes projects to computationally identify new candidate regulators of L1 RNA levels in lymphoblastoid cell lines. To cement the role of candidate genes in L1 regulation, we experimentally modulate the levels of top candidates in vitro, including IL16, STARD5, HSD17B12, and RNF5, and assess changes in TE family expression by Gene Set Enrichment Analysis (GSEA). Remarkably, we observe subtle but widespread upregulation of TE family expression following IL16 and STARD5 overexpression. Moreover, a short-term 24-hour exposure to recombinant human IL16 was sufficient to transiently induce subtle, but widespread, upregulation of L1 subfamilies. Finally, we find that many L1 expression-associated genetic variants are co-associated with aging traits across genome-wide association study databases. Our results expand the catalogue of genes implicated in L1 RNA control and further suggest that L1-derived RNA contributes to aging processes. Given the ever-increasing availability of paired genomic and transcriptomic data, we anticipate this new approach to be a starting point for more comprehensive computational scans for regulators of transposon RNA levels.

Predicted mechanistic impacts of human protein missense variants

Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.

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.

Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.

Genetic context modulates aging and degeneration in the murine retina

Background

Age is the principal risk factor for neurodegeneration in both the retina and brain. The retina and brain share many biological properties; thus, insights into retinal aging and degeneration may shed light onto similar processes in the brain. Genetic makeup strongly influences susceptibility to age-related retinal disease. However, studies investigating retinal aging have not sufficiently accounted for genetic diversity. Therefore, examining molecular aging in the retina across different genetic backgrounds will enhance our understanding of human-relevant aging and degeneration in both the retina and brain-potentially improving therapeutic approaches to these debilitating conditions.

Methods

Transcriptomics and proteomics were employed to elucidate retinal aging signatures in nine genetically diverse mouse strains (C57BL/6J, 129S1/SvlmJ, NZO/HlLtJ, WSB/EiJ, CAST/EiJ, PWK/PhK, NOD/ShiLtJ, A/J, and BALB/cJ) across lifespan. These data predicted human disease-relevant changes in WSB and NZO strains. Accordingly, B6, WSB and NZO mice were subjected to human-relevant in vivo examinations at 4, 8, 12, and/or 18M, including: slit lamp, fundus imaging, optical coherence tomography, fluorescein angiography, and pattern/full-field electroretinography. Retinal morphology, vascular structure, and cell counts were assessed ex vivo.

Results

We identified common molecular aging signatures across the nine mouse strains, which included genes associated with photoreceptor function and immune activation. Genetic background strongly modulated these aging signatures. Analysis of cell type-specific marker genes predicted age-related loss of photoreceptors and retinal ganglion cells (RGCs) in WSB and NZO, respectively. Fundus exams revealed retinitis pigmentosa-relevant pigmentary abnormalities in WSB retinas and diabetic retinopathy (DR)-relevant cotton wool spots and exudates in NZO retinas. Profound photoreceptor dysfunction and loss were confirmed in WSB. Molecular analyses indicated changes in photoreceptor-specific proteins prior to loss, suggesting photoreceptor-intrinsic dysfunction in WSB. In addition, age-associated RGC dysfunction, loss, and concomitant microvascular dysfunction was observed in NZO mice. Proteomic analyses revealed an early reduction in protective antioxidant processes, which may underlie increased susceptibility to DR-relevant pathology in NZO.

Conclusions

Genetic context is a strong determinant of retinal aging, and our multi-omics resource can aid in understanding age-related diseases of the eye and brain. Our investigations identified and validated WSB and NZO mice as improved preclinical models relevant to common retinal neurodegenerative diseases.

Secreted Protein Profiling of Human Aortic Smooth Muscle Cells Identifies Vascular Disease Associations

BACKGROUND

Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular disease, the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a contractile to a synthetic phenotype characterized by an increased proliferation, migration, production of ECM (extracellular matrix) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of cardiovascular disease, including coronary artery disease, stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies.

METHODS

Using human aortic SMCs from 123 multiancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted liquid chromatography-tandem mass spectrometry-based proteomic analysis of the conditioned media.

RESULTS

We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 (latent-transforming growth factor beta-binding protein 1) in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions.

CONCLUSIONS

Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.

