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

Host-Intrinsic Interferon Status in Infection and Immunity

Most genetic ablations of interferon (IFN) signaling abolish both the experimentally induced IFN response and constitutive IFN, whose effects are well established in autoimmunity but understudied during infection. In host-pathogen interactions, most IFN-mediated responses are attributed to infection-driven IFN. However, IFNs confer their activity by regulating networks of interferon-stimulated genes (ISGs), a process that requires de novo transcription and translation of both IFN and downstream ISGs through feedback of IFN receptor signaling. Due to the temporal requirement for IFN activity, many rapid antimicrobial responses may instead result from pre-established IFN signature stemming from host-intrinsic processes. Addressing the permeating effects of constitutive IFN is therefore needed to accurately describe immunity as host intrinsic or pathogen induced.

Evolution of heat-shock protein expression underlying adaptive responses to environmental stress

Heat-shock proteins (Hsps) and their cognates are primary mitigators of cell stress. With increasingly severe impacts of climate change and other human modifications of the biosphere, the ability of the heat-shock system to affect evolutionary fitness in environments outside the laboratory and to evolve in response is topic of growing importance. Since the last major reviews, several advances have occurred. First, demonstrations of the heat-shock response outside the laboratory now include many additional taxa and environments. Many of these demonstrations are only correlative, however. More importantly, technical advances in "omic" quantification of nucleic acids and proteins, genomewide association analysis, and manipulation of genes and their expression have enabled the field to move beyond correlation. Several consequent advances are already evident: The pathway from heat-shock gene expression to stress tolerance in nature can be extremely complex, mediated through multiple biological processes and systems, and even multiple species. The underlying genes are more numerous, diverse and variable than previously appreciated, especially with respect to their regulatory variation and epigenetic changes. The impacts and limitations (e.g., due to trade-offs) of natural selection on these genes have become more obvious and better established. At last, as evolutionary capacitors, Hsps may have distinctive impacts on the evolution of other genes and ecological consequences.

Post-translational buffering leads to convergent protein expression levels between primates

BACKGROUND

Differences in gene regulation between human and closely related species influence phenotypes that are distinctly human. While gene regulation is a multi-step process, the majority of research concerning divergence in gene regulation among primates has focused on transcription.

RESULTS

To gain a comprehensive view of gene regulation, we surveyed genome-wide ribosome occupancy, which reflects levels of protein translation, in lymphoblastoid cell lines derived from human, chimpanzee, and rhesus macaque. We further integrated messenger RNA and protein level measurements collected from matching cell lines. We find that, in addition to transcriptional regulation, the major factor determining protein level divergence between human and closely related species is post-translational buffering. Inter-species divergence in transcription is generally propagated to the level of protein translation. In contrast, gene expression divergence is often attenuated post-translationally, potentially mediated through post-translational modifications.

CONCLUSIONS

Results from our analysis indicate that post-translational buffering is a conserved mechanism that led to relaxation of selective constraint on transcript levels in humans.

Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identified-let alone total-proteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewer missing values. These findings highlight the importance of refining proteome quantitation methods to bring the number of usefully quantified proteins into closer agreement with the number of total quantified proteins.

Genetic Drivers of Pancreatic Islet Function

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.

Islet proteomics reveals genetic variation in dopamine production resulting in altered insulin secretion

The mouse is a critical model in diabetes research, but most research in mice has been limited to a small number of mouse strains and limited genetic variation. Using the eight founder strains and both sexes of the Collaborative Cross (C57BL/6J (B6), A/J, 129S1/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HILtJ (NZO), PWK/PhJ (PWK), WSB/EiJ (WSB), and CAST/EiJ (CAST)), we investigated the genetic dependence of diabetes-related metabolic phenotypes and insulin secretion. We found that strain background is associated with an extraordinary range in body weight, plasma glucose, insulin, triglycerides, and insulin secretion. Our whole-islet proteomic analysis of the eight mouse strains demonstrates that genetic background exerts a strong influence on the islet proteome that can be linked to the differences in diabetes-related metabolic phenotypes and insulin secretion. We computed protein modules consisting of highly correlated proteins that enrich for biological pathways and provide a searchable database of the islet protein expression profiles. To validate the data resource, we identified tyrosine hydroxylase (Th), a key enzyme in catecholamine synthesis, as a protein that is highly expressed in β-cells of PWK and CAST islets. We show that CAST islets synthesize elevated levels of dopamine, which suppresses insulin secretion. Prior studies, using only the B6 strain, concluded that adult mouse islets do not synthesize l-3,4-dihydroxyphenylalanine (l-DOPA), the product of Th and precursor of dopamine. Thus, the choice of the CAST strain, guided by our islet proteomic survey, was crucial for these discoveries. In summary, we provide a valuable data resource to the research community, and show that proteomic analysis identified a strain-specific pathway by which dopamine synthesized in β-cells inhibits insulin secretion.

