Stem-loop-induced ribosome queuing in the uORF2/ATF4 overlap fine-tunes stress-induced human ATF4 translational control.

Activating transcription factor 4 (ATF4) is a master transcriptional regulator of the integrated stress response, leading cells toward adaptation or death. ATF4's induction under stress was thought to be due to delayed translation reinitiation, where the reinitiation-permissive upstream open reading frame 1 (uORF1) plays a key role. Accumulating evidence challenging this mechanism as the sole source of ATF4 translation control prompted us to investigate additional regulatory routes. We identified a highly conserved stem-loop in the uORF2/ATF4 overlap, immediately preceded by a near-cognate CUG, which introduces another layer of regulation in the form of ribosome queuing. These elements explain how the inhibitory uORF2 can be translated under stress, confirming prior observations but contradicting the original regulatory model. We also identified two highly conserved, potentially modified adenines performing antagonistic roles. Finally, we demonstrated that the canonical ATF4 translation start site is substantially leaky scanned. Thus, ATF4's translational control is more complex than originally described, underpinning its key role in diverse biological processes.

Identification of molecular signatures defines the differential proteostasis response in induced spinal and cranial motor neurons.

Amyotrophic lateral sclerosis damages proteostasis, affecting spinal and upper motor neurons earlier than a subset of cranial motor neurons. To aid disease understanding, we exposed induced cranial and spinal motor neurons (iCrMNs and iSpMNs) to proteotoxic stress, under which iCrMNs showed superior survival, quantifying the transcriptome and proteome for >8,200 genes at 0, 12, and 36 h. Two-thirds of the proteome showed cell-type differences. iSpMN-enriched proteins related to DNA/RNA metabolism, and iCrMN-enriched proteins acted in the endoplasmic reticulum (ER)/ER chaperone complex, tRNA aminoacylation, mitochondria, and the plasma/synaptic membrane, suggesting that iCrMNs expressed higher levels of proteins supporting proteostasis and neuronal function. When investigating the increased proteasome levels in iCrMNs, we showed that the activity of the 26S proteasome, but not of the 20S proteasome, was higher in iCrMNs than in iSpMNs, even after a stress-induced decrease. We identified Ublcp1 as an iCrMN-specific regulator of the nuclear 26S activity.

Complex changes in serum protein levels in COVID-19 convalescents.

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

Proteomic Signatures of the Serological Response to Influenza Vaccination in a Large Human Cohort Study.

The serological response to the influenza virus vaccine is highly heterogeneous for reasons that are not entirely clear. While the impact of demographic factors such as age, body mass index (BMI), sex, prior vaccination and titer levels are known to impact seroconversion, they only explain a fraction of the response. To identify signatures of the vaccine response, we analyzed 273 protein levels from 138 serum samples of influenza vaccine recipients (2019-2020 season). We found that levels of proteins functioning in cholesterol transport were positively associated with seroconversion, likely linking to the known impact of BMI. When adjusting seroconversion for the demographic factors, we identified additional, unexpected signatures: proteins regulating actin cytoskeleton dynamics were significantly elevated in participants with high adjusted seroconversion. Viral strain specific analysis showed that this trend was largely driven by the H3N2 strain. Further, we identified complex associations between adjusted seroconversion and other factors: levels of proteins of the complement system associated positively with adjusted seroconversion in younger participants, while they were associated negatively in the older population. We observed the opposite trends for proteins of high density lipoprotein remodeling, transcription, and hemostasis. In sum, careful integrative modeling can extract new signatures of seroconversion from highly variable data that suggest links between the humoral response as well as immune cell communication and migration.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis.

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

New insights into the cellular temporal response to proteostatic stress.

Maintaining a healthy proteome involves all layers of gene expression regulation. By quantifying temporal changes of the transcriptome, translatome, proteome, and RNA-protein interactome in cervical cancer cells, we systematically characterize the molecular landscape in response to proteostatic challenges. We identify shared and specific responses to misfolded proteins and to oxidative stress, two conditions that are tightly linked. We reveal new aspects of the unfolded protein response, including many genes that escape global translation shutdown. A subset of these genes supports rerouting of energy production in the mitochondria. We also find that many genes change at multiple levels, in either the same or opposing directions, and at different time points. We highlight a variety of putative regulatory pathways, including the stress-dependent alternative splicing of aminoacyl-tRNA synthetases, and protein-RNA binding within the 3' untranslated region of molecular chaperones. These results illustrate the potential of this information-rich resource.

Integration of large-scale multi-omic datasets: a protein-centric view.

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

Time-course proteomics dataset monitoring HeLa cells subjected to DTT induced endoplasmic reticulum stress.