Mining cholesterol genes from thousands of mouse livers identifies aldolase C as a regulator of cholesterol biosynthesis

The availability of genome-wide transcriptomic and proteomic datasets is ever-increasing and often not used beyond initial publication. Here, we applied module-based coexpression network analysis to a comprehensive catalog of 35 mouse genome-wide liver expression datasets (encompassing more than 3800 mice) with the goal of identifying and validating unknown genes involved in cholesterol metabolism. From these 35 datasets, we identified a conserved module of genes enriched with cholesterol biosynthetic genes. Using a systematic approach across the 35 datasets, we identified three genes (Rdh11, Echdc1, and Aldoc) with no known role in cholesterol metabolism. We then performed functional validation studies and show that each gene is capable of regulating cholesterol metabolism. For the glycolytic gene, Aldoc, we demonstrate that it contributes to de novo cholesterol biosynthesis and regulates cholesterol and triglyceride levels in mice. As Aldoc is located within a genome-wide significant genome-wide association studies locus for human plasma cholesterol levels, our studies establish Aldoc as a causal gene within this locus. Through our work, we develop a framework for leveraging mouse genome-wide liver datasets for identifying and validating genes involved in cholesterol metabolism.

Highly specific intracellular ubiquitination of a small molecule

Ubiquitin is a small, highly conserved protein that acts as a posttranslational modification in eukaryotes. Ubiquitination of proteins frequently serves as a degradation signal, marking them for disposal by the proteasome. Here, we report a novel small molecule from a diversity-oriented synthesis library, BRD1732, that is directly ubiquitinated in cells, resulting in dramatic accumulation of inactive ubiquitin monomers and polyubiquitin chains causing broad inhibition of the ubiquitin-proteasome system. Ubiquitination of BRD1732 and its associated cytotoxicity are stereospecific and dependent upon two homologous E3 ubiquitin ligases, RNF19A and RNF19B. Our finding opens the possibility for indirect ubiquitination of a target through a ubiquitinated bifunctional small molecule, and more broadly raises the potential for posttranslational modification in trans .

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Transport proteins and their differential roles in the accumulation of phenanthrene in wheat

The study focuses on the uptake, accumulation, and translocation of polycyclic aromatic hydrocarbons (PAHs) in cereals, specifically exploring the role of peroxidase (UniProt accession: A0A3B5XXD0, abbreviation: PX1) and unidentified protein (UniProt accession: A0A3B6LUC6, abbreviation: UP1) in phenanthrene solubilization within wheat xylem sap. This research aims to clarify the interactions between these proteins and phenanthrene. Employing both in vitro and in vivo analyses, we evaluated the solubilization capabilities of recombinant transport proteins for phenanthrene and examined the relationship between protein expression and phenanthrene concentration. UP1 displayed greater transport efficiency, while PX1 excelled at lower concentrations. Elevated PX1 levels contributed to phenanthrene degradation, marginally diminishing its transport. Spectral analyses and molecular dynamics simulations validated the formation of stable protein-phenanthrene complexes. The study offers crucial insights into PAH-related health risks in crops by elucidating the mechanisms of PAH accumulation facilitated by transport proteins.

Cloning, expression analysis and localization of DAZL gene implicated in germ cell development of male Hezuo pig

Deleted in azoospermia-like (DAZL) is essential for mammalian testicular function and spermatogenesis. To explore the molecular characterization, expression patterns, and cellular localization of the DAZL in Hezuo pig testes, testicular tissue was isolated from Hezuo pig at five development stages including 30 days old (30 d), 90 days old (90 d), 120 days old (120 d), 180 days old (180 d), and 240 days old (240 d). DAZL cDNA was first cloned using the RT-PCR method, and its molecular characterization was analyzed using relevant bioinformatics software. Subsequently, the expression patterns and cellular localization of DAZL were evaluated using quantitative real-time PCR (qRT-PCR), Western blot, and immunohistochemistry. The cloning and sequence analysis showed that the Hezuo pig DAZL cDNA fragment contained 888 bp open reading frame (ORF) capable of encoding 295 amino acid residues and exhibited high identities with some other mammals. The qRT-PCR and Western blot results indicated that DAZL was specifically expressed in Hezuo pig testes, and DAZL levels of both mRNA and protein were expressed at all five reproductive stages of Hezuo pig testes, with extremely significant higher expression levels in 90 d, 120 d, 180 d, and 240 d than those in 30 d (p < 0.01). Additionally, immunohistochemistry results revealed that DAZL protein was mainly localized in gonocytes at 30 d testes, primary spermatocytes, and spermatozoon at other developmental stages, and Leydig cells throughout five development stages. Together, these results suggested that DAZL may play an important role by regulating the proliferation or differentiation of gonocytes, development of primary spermatocytes and spermatozoon, and functional maintenance of Leydig cells in testicular development and spermatogenesis of Hezuo pig. Nevertheless, the specific regulatory mechanisms underlying these phenomena still requires further investigated and verified.