Untangling Genetic Risk for Alzheimer's Disease

Alzheimer's disease (AD) is a genetically heterogeneous neurodegenerative disorder caused by fully penetrant single gene mutations in a minority of cases, while the majority of cases are sporadic or show modest familial clustering. These cases are of late onset and likely result from the interaction of many genes and the environment. More than 30 loci have been implicated in AD by a combination of linkage, genome-wide association, and whole genome/exome sequencing. We have learned from these studies that perturbations in endolysosomal, lipid metabolism, and immune response pathways substantially contribute to sporadic AD pathogenesis. We review here current knowledge about functions of AD susceptibility genes, highlighting cells of the myeloid lineage as drivers of at least part of the genetic component in late-onset AD. Although targeted resequencing utilized for the identification of causal variants has discovered coding mutations in some AD-associated genes, a lot of risk variants lie in noncoding regions. Here we discuss the use of functional genomics approaches that integrate transcriptomic, epigenetic, and endophenotype traits with systems biology to annotate genetic variants, and to facilitate discovery of AD risk genes. Further validation in cell culture and mouse models will be necessary to establish causality for these genes. This knowledge will allow mechanism-based design of novel therapeutic interventions in AD and promises coherent implementation of treatment in a personalized manner.

Rare Disease Mechanisms Identified by Genealogical Proteomics of Copper Homeostasis Mutant Pedigrees

Rare neurological diseases shed light onto universal neurobiological processes. However, molecular mechanisms connecting genetic defects to their disease phenotypes are elusive. Here, we obtain mechanistic information by comparing proteomes of cells from individuals with rare disorders with proteomes from their disease-free consanguineous relatives. We use triple-SILAC mass spectrometry to quantify proteomes from human pedigrees affected by mutations in ATP7A, which cause Menkes disease, a rare neurodegenerative and neurodevelopmental disorder stemming from systemic copper depletion. We identified 214 proteins whose expression was altered in ATP7A^-/y fibroblasts. Bioinformatic analysis of ATP7A-mutant proteomes identified known phenotypes and processes affected in rare genetic diseases causing copper dyshomeostasis, including altered mitochondrial function. We found connections between copper dyshomeostasis and the UCHL1/PARK5 pathway of Parkinson disease, which we validated with mitochondrial respiration and Drosophila genetics assays. We propose that our genealogical "omics" strategy can be broadly applied to identify mechanisms linking a genomic locus to its phenotypes.

Molecular and functional signatures in a novel Alzheimer's disease mouse model assessed by quantitative proteomics

Background

Alzheimer’s disease (AD), the most common neurodegenerative disorder, is characterized by the deposition of extracellular amyloid plaques and intracellular neurofibrillary tangles. To understand the pathological mechanisms underlying AD, developing animal models that completely encompass the main features of AD pathologies is indispensable. Although mouse models that display pathological hallmarks of AD (amyloid plaques, neurofibrillary tangles, or both) have been developed and investigated, a systematic approach for understanding the molecular characteristics of AD mouse models is lacking.

Methods

To elucidate the mechanisms underlying the contribution of amyloid beta (Aβ) and tau in AD pathogenesis, we herein generated a novel animal model of AD, namely the AD-like pathology with amyloid and neurofibrillary tangles (ADLP^APT) mice. The ADLP^APT mice carry three human transgenes, including amyloid precursor protein, presenilin-1, and tau, with six mutations. To characterize the molecular and functional signatures of AD in ADLP^APT mice, we analyzed the hippocampal proteome and performed comparisons with individual-pathology transgenic mice (i.e., amyloid or neurofibrillary tangles) and wild-type mice using quantitative proteomics with 10-plex tandem mass tag.

Results

The ADLP^APT mice exhibited accelerated neurofibrillary tangle formation in addition to amyloid plaques, neuronal loss in the CA1 area, and memory deficit at an early age. In addition, our proteomic analysis identified nearly 10,000 protein groups, which enabled the identification of hundreds of differentially expressed proteins (DEPs) in ADLP^APT mice. Bioinformatics analysis of DEPs revealed that ADLP^APT mice experienced age-dependent active immune responses and synaptic dysfunctions.

