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.

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses.

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

Re-mining serum proteomics data reveals extensive post-translational modifications upon Zika and dengue infection.

Zika virus (ZIKV) and dengue virus (DENV) are two closely related flaviviruses with similar symptoms. However, due to the implications of ZIKV infections for pregnancy outcomes, understanding differences in their molecular impact on the host is of high interest. Viral infections change the host proteome, including post-translational modifications. As modifications are diverse and of low abundance, they typically require additional sample processing which is not feasible for large cohort studies. Therefore, we tested the potential of next-generation proteomics data in its ability to prioritize specific modifications for later analysis. We re-mined published mass spectra from 122 serum samples from ZIKV and DENV patients for the presence of phosphorylated, methylated, oxidized, glycosylated/glycated, sulfated, and carboxylated peptides. We identified 246 modified peptides with significantly differential abundance in ZIKV and DENV patients. Amongst these, methionine-oxidized peptides from apolipoproteins and glycosylated peptides from immunoglobulin proteins were more abundant in ZIKV patient serum and generate hypotheses on the potential roles of the modification in the infection. The results demonstrate how data-independent acquisition techniques can help prioritize future analyses of peptide modifications.

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.

Evaluation of determinants of the serological response to the quadrivalent split-inactivated influenza vaccine.

The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) titer levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, vaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for > 75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the major determinants of seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants and an effect of vaccination month. BMI had a surprisingly small effect, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.

Overdosage of Balanced Protein Complexes Reduces Proliferation Rate in Aneuploid Cells.

Cells with complex aneuploidies display a wide range of phenotypic abnormalities. However, the molecular basis for this has been mainly studied in trisomic (2n + 1) and disomic (n + 1) cells. To determine how karyotype affects proliferation in cells with complex aneuploidies, we generated 92 2n + x yeast strains in which each diploid cell has between 3 and 12 extra chromosomes. Genome-wide and, for individual protein complexes, proliferation defects are caused by the presence of protein complexes in which all subunits are balanced at the 3-copy level. Proteomics revealed that over 50% of 3-copy members of imbalanced complexes were expressed at only 2n protein levels, whereas members of complexes in which all subunits are stoichiometrically balanced at 3 copies per cell had 3n protein levels. We validated this finding using orthogonal datasets from yeast and from human cancers. Taken together, our study provides an explanation of how aneuploidy affects phenotype.

The lysine catabolite saccharopine impairs development by disrupting mitochondrial homeostasis.

Amino acid catabolism is frequently executed in mitochondria; however, it is largely unknown how aberrant amino acid metabolism affects mitochondria. Here we report the requirement for mitochondrial saccharopine degradation in mitochondrial homeostasis and animal development. In Caenorhbditis elegans, mutations in the saccharopine dehydrogenase (SDH) domain of the bi-functional enzyme α-aminoadipic semialdehyde synthase AASS-1 greatly elevate the lysine catabolic intermediate saccharopine, which causes mitochondrial damage by disrupting mitochondrial dynamics, leading to reduced adult animal growth. In mice, failure of mitochondrial saccharopine oxidation causes lethal mitochondrial damage in the liver, leading to postnatal developmental retardation and death. Importantly, genetic inactivation of genes that raise the mitochondrial saccharopine precursors lysine and α-ketoglutarate strongly suppresses SDH mutation-induced saccharopine accumulation and mitochondrial abnormalities in C. elegans Thus, adequate saccharopine catabolism is essential for mitochondrial homeostasis. Our study provides mechanistic and therapeutic insights for understanding and treating hyperlysinemia II (saccharopinuria), an aminoacidopathy with severe developmental defects.

Reduced intrinsic DNA curvature leads to increased mutation rate.