The data described here provide an analysis of the dynamic response of HeLa cell proteome to dithiothreitol (DTT) inducing stress of the endoplasmic reticulum (ER). During ER stress, accumulation of misfolded and unfolded proteins in the lumen of the ER initiates the Unfolded Protein Response (UPR), resulting in a large-scale redistribution of proteins. We used label-free mass spectrometry to monitor the proteomic changes of HeLa cells during a 30-h time course, monitoring eight time points (0, 0.5, 1, 2, 8, 16, 24, and 30 h). The data are associated with the research article "Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress" [1], which discusses a core dataset of 1237 proteins. Here, we present the extended dataset of 2131 proteins. The raw mass spectrometry data and the analysis results have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PRIDE: PXD002039.

Genetic markers of pigmentation are novel risk loci for uveal melanoma.

While the role of genetic risk factors in the etiology of uveal melanoma (UM) has been strongly suggested, the genetic susceptibility to UM is currently vastly unexplored. Due to shared epidemiological risk factors between cutaneous melanoma (CM) and UM, in this study we have selected 28 SNPs identified as risk variants in previous genome-wide association studies on CM or CM-related host phenotypes (such as pigmentation and eye color) and tested them for association with UM risk. By logistic regression analysis of 272 UM cases and 1782 controls using an additive model, we identified five variants significantly associated with UM risk, all passing adjustment for multiple testing. The three most significantly associated variants rs12913832 (OR = 0.529, 95% CI 0.415-0.673; p = 8.47E-08), rs1129038 (OR = 0.533, 95% CI 0.419-0.678; p = 1.19E-07) and rs916977 (OR = 0.465, 95% CI 0.339-0.637; p = 3.04E-07) are correlated (r(2) > 0.5) and map at 15q12 in the region of HERC2/OCA2, which determines eye-color in the human population. Our data provides first evidence that the genetic factors associated with pigmentation traits are risk loci of UM susceptibility.

The Expression Quantitative Trait Loci in Immune Pathways and their Effect on Cutaneous Melanoma Prognosis.

PURPOSE: The identification of personalized germline markers with biologic relevance for the prediction of cutaneous melanoma prognosis is highly demanded but to date, it has been largely unsuccessful. As melanoma progression is controlled by host immunity, here we present a novel approach interrogating immunoregulatory pathways using the genome-wide maps of expression quantitative trait loci (eQTL) to reveal biologically relevant germline variants modulating cutaneous melanoma outcomes. EXPERIMENTAL DESIGN: Using whole genome eQTL data from a healthy population, we identified 385 variants significantly impacting the expression of 268 immune-relevant genes. The 40 most significant eQTLs were tested in a prospective cohort of 1,221 patients with cutaneous melanoma for their association with overall (OS) and recurrence-free survival using Cox regression models. RESULTS: We identified highly significant associations with better melanoma OS for rs6673928, impacting IL19 expression (HR, 0.56; 95% CI, 0.41-0.77; P = 0.0002) and rs6695772, controlling the expression of BATF3 (HR, 1.64; 95% CI, 1.19-2.24; P = 0.0019). Both associations map in the previously suspected melanoma prognostic locus at 1q32. Furthermore, we show that their combined effect on melanoma OS is substantially enhanced reaching the level of clinical applicability (HR, 1.92; 95% CI, 1.43-2.60; P = 2.38e-5). CONCLUSIONS: Our unique approach of interrogating lymphocyte-specific eQTLs reveals novel and biologically relevant immunomodulatory eQTL predictors of cutaneous melanoma prognosis that are independent of current histopathologic markers. The significantly enhanced combined effect of identified eQTLs suggests the personalized utilization of both SNPs in a clinical setting, strongly indicating the promise of the proposed design for the discovery of prognostic or risk germline markers in other cancers. Clin Cancer Res; 22(13); 3268-80. ©2016 AACR.

Genotyping of Single Nucleotide Polymorphisms in DNA Isolated from Serum Using Sequenom MassARRAY Technology.

BACKGROUND: Large epidemiologic studies have the potential to make valuable contributions to the assessment of gene-environment interactions because they prospectively collected detailed exposure data. Some of these studies, however, have only serum or plasma samples as a low quantity source of DNA. METHODS: We examined whether DNA isolated from serum can be used to reliably and accurately genotype single nucleotide polymorphisms (SNPs) using Sequenom multiplex SNP genotyping technology. We genotyped 81 SNPs using samples from 158 participants in the NYU Women's Health Study. Each participant had DNA from serum and at least one paired DNA sample isolated from a high quality source of DNA, i.e. clots and/or cell precipitates, for comparison. RESULTS: We observed that 60 of the 81 SNPs (74%) had high call frequencies (≥95%) using DNA from serum, only slightly lower than the 85% of SNPs with high call frequencies in DNA from clots or cell precipitates. Of the 57 SNPs with high call frequencies for serum, clot, and cell precipitate DNA, 54 (95%) had highly concordant (>98%) genotype calls across all three sample types. High purity was not a critical factor to successful genotyping. CONCLUSIONS: Our results suggest that this multiplex SNP genotyping method can be used reliably on DNA from serum in large-scale epidemiologic studies.

Genetic associations of the interleukin locus at 1q32.1 with clinical outcomes of cutaneous melanoma.