Conclusions

Our study is the first to compare and describe the proteomic characteristics in amyloid and neurofibrillary tangle pathologies using isobaric label-based quantitative proteomics. Furthermore, we analyzed the hippocampal proteome of the newly developed ADLP^APT model mice to investigate how both Aβ and tau pathologies regulate the hippocampal proteome. Because the ADLP^APT mouse model recapitulates the main features of AD pathogenesis, the proteomic data derived from its hippocampus has significant utility as a novel resource for the research on the Aβ-tau axis and pathophysiological changes in vivo.

Electronic supplementary material

The online version of this article (10.1186/s13024-017-0234-4) contains supplementary material, which is available to authorized users.

Selenium regulation of selenoprotein enzyme activity and transcripts in a pilot study with Founder strains from the Collaborative Cross

Rodents and humans have 24–25 selenoproteins, and these proteins contain the 21^st amino acid, selenocysteine, incorporated co-translationally into the peptide backbone in a series of reactions dependent on at least 6 unique gene products. In selenium (Se) deficiency, there is differential regulation of selenoprotein expression, whereby levels of some selenoproteins and their transcripts decrease dramatically in Se deficiency, but other selenoprotein transcripts are spared this decrease; the underlying mechanism, however, is not fully understood. To begin explore the genetic basis for this variation in regulation by Se status in a pilot study, we fed Se-deficient or Se-adequate diets (0.005 or 0.2 μg Se/g, respectively) for eight weeks to the eight Founder strains of the Collaborative Cross. We found rather uniform expression of selenoenzyme activity for glutathione peroxidase (Gpx) 3 in plasma, Gpx1 in red blood cells, and Gpx1, Gpx4, and thioredoxin reductase in liver. In Founder mice, Se deficiency decreased each of these activities to a similar extent. Regulation of selenoprotein transcript expression by Se status was also globally retained intact, with dramatic down-regulation of Gpx1, Selenow, and Selenoh transcripts in all 8 strains of Founder mice. These results indicate that differential regulation of selenoprotein expression by Se status is an essential aspect of Se metabolism and selenoprotein function. A few lone differences in Se regulation were observed for individual selenoproteins in this pilot study, but these differences did not single-out one strain or one selenoprotein that consistently had unique Se regulation of selenoprotein expression. These differences should be affirmed in larger studies; use of the Diversity Outbred and Collaborative Cross strains may help to better define the functions of these selenoproteins.

Integrated omics dissection of proteome dynamics during cardiac remodeling

Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.

Single subject transcriptome analysis to identify functionally signed gene set or pathway activity

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject's response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes × 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at http://www.lussiergroup.org/publications/PathwayActivity.

Protein quantitative trait locus study in obesity during weight-loss identifies a leptin regulator

Thousands of genetic variants have been associated with complex traits through genome-wide association studies. However, the functional variants or mechanistic consequences remain elusive. Intermediate traits such as gene expression or protein levels are good proxies of the metabolic state of an organism. Proteome analysis especially can provide new insights into the molecular mechanisms of complex traits like obesity. The role of genetic variation in determining protein level variation has not been assessed in obesity. To address this, we design a large-scale protein quantitative trait locus (pQTL) analysis based on a set of 1129 proteins from 494 obese subjects before and after a weight loss intervention. This reveals 55 BMI-associated cis-pQTLs and trans-pQTLs at baseline and 3 trans-pQTLs after the intervention. We provide evidence for distinct genetic mechanisms regulating BMI-associated proteins before and after weight loss. Finally, by functional analysis, we identify and validate FAM46A as a trans regulator for leptin.

Although many genetic variants are known for obesity, their function remains largely unknown. Here, in a weight-loss intervention cohort, the authors identify protein quantitative trait loci associated with BMI at baseline and after weight loss and find FAM46A to be a regulator of leptin in adipocytes.

Immunogenomic approaches to understand the function of immune disease variants

Mapping hundreds of genetic variants through genome wide association studies provided an opportunity to gain insights into the pathobiology of immune-mediated diseases. However, as most of the disease variants fall outside the gene coding sequences the functional interpretation of the exact role of the associated variants remains to be determined. The integration of disease-associated variants with large scale genomic maps of cell-type-specific gene regulation at both chromatin and transcript levels deliver examples of functionally prioritized causal variants and genes. In particular, the enrichment of disease variants with histone marks can point towards the cell types most relevant to disease development. Furthermore, chromatin contact maps that link enhancers to promoter regions in a direct way allow the identification of genes that can be regulated by the disease variants. Candidate genes implicated with such approaches can be further examined through the correlation of gene expression with genotypes. Additionally, in the context of immune-mediated diseases it is important to combine genomics with immunology approaches. Genotype correlations with the immune system as a whole, as well as with cellular responses to different stimuli, provide a valuable platform for understanding the functional impact of disease-associated variants. The intersection of immunogenomic resources with disease-associated variants paints a detailed picture of disease causal mechanisms. Here, we provide an overview of recent studies that combine these approaches to identify disease vulnerable pathways.