BACKGROUND: Mutation rates vary across the genome. Many trans factors that influence mutation rates have been identified, as have specific sequence motifs at the 1-7-bp scale, but cis elements remain poorly characterized. The lack of understanding regarding why different sequences have different mutation rates hampers our ability to identify positive selection in evolution and to identify driver mutations in tumorigenesis. RESULTS: Here, we use a combination of synthetic genes and sequences of thousands of isolated yeast colonies to show that intrinsic DNA curvature is a major cis determinant of mutation rate. Mutation rate negatively correlates with DNA curvature within genes, and a 10% decrease in curvature results in a 70% increase in mutation rate. Consistently, both yeast and humans accumulate mutations in regions with small curvature. We further show that this effect is due to differences in the intrinsic mutation rate, likely due to differences in mutagen sensitivity and not due to differences in the local activity of DNA repair. CONCLUSIONS: Our study establishes a framework for understanding the cis properties of DNA sequence in modulating the local mutation rate and identifies a novel causal source of non-uniform mutation rates across the genome.

HeteroMeth: A Database of Cell-to-cell Heterogeneity in DNA Methylation.

DNA methylation is an important epigenetic mark that plays a vital role in gene expression and cell differentiation. The average DNA methylation level among a group of cells has been extensively documented. However, the cell-to-cell heterogeneity in DNA methylation, which reflects the differentiation of epigenetic status among cells, remains less investigated. Here we established a gold standard of the cell-to-cell heterogeneity in DNA methylation based on single-cell bisulfite sequencing (BS-seq) data. With that, we optimized a computational pipeline for estimating the heterogeneity in DNA methylation from bulk BS-seq data. We further built HeteroMeth, a database for searching, browsing, visualizing, and downloading the data for heterogeneity in DNA methylation for a total of 141 samples in humans, mice, Arabidopsis, and rice. Three genes are used as examples to illustrate the power of HeteroMeth in the identification of unique features in DNA methylation. The optimization of the computational strategy and the construction of the database in this study complement the recent experimental attempts on single-cell DNA methylomes and will facilitate the understanding of epigenetic mechanisms underlying cell differentiation and embryonic development. HeteroMeth is publicly available at http://qianlab.genetics.ac.cn/HeteroMeth.

Genetic Interaction Network as an Important Determinant of Gene Order in Genome Evolution.

Although it is generally accepted that eukaryotic gene order is not random, the basic principles of gene arrangement on a chromosome remain poorly understood. Here, we extended existing population genetics theories that were based on two-locus models and proposed a hypothesis that genetic interaction networks drive the evolution of eukaryotic gene order. We predicted that genes with positive epistasis would move toward each other in evolution, during which a negative correlation between epistasis and gene distance formed. We tested and confirmed our prediction with computational simulations and empirical data analyses. Importantly, we demonstrated that gene order in the budding yeast could be successfully predicted from the genetic interaction network. Taken together, our study reveals the role of the genetic interaction network in the evolution of gene order, extends our understanding of the encoding principles in genomes, and potentially offers new strategies to improve synthetic biology.

Codon-Resolution Analysis Reveals a Direct and Context-Dependent Impact of Individual Synonymous Mutations on mRNA Level.

Codon usage bias (CUB) refers to the observation that synonymous codons are not used equally frequently in a genome. CUB is stronger in more highly expressed genes, a phenomenon commonly explained by stronger natural selection on translational accuracy and/or efficiency among these genes. Nevertheless, this phenomenon could also occur if CUB regulates gene expression at the mRNA level, a hypothesis that has not been tested until recently. Here, we attempt to quantify the impact of synonymous mutations on mRNA level in yeast using 3,556 synonymous variants of a heterologous gene encoding green fluorescent protein (GFP) and 523 synonymous variants of an endogenous gene TDH3. We found that mRNA level was positively correlated with CUB among these synonymous variants, demonstrating a direct role of CUB in regulating transcript concentration, likely via regulating mRNA degradation rate, as our additional experiments suggested. More importantly, we quantified the effects of individual synonymous mutations on mRNA level and found them dependent on 1) CUB and 2) mRNA secondary structure, both in proximal sequence contexts. Our study reveals the pleiotropic effects of synonymous codon usage and provides an additional explanation for the well-known correlation between CUB and gene expression level.

Independent regulation of gene expression level and noise by histone modifications.