An eQTL-based Approach Reveals Candidate Regulators of LINE-1 RNA Levels in Lymphoblastoid Cells

Long interspersed element 1 (L1) are a family of autonomous, actively mobile transposons that occupy ~17% of the human genome. A number of pleiotropic effects induced by L1 (promoting genome instability, inflammation, or cellular senescence) have been observed, and L1's contributions to aging and aging diseases is an area of active research. However, because of the cell type-specific nature of transposon control, the catalogue of L1 regulators remains incomplete. Here, we employ an eQTL approach leveraging transcriptomic and genomic data from the GEUVADIS and 1000Genomes projects to computationally identify new candidate regulators of L1 RNA levels in lymphoblastoid cell lines. To cement the role of candidate genes in L1 regulation, we experimentally modulate the levels of top candidates in vitro, including IL16, STARD5, HSDB17B12, and RNF5, and assess changes in TE family expression by Gene Set Enrichment Analysis (GSEA). Remarkably, we observe subtle but widespread upregulation of TE family expression following IL16 and STARD5 overexpression. Moreover, a short-term 24-hour exposure to recombinant human IL16 was sufficient to transiently induce subtle, but widespread, upregulation of L1 subfamilies. Finally, we find that many L1 expression-associated genetic variants are co-associated with aging traits across genome-wide association study databases. Our results expand the catalogue of genes implicated in L1 RNA control and further suggest that L1-derived RNA contributes to aging processes. Given the ever-increasing availability of paired genomic and transcriptomic data, we anticipate this new approach to be a starting point for more comprehensive computational scans for transposon transcriptional regulators.

Mutation Impact on mRNA Versus Protein Expression across Human Cancers

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression has rarely been systematically investigated. We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across six cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins were experimentally confirmed as functional. Our study demonstrates the importance of considering protein-level expression to validate mutation impacts and identify functional genes and mutations.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Workability of mRNA Sequencing for Predicting Protein Abundance

Transcriptomics methods (RNA-Seq, PCR) today are more routine and reproducible than proteomics methods, i.e., both mass spectrometry and immunochemical analysis. For this reason, most scientific studies are limited to assessing the level of mRNA content. At the same time, protein content (and its post-translational status) largely determines the cell's state and behavior. Such a forced extrapolation of conclusions from the transcriptome to the proteome often seems unjustified. The ratios of "transcript-protein" pairs can vary by several orders of magnitude for different genes. As a rule, the correlation coefficient between transcriptome-proteome levels for different tissues does not exceed 0.3-0.5. Several characteristics determine the ratio between the content of mRNA and protein: among them, the rate of movement of the ribosome along the mRNA and the number of free ribosomes in the cell, the availability of tRNA, the secondary structure, and the localization of the transcript. The technical features of the experimental methods also significantly influence the levels of the transcript and protein of the corresponding gene on the outcome of the comparison. Given the above biological features and the performance of experimental and bioinformatic approaches, one may develop various models to predict proteomic profiles based on transcriptomic data. This review is devoted to the ability of RNA sequencing methods for protein abundance prediction.

Secreted protein profiling of human aortic smooth muscle cells identifies vascular disease associations

Background

Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular diseases (CVD), the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a "contractile" to a "synthetic" phenotype characterized by an increased proliferation, migration, production of extracellular matrix (ECM) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of CVD, including coronary artery disease (CAD), stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies (GWAS).

Methods

Using human aortic SMCs from 123 multi-ancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis of the conditioned media.

Results

We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping (pQTL) and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions.

Conclusions

Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.

ErbB4 processing is involved in OGD/R induced neuron injury

OBJECTIVE

Our previous study found that ErbB4 gene expression was changed after oxygen-glucose deprivation/reperfusion (OGD/R). However, the exact role and mechanism of ErbB4 in brain ischemia are largely unknown. In this study, we explored the protective effects of ErbB4 and its possible mechanism after OGD/R.