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.

A Compendium of Co-regulated Protein Complexes in Breast Cancer Reveals Collateral Loss Events

Protein complexes are responsible for the bulk of activities within the cell, but how their behavior and abundance varies across tumors remains poorly understood. By combining proteomic profiles of breast tumors with a large-scale protein-protein interaction network, we have identified a set of 285 high-confidence protein complexes whose subunits have highly correlated protein abundance across tumor samples. We used this set to identify complexes that are reproducibly under- or overexpressed in specific breast cancer subtypes. We found that mutation or deletion of one subunit of a co-regulated complex was often associated with a collateral reduction in protein expression of additional complex members. This collateral loss phenomenon was typically evident from proteomic, but not transcriptomic, profiles, suggesting post-transcriptional control. Mutation of the tumor suppressor E-cadherin (CDH1) was associated with a collateral loss of members of the adherens junction complex, an effect we validated using an engineered model of E-cadherin loss.

Vitrification of Rhesus Macaque Mesenchymal Stem Cells and the Effects on Global Gene Expression

Mesenchymal stem cells (MSCs) are one of the most promising adult stem cells for clinical application in a cell therapy. The development of large-scale cryopreservation techniques, such as vitrification, for MSCs is a prerequisite for clinical therapies. Dimethyl sulfoxide (DMSO) and ethylene glycol (EG) are two types of cryoprotectants widely used for cell vitrification. However, the effects of DMSO and EG on the biological characteristics and transcriptome profiles of MSCs after cryopreservation remain unknown. In the present study, the viability, immunophenotype of cell surface markers, proliferation, differentiation potency, and global gene expression of rhesus macaque bone marrow-derived MSCs vitrified using DMSO and EG were studied. The results showed that vitrification did not affect the morphology, surface markers, and differentiation of the MSCs, and compared to DMSO, EG better protected cell viability and proliferation. Most importantly, vitrification resulted in changes in a large number of transcripts of MSCs either preserved using DMSO or EG. This report is the first to examine the effects of DMSO and EG on global gene expression in stem cells. These results will be beneficial to understanding the biological process involved in MSC vitrification and will contribute to improving cryopreservation protocols that maintain transcriptomic identity with high cryosurvival for preclinical research and clinical long-term storage.

Widespread Post-transcriptional Attenuation of Genomic Copy-Number Variation in Cancer

Copy-number variations (CNVs) are ubiquitous in cancer and often act as driver events, but the effects of CNVs on the proteome of tumors are poorly understood. Here, we analyze recently published genomics, transcriptomics, and proteomics datasets made available by CPTAC and TCGA consortia on 282 breast, ovarian, and colorectal tumor samples to investigate the impact of CNVs in the proteomes of these cells. We found that CNVs are buffered by post-transcriptional regulation in 23%-33% of proteins that are significantly enriched in protein complex members. Our analyses show that complex subunits are highly co-regulated, and some act as rate-limiting steps of complex assembly, as their depletion induces decreased abundance of other complex members. We identified 48 such rate-limiting interactions and experimentally confirmed our predictions on the interactions of AP3B1 with AP3M1 and GTF2E2 with GTF2E1. This study highlights the importance of post-transcriptional mechanisms in cancer that allow cells to cope with their altered genomes.

Recent advances in proteomic profiling of pancreatic ductal adenocarcinoma and the road ahead

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide. However, there remain many unmet clinical needs, from diagnosis to treatment strategies. The inherent complexity of the molecular characteristics of PDAC has made it difficult to meet these challenges, rendering proteomic profiling of PDAC a critical area of research. Area covered: In this review, we present recent advances in mass spectrometry (MS) and its current application in proteomic studies on PDAC. In addition, we discuss future directions for research that can efficiently incorporate current MS-based technologies that address key issues of PDAC proteomics. Expert commentary: Compared with other cancer studies, little progress has been made in PDAC proteomics, perhaps attributed to the difficulty in performing in-depth and large-scale clinical studies on PDAC. However, recent advances in mass spectrometry can advance PDAC proteomics past the fundamental research stage.