The inherent stochasticity generates substantial gene expression variation among isogenic cells under identical conditions, which is frequently referred to as gene expression noise or cell-to-cell expression variability. Similar to (average) expression level, expression noise is also subject to natural selection. Yet it has been observed that noise is negatively correlated with expression level, which manifests as a potential constraint for simultaneous optimization of both. Here, we studied expression noise in human embryonic cells with computational analysis on single-cell RNA-seq data and in yeast with flow cytometry experiments. We showed that this coupling is overcome, to a certain degree, by a histone modification strategy in multiple embryonic developmental stages in human, as well as in yeast. Importantly, this epigenetic strategy could fit into a burst-like gene expression model: promoter-localized histone modifications (such as H3K4 methylation) are associated with both burst size and burst frequency, which together influence expression level, while gene-body-localized ones (such as H3K79 methylation) are more associated with burst frequency, which influences both expression level and noise. We further knocked out the only "writer" of H3K79 methylation in yeast, and observed that expression noise is indeed increased. Consistently, dosage sensitive genes, such as genes in the Wnt signaling pathway, tend to be marked with gene-body-localized histone modifications, while stress responding genes, such as genes regulating autophagy, tend to be marked with promoter-localized ones. Our findings elucidate that the "division of labor" among histone modifications facilitates the independent regulation of expression level and noise, extend the "histone code" hypothesis to include expression noise, and shed light on the optimization of transcriptome in evolution.

Trans-splicing enhances translational efficiency in C. elegans.

Translational efficiency is subject to extensive regulation. However, the factors influencing such regulation are poorly understood. In Caenorhabditis elegans, 62% of genes are trans-spliced to a specific spliced leader (SL1), which replaces part of the native 5' untranslated region (5' UTR). Given the pivotal role the 5' UTR plays in the regulation of translational efficiency, we hypothesized that SL1 trans-splicing functions to regulate translational efficiency. With genome-wide analysis on Ribo-seq data, polysome profiling experiments, and CRISPR-Cas9-based genetic manipulation of trans-splicing sites, we found four lines of evidence in support of this hypothesis. First, SL1 trans-spliced genes have higher translational efficiencies than non-trans-spliced genes. Second, SL1 trans-spliced genes have higher translational efficiencies than non-trans-spliced orthologous genes in other nematode species. Third, an SL1 trans-spliced isoform has higher translational efficiency than the non-trans-spliced isoform of the same gene. Fourth, deletion of trans-splicing sites of endogenous genes leads to reduced translational efficiency. Importantly, we demonstrated that SL1 trans-splicing plays a key role in enhancing translational efficiencies of essential genes. We further discovered that SL1 trans-splicing likely enhances translational efficiency by shortening the native 5' UTRs, hence reducing the presence of upstream start codons (uAUG) and weakening mRNA secondary structures. Taken together, our study elucidates the global function of trans-splicing in enhancing translational efficiency in nematodes, paving the way for further understanding the genomic mechanisms of translational regulation.

A High Temperature-Dependent Mitochondrial Lipase EXTRA GLUME1 Promotes Floral Phenotypic Robustness against Temperature Fluctuation in Rice (Oryza sativa L.).

The sessile plants have evolved diverse intrinsic mechanisms to control their proper development under variable environments. In contrast to plastic vegetative development, reproductive traits like floral identity often show phenotypic robustness against environmental variations. However, it remains obscure about the molecular basis of this phenotypic robustness. In this study, we found that eg1 (extra glume1) mutants of rice (Oryza savita L.) showed floral phenotypic variations in different growth locations resulting in a breakdown of floral identity robustness. Physiological and biochemical analyses showed that EG1 encodes a predominantly mitochondria-localized functional lipase and functions in a high temperature-dependent manner. Furthermore, we found that numerous environmentally responsive genes including many floral identity genes are transcriptionally repressed in eg1 mutants and OsMADS1, OsMADS6 and OsG1 genetically act downstream of EG1 to maintain floral robustness. Collectively, our results demonstrate that EG1 promotes floral robustness against temperature fluctuation by safeguarding the expression of floral identify genes through a high temperature-dependent mitochondrial lipid pathway and uncovers a novel mechanistic insight into floral developmental control.