BACKGROUND: Due to high melanoma immunogenicity, germline genetic variants in immune pathways have been studied for association with melanoma prognosis. However, limited candidate selection, inadequate power, or lack of independent validation have hampered the reproducibility of these prior findings, preventing personalised clinical applicability in melanoma prognostication. Our objective was to assess the prognostic utility of genetic variants in immunomodulatory pathways for prediction of melanoma clinical outcomes. METHODS: We genotyped 72 tag single nucleotide polymorphisms (SNPs) in 44 immunomodulatory genes in a population sample of 1022 melanoma patients and performed Cox regression analysis to test the association between SNPs and melanoma recurrence-free (RFS) and overall survival (OS). We have further investigated the most significant associations using a fine mapping strategy and followed with functional analyses in CD4+ T cells in a subset of 75 melanoma patients. RESULTS: The most significant associations were found with melanoma OS for rs3024493 in IL10 at chromosome 1q32.1 (heterozygous HR 0.58, 95% CI 0.39 to 0.86; p=0.0006), a variant previously shown to be linked with autoimmune conditions. Multiple additional SNPs at 1q32.1 were also nominally associated with OS confirming at least two independent association signals in this locus. In addition, we found rs3024493 associated with the downregulation of interleukin 10 (IL10) secretion in CD4+ T cells. CONCLUSIONS: We discovered novel associations of IL10 with melanoma survival at 1q32.1, suggesting this locus should be considered as a novel melanoma prognostic biomarker with potential for aiding melanoma patient management. Our findings also provide further support for an alternative role of IL10 in stimulation of anti-tumour immune response.

Genetic variation in DNA repair pathways and risk of non-Hodgkin's lymphoma.

Molecular and genetic evidence suggests that DNA repair pathways may contribute to lymphoma susceptibility. Several studies have examined the association of DNA repair genes with lymphoma risk, but the findings from these reports have been inconsistent. Here we provide the results of a focused analysis of genetic variation in DNA repair genes and their association with the risk of non-Hodgkin's lymphoma (NHL). With a population of 1,297 NHL cases and 1,946 controls, we have performed a two-stage case/control association analysis of 446 single nucleotide polymorphisms (SNPs) tagging the genetic variation in 81 DNA repair genes. We found the most significant association with NHL risk in the ATM locus for rs227060 (OR = 1.27, 95% CI: 1.13-1.43, p = 6.77×10(-5)), which remained significant after adjustment for multiple testing. In a subtype-specific analysis, associations were also observed for the ATM locus among both diffuse large B-cell lymphomas (DLBCL) and small lymphocytic lymphomas (SLL), however there was no association observed among follicular lymphomas (FL). In addition, our study provides suggestive evidence of an interaction between SNPs in MRE11A and NBS1 associated with NHL risk (OR = 0.51, 95% CI: 0.34-0.77, p = 0.0002). Finally, an imputation analysis using the 1,000 Genomes Project data combined with a functional prediction analysis revealed the presence of biologically relevant variants that correlate with the observed association signals. While the findings generated here warrant independent validation, the results of our large study suggest that ATM may be a novel locus associated with the risk of multiple subtypes of NHL.

Melanoma risk loci as determinants of melanoma recurrence and survival.

BACKGROUND: Steadily high melanoma mortality rates urge for the availability of novel biomarkers with a more personalized ability to predict melanoma clinical outcomes. Germline risk variants are promising candidates for this purpose; however, their prognostic potential in melanoma has never been systematically tested. METHODS: We examined the effect of 108 melanoma susceptibility single nucleotide polymorphisms (SNPs), associated in recent GWAS with melanoma and melanoma-related phenotypes, on recurrence-free survival (RFS) and overall survival (OS), in 891 prospectively accrued melanoma patients. Cox proportional hazards models (Cox PH) were used to test the associations between 108 melanoma risk SNPs and RFS and OS adjusted by age at diagnosis, gender, tumor stage, histological subtype and other primary tumor characteristics. RESULTS: We identified significant associations for rs7538876 (RCC2) with RFS (HR=1.48, 95% CI=1.20-1.83, p=0.0005) and rs9960018 (DLGAP1) with both RFS and OS (HR=1.43, 95% CI=1.07-1.91, p=0.01, HR=1.52, 95% CI=1.09-2.12, p=0.01, respectively) using multivariable Cox PH models. In addition, we developed a logistic regression model that incorporates rs7538876, rs9960018, primary tumor histological type and stage at diagnosis that has an improved discriminatory ability to classify 3-year recurrence (AUC=82%) compared to histological type and stage alone (AUC=78%). CONCLUSIONS: We identified associations between melanoma risk variants and melanoma outcomes. The significant associations observed for rs7538876 and rs9960018 suggest a biological implication of these loci in melanoma progression. The observed predictive patterns of associated variants with clinical end-points suggest for the first time the potential for utilization of genetic risk markers in melanoma prognostication.