Age-related neurodegenerative disease associated pathways identified in retinal and vitreous proteome from human glaucoma eyes

Glaucoma is a chronic disease that shares many similarities with other neurodegenerative disorders of the central nervous system. This study was designed to evaluate the association between glaucoma and other neurodegenerative disorders by investigating glaucoma-associated protein changes in the retina and vitreous humour. The multiplexed Tandem Mass Tag based proteomics (TMT-MS3) was carried out on retinal tissue and vitreous humour fluid collected from glaucoma patients and age-matched controls followed by functional pathway and protein network interaction analysis. About 5000 proteins were quantified from retinal tissue and vitreous fluid of glaucoma and control eyes. Of the differentially regulated proteins, 122 were found linked with pathophysiology of Alzheimer’s disease (AD). Pathway analyses of differentially regulated proteins indicate defects in mitochondrial oxidative phosphorylation machinery. The classical complement pathway associated proteins were activated in the glaucoma samples suggesting an innate inflammatory response. The majority of common differentially regulated proteins in both tissues were members of functional protein networks associated brain changes in AD and other chronic degenerative conditions. Identification of previously reported and novel pathways in glaucoma that overlap with other CNS neurodegenerative disorders promises to provide renewed understanding of the aetiology and pathogenesis of age related neurodegenerative diseases.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

The Clinic Is My Laboratory: Life as a Clinical Geneticist

Clinical genetics is the application of advances in genetics and medicine to real human families. It involves diagnosis, care, and counseling concerning options available to affected individuals and their family members. Advances in medicine and genetics have led to dramatic changes in the scope and responsibilities of clinical genetics. This reflection on the last 50+ years of clinical genetics comes from personal experience, with an emphasis on the important contributions that clinical geneticists have made to the understanding of disease/disorder processes and mechanisms. The genetics clinic is a research laboratory where major advances in knowledge can and have been made.

Identification of trans Protein QTL for Secreted Airway Mucins in Mice and a Causal Role for Bpifb1

Mucus hyper-secretion is a hallmark feature of asthma and other muco-obstructive airway diseases. The mucin proteins MUC5AC and MUC5B are the major glycoprotein components of mucus and have critical roles in airway defense. Despite the biomedical importance of these two proteins, the loci that regulate them in the context of natural genetic variation have not been studied. To identify genes that underlie variation in airway mucin levels, we performed genetic analyses in founder strains and incipient lines of the Collaborative Cross (CC) in a house dust mite mouse model of asthma. CC founder strains exhibited significant differences in MUC5AC and MUC5B, providing evidence of heritability. Analysis of gene and protein expression of Muc5ac and Muc5b in incipient CC lines (n = 154) suggested that post-transcriptional events were important regulators of mucin protein content in the airways. Quantitative trait locus (QTL) mapping identified distinct, trans protein QTL for MUC5AC (chromosome 13) and MUC5B (chromosome 2). These two QTL explained 18 and 20% of phenotypic variance, respectively. Examination of the MUC5B QTL allele effects and subsequent phylogenetic analysis allowed us to narrow the MUC5B QTL and identify Bpifb1 as a candidate gene. Bpifb1 mRNA and protein expression were upregulated in parallel to MUC5B after allergen challenge, and Bpifb1 knockout mice exhibited higher MUC5B expression. Thus, BPIFB1 is a novel regulator of MUC5B.

Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain

Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.

Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

Genomes of the Mouse Collaborative Cross

The Collaborative Cross (CC) is a multiparent panel of recombinant inbred (RI) mouse strains derived from eight founder laboratory strains. RI panels are popular because of their long-term genetic stability, which enhances reproducibility and integration of data collected across time and conditions. Characterization of their genomes can be a community effort, reducing the burden on individual users. Here we present the genomes of the CC strains using two complementary approaches as a resource to improve power and interpretation of genetic experiments. Our study also provides a cautionary tale regarding the limitations imposed by such basic biological processes as mutation and selection. A distinct advantage of inbred panels is that genotyping only needs to be performed on the panel, not on each individual mouse. The initial CC genome data were haplotype reconstructions based on dense genotyping of the most recent common ancestors (MRCAs) of each strain followed by imputation from the genome sequence of the corresponding founder inbred strain. The MRCA resource captured segregating regions in strains that were not fully inbred, but it had limited resolution in the transition regions between founder haplotypes, and there was uncertainty about founder assignment in regions of limited diversity. Here we report the whole genome sequence of 69 CC strains generated by paired-end short reads at 30× coverage of a single male per strain. Sequencing leads to a substantial improvement in the fine structure and completeness of the genomes of the CC. Both MRCAs and sequenced samples show a significant reduction in the genome-wide haplotype frequencies from two wild-derived strains, CAST/EiJ and PWK/PhJ. In addition, analysis of the evolution of the patterns of heterozygosity indicates that selection against three wild-derived founder strains played a significant role in shaping the genomes of the CC. The sequencing resource provides the first description of tens of thousands of new genetic variants introduced by mutation and drift in the CC genomes. We estimate that new SNP mutations are accumulating in each CC strain at a rate of 2.4 ± 0.4 per gigabase per generation. The fixation of new mutations by genetic drift has introduced thousands of new variants into the CC strains. The majority of these mutations are novel compared to currently sequenced laboratory stocks and wild mice, and some are predicted to alter gene function. Approximately one-third of the CC inbred strains have acquired large deletions (>10 kb) many of which overlap known coding genes and functional elements. The sequence of these mice is a critical resource to CC users, increases threefold the number of mouse inbred strain genomes available publicly, and provides insight into the effect of mutation and drift on common resources.

Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals

Mitochondrial stress activates a mitonuclear response to safeguard and repair mitochondrial function and to adapt cellular metabolism to stress. Using a multiomics approach in mammalian cells treated with four types of mitochondrial stressors, we identify activating transcription factor 4 (ATF4) as the main regulator of the stress response. Surprisingly, canonical mitochondrial unfolded protein response genes mediated by ATF5 are not activated. Instead, ATF4 activates the expression of cytoprotective genes, which reprogram cellular metabolism through activation of the integrated stress response (ISR). Mitochondrial stress promotes a local proteostatic response by reducing mitochondrial ribosomal proteins, inhibiting mitochondrial translation, and coupling the activation of the ISR with the attenuation of mitochondrial function. Through a trans-expression quantitative trait locus analysis, we provide genetic evidence supporting a role for Fh1 in the control of Atf4 expression in mammals. Using gene expression data from mice and humans with mitochondrial diseases, we show that the ATF4 pathway is activated in vivo upon mitochondrial stress. Our data illustrate the value of a multiomics approach to characterize complex cellular networks and provide a versatile resource to identify new regulators of mitochondrial-related diseases.

Comparative transcriptomics in human and mouse

Cross-species comparisons of genomes, transcriptomes and gene regulation are now feasible at unprecedented resolution and throughput, enabling the comparison of human and mouse biology at the molecular level. Insights have been gained into the degree of conservation between human and mouse at the level of not only gene expression but also epigenetics and inter-individual variation. However, a number of limitations exist, including incomplete transcriptome characterization and difficulties in identifying orthologous phenotypes and cell types, which are beginning to be addressed by emerging technologies. Ultimately, these comparisons will help to identify the conditions under which the mouse is a suitable model of human physiology and disease, and optimize the use of animal models.

Paeoniflorin suppressed IL-22 via p38 MAPK pathway and exerts anti-psoriatic effect

AIMS

The total glucosides of paeony (TGP) are used to treat psoriasis in the clinic. However, its active components and mechanisms are not clear. Paeoniflorin is the main constituent of TGP. Thus, the anti-psoriasis effect of paeoniflorin was studied, and its mechanism was explored.

MATERIALS AND METHODS

The effect of paeoniflorin was evaluated using a psoriasis-like model of guinea pigs. The levels of IL-6, IL-17A, IL-22, p38 MAPK, and ERK1/2 in HaCaT cells stimulated by lipopolysaccharide (LPS) were determined using RT-qPCR, enzyme linked immunosorbent assays (ELISAs) and western blot.

KEY FINDING

Compared with the control group, the model group showed edema, redness, and lesions in the ear upon stimulation with propranolol hydrochloride, and the Baker Score increased by 7-fold. Paeoniflorin ameliorated the lesion and decreased the Baker Score by 37% (p<0.05). In vitro, paeoniflorin significantly inhibited the mRNA expression of IL-6, IL-17A and IL-22 at both 2.08 and 10.41μM (p<0.01), and paeoniflorin had a marginal effect on the protein expression of IL-17A and IL-6. However, it inhibited the protein expression of IL-22 significantly, with inhibition ratios of 48.5% and 47.8% at 2.08 and 10.41μM, respectively (p<0.05). This effect was achieved by inhibiting the phosphorylation of p38 MAPK.

SIGNIFICANCE

The results of this work demonstrated that paeoniflorin is the active components of TGP and support its use as a therapeutic compound for psoriasis therapy.