METHODS

Cerebral ischemia/reperfusion (I/R) injury model was established in vitro and in vivo. Cell viability, apoptosis, and ROS production were measured by MTT, TUNEL, and fluorescent probe 2', 7'-dichlorofluorescein diacetate (DCFH-DA). Infarct size was evaluated by TTC. We performed bioinformatics analyses to screen for novel key genes involved in ErbB4 changes. RNA-Seq was used to transcriptome analysis. RNA and protein expression were detected by quantitative RT‒PCR and western bloting.

RESULTS

The expression of 80-kDa ErbB4 decreased after cerebral I/R injury in vitro and in vivo. Co-expression network analysis revealed that ErbB4 expression was correlated with the changes in Adrb1, Adrb2, Ldlr, and Dab2. Quantitative RT‒PCR revealed that the mRNA expression levels of Adrb1, Adrb2, and Dab2 were upregulated, and that of Ldlr was decreased after OGD/R. Activation of ErbB4 expression by neuregulin 1 (NRG1) significantly promoted cell survival, attenuated hippocampal apoptosis, and decreased ROS production after OGD/R. Furthermore, the elimination of ErbB4 using a specific siRNA reversed these beneficial effects.

CONCLUSION

Our data revealed the neuroprotective effects of ErbB4 against OGD/R injury, and the action could be related to changes in the ErbB4 membrane-associated fragment and the expression of Adrb1, Adrb2, Ldlr, and Dab2.

Early Proteomic Characteristics and Changes in the Optic Nerve Head, Optic Nerve, and Retina in a Rat Model of Ocular Hypertension

The pathogenesis of glaucoma is still unknown. There are few studies on the dynamic change of tissue-specific and time-specific molecular pathophysiology caused by ocular hypertension (OHT). This study aimed to identify the early proteomic alterations in the retina, optic nerve head (ONH), and optic nerve (ON). After establishing a rat model of OHT, we harvested the tissues from control and glaucomatous eyes and analyzed the changes in protein expression using a multiplexed quantitative proteomics approach (TMT-MS3). Our study identified 6403 proteins after 1-day OHT and 4399 proteins after 7-days OHT in the retina, 5493 proteins after 1-day OHT and 4544 proteins after 7-days OHT in ONH, and 5455 proteins after 1-day OHT and 3835 proteins after 7-days OHT in the ON. Of these, 560 and 489 differential proteins were identified on day 1 and 7 after OHT in the retina, 428 and 761 differential proteins were identified on day 1 and 7 after OHT in the ONH, and 257 and 205 differential proteins on days 1 and 7 after OHT in the ON. Computational analysis on day 1 and 7 of OHT revealed that alpha-2 macroglobulin was upregulated across two time points and three tissues stably. The differentially expressed proteins between day 1 and 7 after OHT in the retina, ONH, and ON were associated with glutathione metabolism, mitochondrial dysfunction/oxidative phosphorylation, oxidative stress, microtubule, and crystallin. And the most significant change in retina are crystallins. We validated this proteomic result with the Western blot of crystallin proteins and found that upregulated on day 1 but recovered on day 7 after OHT, which are promising as therapeutic targets. These findings provide insights into the time- and region-order mechanisms that are specifically affected in the retina, ONH, and ON in response to elevated IOP during the early stages.

Potential drug targets for asthma identified in the plasma and brain through Mendelian randomization analysis

Background

Asthma is a heterogeneous disease, and the involvement of neurogenic inflammation is crucial in its development. The standardized treatments focus on alleviating symptoms. Despite the availability of medications for asthma, they have proven to be inadequate in controlling relapses and halting the progression of the disease. Therefore, there is a need for novel drug targets to prevent asthma.

Methods

We utilized Mendelian randomization to investigate potential drug targets for asthma. We analyzed summary statistics from the UK Biobank and then replicated our findings in GWAS data by Demenais et al. and the FinnGen cohort. We obtained genetic instruments for 734 plasma and 73 brain proteins from recently reported GWAS. Next, we utilized reverse causal relationship analysis, Bayesian co-localization, and phenotype scanning as part of our sensitivity analysis. Furthermore, we performed a comparison and protein-protein interaction analysis to identify causal proteins. We also analyzed the possible consequences of our discoveries by the given existing asthma drugs and their targets.