Toward Personalized Gene Therapy: Characterizing the Host Genetic Control of Lentiviral-Vector-Mediated Hepatic Gene Delivery

The success of lentiviral vectors in curing fatal genetic and acquired diseases has opened a new era in human gene therapy. However, variability in the efficacy and safety of this therapeutic approach has been reported in human patients. Consequently, lentiviral-vector-based gene therapy is limited to incurable human diseases, with little understanding of the underlying causes of adverse effects and poor efficacy. To assess the role that host genetic variation has on efficacy of gene therapy, we characterized lentiviral-vector gene therapy within a set of 12 collaborative cross mouse strains. Lentiviral vectors carrying the firefly luciferase cDNA under the control of a liver-specific promoter were administered to female mice, with total-body and hepatic luciferase expression periodically monitored through 41 weeks post-vector administration. Vector copy number per diploid genome in mouse liver and spleen was determined at the end of this study. We identified major strain-specific contributions to overall success of transduction, vector biodistribution, maximum luciferase expression, and the kinetics of luciferase expression throughout the study. Our results highlight the importance of genetic variation on gene-therapeutic efficacy; provide new models with which to more rigorously assess gene therapy approaches; and suggest that redesigning preclinical studies of gene-therapy methodologies might be appropriate.

An Expanded View of Complex Traits: From Polygenic to Omnigenic

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

An Expanded View of Complex Traits: From Polygenic to Omnigenic

A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome-including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an "omnigenic" model.

Genetic background influences susceptibility to chemotherapy-induced hematotoxicity

Hematotoxicity is a life-threatening side effect of many chemotherapy regimens. While clinical factors influence patient responses, genetic factors may also play an important role. We sought to identify genomic loci that influence chemotherapy-induced hematotoxicity by dosing Diversity Outbred mice with one of three chemotherapy drugs; doxorubicin, cyclophosphamide or docetaxel. We observed that each drug had a distinct effect on both the changes in blood cell sub-populations and the underlying genetic architecture of hematotoxicity. For doxorubicin, we mapped the change in cell counts before and after dosing and found that alleles of ATP-binding cassette B1B (Abcb1b) on chromosome 5 influence all cell populations. For cyclophosphamide and docetaxel, we found that each cell population was influenced by distinct loci, none of which overlapped between drugs. These results suggest that susceptibility to chemotherapy-induced hematotoxicity is influenced by different genes for different chemotherapy drugs.

Translational Rodent Models for Research on Parasitic Protozoa-A Review of Confounders and Possibilities

Rodents, in particular Mus musculus, have a long and invaluable history as models for human diseases in biomedical research, although their translational value has been challenged in a number of cases. We provide some examples in which rodents have been suboptimal as models for human biology and discuss confounders which influence experiments and may explain some of the misleading results. Infections of rodents with protozoan parasites are no exception in requiring close consideration upon model choice. We focus on the significant differences between inbred, outbred and wild animals, and the importance of factors such as microbiota, which are gaining attention as crucial variables in infection experiments. Frequently, mouse or rat models are chosen for convenience, e.g., availability in the institution rather than on an unbiased evaluation of whether they provide the answer to a given question. Apart from a general discussion on translational success or failure, we provide examples where infections with single-celled parasites in a chosen lab rodent gave contradictory or misleading results, and when possible discuss the reason for this. We present emerging alternatives to traditional rodent models, such as humanized mice and organoid primary cell cultures. So-called recombinant inbred strains such as the Collaborative Cross collection are also a potential solution for certain challenges. In addition, we emphasize the advantages of using wild rodents for certain immunological, ecological, and/or behavioral questions. The experimental challenges (e.g., availability of species-specific reagents) that come with the use of such non-model systems are also discussed. Our intention is to foster critical judgment of both traditional and newly available translational rodent models for research on parasitic protozoa that can complement the existing mouse and rat models.

Offline pentafluorophenyl (PFP)-RP prefractionation as an alternative to high-pH RP for comprehensive LC-MS/MS proteomics and phosphoproteomics

Technological advances in liquid chromatography and tandem mass spectrometry (LC-MS/MS) have enabled comprehensive analyses of proteins and their post-translational modifications from cell culture and tissue samples. However, sample complexity necessitates offline prefractionation via a chromatographic method that is orthogonal to online reversed-phase high-performance liquid chromatography (RP-HPLC). This additional fractionation step improves target identification rates by reducing the complexity of the sample as it is introduced to the instrument. A commonly employed offline prefractionation method is high pH reversed-phase (Hi-pH RP) chromatography. Though highly orthogonal to online RP-HPLC, Hi-pH RP relies on buffers that interfere with electrospray ionization. Thus, samples that are prefractionated using Hi-pH RP are typically desalted prior to LC-MS/MS. In the present work, we evaluate an alternative offline prefractionation method, pentafluorophenyl (PFP)-based reversed-phase chromatography. Importantly, PFP prefractionation results in samples that are dried prior to analysis by LC-MS/MS. This reduction in sample handling relative to Hi-pH RP results in time savings and could facilitate higher target identification rates. Here, we have compared the performances of PFP and Hi-pH RP in offline prefractionation of peptides and phosphopeptides that have been isolated from human cervical carcinoma (HeLa) cells. Given the prevalence of isobaric mass tags for peptide quantification, we evaluated PFP chromatography of peptides labeled with tandem mass tags. Our results suggest that PFP is a viable alternative to Hi-pH RP for both peptide and phosphopeptide offline prefractionation.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Multi-omics approaches to disease