Results

Using Mendelian randomization analysis, we identified five protein-asthma pairs that were significant at the Bonferroni level (P < 6.35 × 10-5). Specifically, in plasma, we found that an increase of one standard deviation in IL1R1 and ECM1 was associated with an increased risk of asthma, while an increase in ADAM19 was found to be protective. The corresponding odds ratios were 1.03 (95% CI, 1.02-1.04), 1.00 (95% CI, 1.00-1.01), and 0.99 (95% CI, 0.98-0.99), respectively. In the brain, per 10-fold increase in ECM1 (OR, 1.05; 95% CI, 1.03-1.08) and PDLIM4 (OR, 1.05; 95% CI, 1.04-1.07) increased the risk of asthma. Bayesian co-localization found that ECM1 in the plasma (coloc.abf-PPH4 = 0.965) and in the brain (coloc.abf-PPH4 = 0.931) shared the same mutation with asthma. The target proteins of current asthma medications were found to interact with IL1R1. IL1R1 and PDLIM4 were validated in two replication cohorts.

Conclusion

Our integrative analysis revealed that asthma risk is causally affected by the levels of IL1R1, ECM1, and PDLIM4. The results suggest that these three proteins have the potential to be used as drug targets for asthma, and further investigation through clinical trials is needed.

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.

Identification of an alternative triglyceride biosynthesis pathway

Triacylglycerols (TAGs) are the main source of stored energy in the body, providing an important substrate pool for mitochondrial beta-oxidation. Imbalances in the amount of TAGs are associated with obesity, cardiac disease and various other pathologies1,2. In humans, TAGs are synthesized from excess, coenzyme A-conjugated fatty acids by diacylglycerol O-acyltransferases (DGAT1 and DGAT2)3. In other organisms, this activity is complemented by additional enzymes4, but whether such alternative pathways exist in humans remains unknown. Here we disrupt the DGAT pathway in haploid human cells and use iterative genetics to reveal an unrelated TAG-synthesizing system composed of a protein we called DIESL (also known as TMEM68, an acyltransferase of previously unknown function) and its regulator TMX1. Mechanistically, TMX1 binds to and controls DIESL at the endoplasmic reticulum, and loss of TMX1 leads to the unconstrained formation of DIESL-dependent lipid droplets. DIESL is an autonomous TAG synthase, and expression of human DIESL in Escherichia coli endows this organism with the ability to synthesize TAG. Although both DIESL and the DGATs function as diacylglycerol acyltransferases, they contribute to the cellular TAG pool under specific conditions. Functionally, DIESL synthesizes TAG at the expense of membrane phospholipids and maintains mitochondrial function during periods of extracellular lipid starvation. In mice, DIESL deficiency impedes rapid postnatal growth and affects energy homeostasis during changes in nutrient availability. We have therefore identified an alternative TAG biosynthetic pathway driven by DIESL under potent control by TMX1.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Genome-wide screen identifies host loci that modulate Mycobacterium tuberculosis fitness in immunodivergent mice

Genetic differences among mammalian hosts and among strains of Mycobacterium tuberculosis (Mtb) are well-established determinants of tuberculosis (TB) patient outcomes. The advent of recombinant inbred mouse panels and next-generation transposon mutagenesis and sequencing approaches has enabled dissection of complex host-pathogen interactions. To identify host and pathogen genetic determinants of Mtb pathogenesis, we infected members of the highly diverse BXD family of strains with a comprehensive library of Mtb transposon mutants (TnSeq). Members of the BXD family segregate for Mtb-resistant C57BL/6J (B6 or B) and Mtb-susceptible DBA/2J (D2 or D) haplotypes. The survival of each bacterial mutant was quantified within each BXD host, and we identified those bacterial genes that were differentially required for Mtb fitness across BXD genotypes. Mutants that varied in survival among the host family of strains were leveraged as reporters of "endophenotypes," each bacterial fitness profile directly probing specific components of the infection microenvironment. We conducted quantitative trait loci (QTL) mapping of these bacterial fitness endophenotypes and identified 140 host-pathogen QTL (hpQTL). We located a QTL hotspot on chromosome 6 (75.97-88.58 Mb) associated with the genetic requirement of multiple Mtb genes: Rv0127 (mak), Rv0359 (rip2), Rv0955 (perM), and Rv3849 (espR). Together, this screen reinforces the utility of bacterial mutant libraries as precise reporters of the host immunological microenvironment during infection and highlights specific host-pathogen genetic interactions for further investigation. To enable downstream follow-up for both bacterial and mammalian genetic research communities, all bacterial fitness profiles have been deposited into GeneNetwork.org and added into the comprehensive collection of TnSeq libraries in MtbTnDB.