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of “integrative genetics”. Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

How mice are indispensable for understanding obesity and diabetes genetics

PURPOSE OF REVIEW

The task of cataloging human genetic variation and its relation to disease is rapidly approaching completion. The new challenge is to discover the function of disease-associated genes and to understand the pathways that lead to human disease. We propose that achieving this new level of understanding will increasingly rely on the use of model organisms. We discuss the advantages of the mouse as a model organism to our understanding of human disease.

RECENT FINDINGS

The collection of available mouse strains represents as much genetic and phenotypic variation as is found in the human population. However, unlike humans, mice can be subjected to experimental breeding protocols and the availability of tissues allows for a far greater and deeper level of phenotyping. New methods for gene editing make it relatively easy to create mouse models of known human mutations. The distinction between genetic and epigenetic inheritance can be studied in great detail. Various experimental protocols enable the exploration of the role of the microbiome in physiology and disease.

SUMMARY

We propose that there will be an interdependence between human and model organism research. Technological advances and new genetic screening platforms in the mouse have greatly improved the path to gene discovery and mechanistic studies of gene function.

Extensive Peptide Fractionation and y1 Ion-Based Interference Detection Method for Enabling Accurate Quantification by Isobaric Labeling and Mass Spectrometry

Isobaric labeling quantification by mass spectrometry (MS) has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from coisolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection, and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT)-labeled Escherichia coli peptides at 1:3:10 ratios and added in ∼20-fold more rat peptides as background, followed by the analysis of two-dimensional liquid chromatography (LC)-MS/MS. Systematic investigation shows that quantitative interference was impacted by LC fractionation depth, MS isolation window, and peptide loading amount. Exhaustive fractionation (320 × 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y₁ product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicate that intermediate fractionation (40 × 2 h) and y₁ ion-based correction allow accurate and deep TMT profiling of more than 10 000 proteins, which represents a straightforward and affordable strategy in isobaric labeling proteomics.

G1 cyclins link proliferation, pluripotency and differentiation of embryonic stem cells

Progression of mammalian cells through the G1 and S phases of the cell cycle is driven by the D-type and E-type cyclins. According to the current models, at least one of these cyclin families must be present to allow cell proliferation. Here, we show that several cell types can proliferate in the absence of all G1 cyclins. However, following ablation of G1 cyclins, embryonic stem (ES) cells attenuated their pluripotent characteristics, with the majority of cells acquiring the trophectodermal cell fate. We established that G1 cyclins, together with their associated cyclin-dependent kinases (CDKs), phosphorylate and stabilize the core pluripotency factors Nanog, Sox2 and Oct4. Treatment of murine ES cells, patient-derived glioblastoma tumour-initiating cells, or triple-negative breast cancer cells with a CDK inhibitor strongly decreased Sox2 and Oct4 levels. Our findings suggest that CDK inhibition might represent an attractive therapeutic strategy by targeting glioblastoma tumour-initiating cells, which depend on Sox2 to maintain their tumorigenic potential.

A Golden Age for Working with Public Proteomics Data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets.

The field of proteomics has matured and diversified substantially over the past 10 years.

Proteomics data are increasingly shared through centralized, public repositories.

Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher.

Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data.

While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.

A Strategy to Combine Sample Multiplexing with Targeted Proteomics Assays for High-Throughput Protein Signature Characterization

Targeted mass spectrometry assays for protein quantitation monitor peptide surrogates, which are easily multiplexed to target many peptides in a single assay. However, these assays have generally not taken advantage of sample multiplexing, which allows up to ten analyses to occur in parallel. We present a two-dimensional multiplexing workflow that utilizes synthetic peptides for each protein to prompt the simultaneous quantification of >100 peptides from up to ten mixed sample conditions. We demonstrate that targeted analysis of unfractionated lysates (2 hr) accurately reproduces the quantification of fractionated lysates (72 hr analysis) while obviating the need for peptide detection prior to quantification. We targeted 131 peptides corresponding to 69 proteins across all 60 National Cancer Institute cell lines in biological triplicate, analyzing 180 samples in only 48 hr (the equivalent of 16 min/sample). These data further elucidated a correlation between the expression of key proteins and their cellular response to drug treatment.