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.

Pan-cancer analysis of G6PD carcinogenesis in human tumors

Glucose-6-phosphate dehydrogenase (G6PD) is involved in the catalytic pentose phosphate pathway (PPP), which is closely related to energy metabolism. G6PD plays a crucial role in many types of cancer, but the specific molecular mechanisms of G6PD in cancer remain unclear. Therefore, we investigated the potential oncogenic role of G6PD in various tumors based on The Cancer Genome Atlas (TCGA), the cBioPortal datasets, the University of California Santa Cruz (UCSC) Xena browser, and the UALCAN-based online tool. G6PD was highly expressed in several cancer tissues (hepatocellular carcinoma, glioma, and breast cancer) compared with normal tissues and was significantly associated with poor prognosis of hepatocellular carcinoma, clear cell renal cell carcinoma, and breast cancer. Promoter methylation levels of G6PD were lower in Bladder Urothelial Carcinoma (BLCA) (P = 2.77e-02), breast invasive carcinoma (BRCA) (P = 1.62e-12), kidney renal clear cell carcinoma (KIRC) (P = 4.23e-02), kidney renal papillary cell carcinoma (KIRP) (P = 2.64e-03), liver hepatocellular carcinoma (LIHC) (P = 1.76e-02), stomach adenocarcinoma (STAD) (P = 3.50e-02), testicular germ cell tumors (TGCT) (P = 1.62e-12), higher in prostate adenocarcinoma (PRAD) (P = 1.81e-09), and uterine corpus endometrial carcinoma (UCEC) (P = 2.96e-04) compared with corresponding normal tissue samples. G6PD expression was positively correlated with the infiltration level of immune cells in most tumors, suggesting that G6PD may be involved in tumor immune infiltration. In addition, the functional mechanism of G6PD also involves 'Carbon metabolism', 'Glycolysis/Gluconeogenesis', 'Pentose phosphate pathway', and 'Central carbon pathway metabolism in cancer signaling pathway'. This pan-cancer study provides a relatively broad understanding of the oncogenic role of G6PD in various tumors and presents a theoretical basis for the development of G6PD inhibitors as therapeutic drugs for multiple cancers.

Gene expression genetics of the striatum of Diversity Outbred mice

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

Global and tissue-specific aging effects on murine proteomes

Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues.

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development

Defining the mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the proteome at critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and demonstrated a functional role for the mevalonate pathway in regulating the cell cycle of embryonic cardiomyocytes. Overall, our proteomic datasets are a resource for studying events that regulate embryonic heart development and contribute to congenital heart disease.

Integrative analysis of hepatic transcriptional profiles reveals genetic regulation of atherosclerosis in hyperlipidemic Diversity Outbred-F1 mice

Atherogenesis is an insipidus but precipitating process leading to serious consequences of many cardiovascular diseases (CVD). Numerous genetic loci contributing to atherosclerosis have been identified in human genome-wide association studies, but these studies have limitations in the ability to control environmental factors and to decipher cause/effect relationships. To assess the power of hyperlipidemic Diversity Outbred (DO) mice in facilitating quantitative trait loci (QTL) analysis of complex traits, we generated a high-resolution genetic panel of atherosclerosis susceptible (DO-F1) mouse cohort by crossing 200 DO females with C57BL/6J males carrying two human genes: encoding apolipoprotein E3-Leiden and cholesterol ester transfer protein. We examined atherosclerotic traits including plasma lipids and glucose in the 235 female and 226 male progeny before and after 16 weeks of a high-fat/cholesterol diet, and aortic plaque size at 24 weeks. We also assessed the liver transcriptome using RNA-sequencing. Our QTL mapping for atherosclerotic traits identified one previously reported female-specific QTL on Chr10 with a narrower interval of 22.73 to 30.80 Mb, and one novel male-specific QTL at 31.89 to 40.25 Mb on Chr19. Liver transcription levels of several genes within each QTL were highly correlated with the atherogenic traits. A majority of these candidates have already known atherogenic potential in humans and/or mice, but integrative QTL, eQTL, and correlation analyses further pointed Ptprk as a major candidate of the Chr10 QTL, while Pten and Cyp2c67 of the Chr19 QTL in our DO-F1 cohort. Finally, through additional analyses of RNA-seq data we identified genetic regulation of hepatic transcription factors, including Nr1h3, contributes to atherogenesis in this cohort. Thus, an integrative approach using DO-F1 mice effectively validates the influence of genetic factors on atherosclerosis in DO mice and suggests an opportunity to discover therapeutics in the setting of hyperlipidemia.

Variation in histone configurations correlates with gene expression across nine inbred strains of mice

The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), as well as DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represents a distinct combination of the four histone modifications. We found that the epigenetic landscape is highly variable across the DO founders and is associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders, suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis-regulatory regions. Finally, we provide a data resource that documents strain-specific variation in the chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice.

Gene expression genetics of the striatum of Diversity Outbred mice

Brain transcriptional variation is a heritable trait that mediates complex behaviors, including addiction. Expression quantitative trait locus (eQTL) mapping reveals genomic regions harboring genetic variants that influence transcript abundance. In this study, we profiled transcript abundance in the striatum of 386 Diversity Outbred (J:DO) mice of both sexes using RNA-Seq. All mice were characterized using a behavioral battery of widely-used exploratory and risk-taking assays prior to transcriptional profiling. We performed eQTL mapping, incorporated the results into a browser-based eQTL viewer, and deposited co-expression network members in GeneWeaver. The eQTL viewer allows researchers to query specific genes to obtain allelic effect plots, analyze SNP associations, assess gene expression correlations, and apply mediation analysis to evaluate whether the regulatory variant is acting through the expression of another gene. GeneWeaver allows multi-species comparison of gene sets using statistical and combinatorial tools. This data resource allows users to find genetic variants that regulate differentially expressed transcripts and place them in the context of other studies of striatal gene expression and function in addiction-related behavior.

The trouble with triples: Examining the impact of measurement error in mediation analysis

Mediation analysis is used in genetic mapping studies to identify candidate gene mediators of quantitative trait loci (QTL). We consider genetic mediation analysis of triplets-sets of three variables consisting of a target trait, the genotype at a QTL for the target trait, and a candidate mediator that is the abundance of a transcript or protein whose coding gene co-locates with the QTL. We show that, in the presence of measurement error, mediation analysis can infer partial mediation even in the absence of a causal relationship between the candidate mediator and the target. We describe a measurement error model and a corresponding latent variable model with estimable parameters that are combinations of the causal effects and measurement errors across all three variables. The relative magnitudes of the latent variable correlations determine whether or not mediation analysis will tend to infer the correct causal relationship in large samples. We examine case studies that illustrate the common failure modes of genetic mediation analysis and demonstrate how to evaluate the effects of measurement error. While genetic mediation analysis is a powerful tool for identifying candidate genes, we recommend caution when interpreting mediation analysis findings.

Localization and expression of SLX4 in the testis of sterile male cattle-yak

Cattle-yak, the hybrid offspring of yak (Bos grunniens) and cattle (Bos taurus), serves as a unique model to dissect the molecular mechanisms underlying reproductive isolation. While female cattle-yaks are fertile, the males are completely sterile due to spermatogenic arrest at the meiosis stage and massive germ cell apoptosis. Interestingly, meiotic defects are partially rescued in the testes of backcrossed offspring. The genetic basis of meiotic defects in male cattle-yak remains unclear. Structure-specific endonuclease subunit (SLX4) participates in meiotic double-strand break (DSB) formation in mice, and its deletion results in defects in spermatogenesis. In the present study, we examined the expression patterns of SLX4 in the testes of yak, cattle-yak, and backcrossed offspring to investigate its potential roles in hybrid sterility. The results showed that the relative abundances of SLX4 mRNA and protein were significantly reduced in the testis of cattle-yak. The results of immunohistochemistry revealed that SLX4 was predominately expressed in spermatogonia and spermatocytes. Chromosome spreading experiments showed that SLX4 was significantly decreased in the pachytene spermatocytes of cattle-yak compared with yak and backcrossed offspring. These findings suggest that SLX4 expression was dysregulated in the testis of cattle-yak, potentially resulting in the failure of crossover formation and collapses of meiosis in hybrid males.

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.

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.

Sex chromosome mechanisms in cardiac development and disease

Many human diseases, including cardiovascular disease, show differences between men and women in pathology and treatment outcomes. In the case of cardiac disease, sex differences are exemplified by differences in the frequency of specific types of congenital and adult-onset heart disease. Clinical studies have suggested that gonadal hormones are a factor in sex bias. However, recent research has shown that gene and protein networks under non-hormonal control also account for cardiac sex differences. In this review, we describe the sex chromosome pathways that lead to sex differences in the development and function of the heart and highlight how these findings affect future care and treatment of cardiac disease.

Multi-omics analysis identifies drivers of protein phosphorylation

BACKGROUND

Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.

RESULTS

We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.

CONCLUSIONS

Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.

Genome-wide screen identifies host loci that modulate M. tuberculosis fitness in immunodivergent mice

Genetic differences among mammalian hosts and Mycobacterium tuberculosis ( Mtb ) strains determine diverse tuberculosis (TB) patient outcomes. The advent of recombinant inbred mouse panels and next-generation transposon mutagenesis and sequencing approaches has enabled dissection of complex host- pathogen interactions. To identify host and pathogen genetic determinants of Mtb pathogenesis, we infected members of the BXD family of mouse strains with a comprehensive library of Mtb transposon mutants (TnSeq). Members of the BXD family segregate for Mtb -resistant C57BL/6J (B6 or B ) and Mtb -susceptible DBA/2J (D2 or D ) haplotypes. The survival of each bacterial mutant was quantified within each BXD host, and we identified those bacterial genes that were differentially required for Mtb fitness across BXD genotypes. Mutants that varied in survival among the host family of strains were leveraged as reporters for "endophenotypes", each bacterial fitness profile directly probing specific components of the infection microenvironment. We conducted QTL mapping of these bacterial fitness endophenotypes and identified 140 h ost- p athogen quantitative trait loci ( hp QTL). We identified a QTL hotspot on chromosome 6 (75.97-88.58 Mb) associated with the genetic requirement of multiple Mtb genes; Rv0127 ( mak ), Rv0359 ( rip2 ), Rv0955 ( perM ), and Rv3849 ( espR ). Together, this screen reinforces the utility of bacterial mutant libraries as precise reporters of the host immunological microenvironment during infection and highlights specific host-pathogen genetic interactions for further investigation. To enable downstream follow-up for both bacterial and mammalian genetic research communities, all bacterial fitness profiles have been deposited into GeneNetwork.org and added into the comprehensive collection of TnSeq libraries in MtbTnDB.

Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Genetic mapping of microbial and host traits reveals production of immunomodulatory lipids by Akkermansia muciniphila in the murine gut

The molecular bases of how host genetic variation impacts the gut microbiome remain largely unknown. Here we used a genetically diverse mouse population and applied systems genetics strategies to identify interactions between host and microbe phenotypes including microbial functions, using faecal metagenomics, small intestinal transcripts and caecal lipids that influence microbe-host dynamics. Quantitative trait locus (QTL) mapping identified murine genomic regions associated with variations in bacterial taxa; bacterial functions including motility, sporulation and lipopolysaccharide production and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and caecal levels of ornithine lipids. Follow-up in vitro and in vivo studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that ornithine lipids have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits including Atf3, which encodes for a transcription factor that plays vital roles in modulating metabolism and immunity. Collectively, these results suggest that ornithine lipids are potentially important for A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

Evolution of enzyme levels in metabolic pathways: A theoretical approach. Part 2

Metabolism is essential for cell function and adaptation. Because of their central role in metabolism, kinetic parameters and enzyme concentrations are under constant selective pressure to adapt the fluxes of the metabolic networks to the needs of the organism. In line with various studies dealing with enzyme evolution, we recently developed a model of the evolution of enzyme concentrations under selection for increased flux, considered as a proxy for fitness (Coton et al., 2022). With this model, taking into account two realistic cellular constraints, competition for resources and co-regulation, we determined the evolutionary equilibria and range of neutral variations of enzyme concentrations. In this article, we expanded this model by considering that the enzymes in a pathway can belong to different co-regulation groups. We determined the equilibria and showed that the constraints modify the adaptive landscape by limiting the number of independent dimensions. We also showed that any trade-off between enzyme concentrations is sufficient to limit the flux and relax selection for increasing the concentration of other enzymes. Even though this model is based on simplifying assumptions, the complexity of the relationship between enzyme concentrations prevents the formal analysis of the range of neutral variation of enzyme concentrations. However, we could show that selection for maximizing the flux results in selective neutrality for all enzymes regardless the constraints applied, giving generality to the prediction of Hartl et al. (1985).

Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression

Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.