Gene Essentiality Is a Quantitative Property Linked to Cellular Evolvability.

Gene essentiality is typically determined by assessing the viability of the corresponding mutant cells, but this definition fails to account for the ability of cells to adaptively evolve to genetic perturbations. Here, we performed a stringent screen to assess the degree to which Saccharomyces cerevisiae cells can survive the deletion of ~1,000 individual "essential" genes and found that ~9% of these genetic perturbations could in fact be overcome by adaptive evolution. Our analyses uncovered a genome-wide gradient of gene essentiality, with certain essential cellular functions being more "evolvable" than others. Ploidy changes were prevalent among the evolved mutant strains, and aneuploidy of a specific chromosome was adaptive for a class of evolvable nucleoporin mutants. These data justify a quantitative redefinition of gene essentiality that incorporates both viability and evolvability of the corresponding mutant cells and will enable selection of therapeutic targets associated with lower risk of emergence of drug resistance.

A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways.

Chaperones are abundant cellular proteins that promote the folding and function of their substrate proteins (clients). In vivo, chaperones also associate with a large and diverse set of cofactors (cochaperones) that regulate their specificity and function. However, how these cochaperones regulate protein folding and whether they have chaperone-independent biological functions is largely unknown. We combined mass spectrometry and quantitative high-throughput LUMIER assays to systematically characterize the chaperone-cochaperone-client interaction network in human cells. We uncover hundreds of chaperone clients, delineate their participation in specific cochaperone complexes, and establish a surprisingly distinct network of protein-protein interactions for cochaperones. As a salient example of the power of such analysis, we establish that NUDC family cochaperones specifically associate with structurally related but evolutionarily distinct ß-propeller folds. We provide a framework for deciphering the proteostasis network and its regulation in development and disease and expand the use of chaperones as sensors for drug-target engagement.

Mutant analysis of Kcng4b reveals how the different functional states of the voltage-gated potassium channel regulate ear development.

The voltage gated (Kv) slow-inactivating delayed rectifier channel regulates the development of hollow organs of the zebrafish. The functional channel consists of the tetramer of electrically active Kcnb1 (Kv2.1) subunits and Kcng4b (Kv6.4) modulatory or electrically silent subunits. The two mutations in zebrafish kcng4b gene - kcng4b-C1 and kcng4b-C2 (Gasanov et al., 2021) - have been studied during ear development using electrophysiology, developmental biology and in silico structural modelling. kcng4b-C1 mutation causes a C-terminal truncation characterized by mild Kcng4b loss-of-function (LOF) manifested by failure of kinocilia to extend and formation of ectopic otoliths. In contrast, the kcng4b-C2-/- mutation causes the C-terminal domain to elongate and the ectopic seventh transmembrane (TM) domain to form, converting the intracellular C-terminus to an extracellular one. Kcng4b-C2 acts as a Kcng4b gain-of-function (GOF) allele. Otoliths fail to develop and kinocilia are reduced in kcng4b-C2-/-. These results show that different mutations of the silent subunit Kcng4 can affect the activity of the Kv channel and cause a wide range of developmental defects.

Non-genetic and genetic rewiring underlie adaptation to hypomorphic alleles of an essential gene.

Adaptive evolution to cellular stress is a process implicated in a wide range of biological and clinical phenomena. Two major routes of adaptation have been identified: non-genetic changes, which allow expression of different phenotypes in novel environments, and genetic variation achieved by selection of fitter phenotypes. While these processes are broadly accepted, their temporal and epistatic features in the context of cellular evolution and emerging drug resistance are contentious. In this manuscript, we generated hypomorphic alleles of the essential nuclear pore complex (NPC) gene NUP58. By dissecting early and long-term mechanisms of adaptation in independent clones, we observed that early physiological adaptation correlated with transcriptome rewiring and upregulation of genes known to interact with the NPC; long-term adaptation and fitness recovery instead occurred via focal amplification of NUP58 and restoration of mutant protein expression. These data support the concept that early phenotypic plasticity allows later acquisition of genetic adaptations to a specific impairment. We propose this approach as a genetic model to mimic targeted drug therapy in human cells and to dissect mechanisms of adaptation.

Adjunctive pascolizumab in rifampicin-susceptible pulmonary tuberculosis: proof-of-concept, partially-randomised, double-blind, placebo-controlled, dose-escalation trial.

BACKGROUND: Interleukin-4 (IL-4), increased in tuberculosis infection, may impair bacterial killing. Blocking IL-4 confers benefit in animal models. We evaluated safety and efficacy of pascolizumab (humanised anti-IL-4 monoclonal antibody) as adjunctive tuberculosis treatment. METHODS: Participants with rifampicin-susceptible pulmonary tuberculosis received a single intravenous infusion of pascolizumab or placebo; and standard 6-month tuberculosis treatment. Pascolizumab dose increased in successive cohorts: [1] non-randomised 0.05 mg/kg (n = 4); [2] non-randomised 0.5 mg/kg (n = 4); [3] randomised 2.5 mg/kg (n = 9) or placebo (n = 3); [4] randomised 10 mg/kg (n = 9) or placebo (n = 3). Co-primary safety outcome was study-drug-related grade 4 or serious adverse event (G4/SAE); in all cohorts (1-4). Co-primary efficacy outcome was week-8 sputum culture time-to-positivity (TTP); in randomised cohorts (3-4) combined. RESULTS: Pascolizumab levels exceeded IL-4 50% neutralising dose for 8 weeks in 78-100% of participants in cohorts 3-4. There were no study-drug-related G4/SAEs. Median week-8 TTP was 42 days in pascolizumab and placebo groups (p = 0.185). Rate of TTP increase was greater with pascolizumab (difference from placebo 0.011 [95% Bayesian credible interval 0.006 to 0.015] log10TTP/day. CONCLUSIONS: There was no evidence to suggest blocking IL-4 was unsafe. Preliminary efficacy findings are consistent with animal models. This supports further investigation of adjunctive anti-IL-4 interventions for tuberculosis in larger phase 2 trials.

Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.

Statistical analysis of ultrahigh-dimensional omics scale data has long depended on univariate hypothesis testing. With growing data features and samples, the obvious next step is to establish multivariable association analysis as a routine method to describe genotype-phenotype association. Here we present ParProx, a state-of-the-art implementation to optimize overlapping and non-overlapping group lasso regression models for time-to-event and classification analysis, with selection of variables grouped by biological priors. ParProx enables multivariable model fitting for ultrahigh-dimensional data within an architecture for parallel or distributed computing via latent variable group representation. It thereby aims to produce interpretable regression models consistent with known biological relationships among independent variables, a property often explored post hoc, not during model estimation. Simulation studies clearly demonstrate the scalability of ParProx with graphics processing units in comparison to existing implementations. We illustrate the tool using three different omics data sets featuring moderate to large numbers of variables, where we use genomic regions and biological pathways as variable groups, rendering the selected independent variables directly interpretable with respect to those groups. ParProx is applicable to a wide range of studies using ultrahigh-dimensional omics data, from genome-wide association analysis to multi-omics studies where model estimation is computationally intractable with existing implementation.

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.

Novel Autoantibodies in Idiopathic Small Fiber Neuropathy.

OBJECTIVE: Small fiber neuropathy (SFN) is clinically and etiologically heterogeneous. Although autoimmunity has been postulated to be pathophysiologically important in SFN, few autoantibodies have been described. We aimed to identify autoantibodies associated with idiopathic SFN (iSFN) by a novel high-throughput protein microarray platform that captures autoantibodies expressed in the native conformational state. METHODS: Sera from 58 SFN patients and 20 age- and gender-matched healthy controls (HCs) were screened against >1,600 immune-related antigens. Fluorescent unit readout and postassay imaging were performed, followed by composite data normalization and protein fold change (pFC) analysis. Analysis of an independent validation cohort of 33 SFN patients against the same 20 HCs was conducted to identify reproducible proteins in both cohorts. RESULTS: Nine autoantibodies were screened with statistical significance and pFC criteria in both cohorts, with at least 50% change in serum levels. Three proteins showed consistently high fold changes in main and validation cohorts: MX1 (FC = 2.99 and 3.07, respectively, p = 0.003, q = 0.076), DBNL (FC = 2.11 and 2.16, respectively, p = 0.009, q < 0.003), and KRT8 (FC = 1.65 and 1.70, respectively, p = 0.043, q < 0.003). Further subgroup analysis into iSFN and SFN by secondary causes (secondary SFN) in the main cohort showed that MX1 is higher in iSFN compared to secondary SFN (FC = 1.61 vs 0.106, p = 0.009). INTERPRETATION: Novel autoantibodies MX1, DBNL, and KRT8 are found in iSFN. MX1 may allow diagnostic subtyping of iSFN patients. ANN NEUROL 2022;91:66-77.

Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis.

OBJECTIVE: While the risk of acute coronary events has been associated with biological variability of circulating cholesterol, the association with variability of other atherogenic lipids remains less understood. We evaluated the longitudinal variability of 284 lipids and investigated their association with asymptomatic coronary atherosclerosis. Approach and Results: Circulating lipids were extracted from fasting blood samples of 83 community-sampled symptom-free participants (age 41-75 years), collected longitudinally over 6 months. Three types of coronary plaque volume (calcified, lipid-rich, and fibrotic) were quantified using computed tomography coronary angiogram. We first deconvoluted between-subject (CVg) and within-subject (CVw) lipid variabilities. We then tested whether the mean lipid abundance was different across groups categorized by Framingham risk score and plaques phenotypes (lipid-rich, fibrotic, and calcified). Finally, we investigated whether visit-to-visit variability of each lipid was associated with plaque burden. Most lipids (72.5%) exhibited higher CVg than CVw. Among the lipids (n=145) with 1.2-fold higher CVg than CVw, 26 species including glycerides and ceramides were significantly associated with Framingham risk score and the 3 plaque phenotypes (false discovery rate <0.05). In an exploratory analysis of person-specific visit-to-visit variability without multiple testing correction, high variability of 3 lysophospholipids (lysophosphatidylethanolamines 16:0, 18:0, and lysophosphatidylcholine O-18:1) was associated with lipid-rich and fibrotic (noncalcified) plaque volume while high variability of diacylglycerol 18:1_20:0, triacylglycerols 52:2, 52:3, and 52:4, ceramide d18:0/20:0, dihexosylceramide d18:1/16:0, and sphingomyelin 36:3 was associated with calcified plaque volume. CONCLUSIONS: High person-specific longitudinal variation of specific nonsterol lipids is associated with the burden of subclinical coronary atherosclerosis. Larger studies are needed to confirm these exploratory findings.

New Proteomic Signatures to Distinguish Between Zika and Dengue Infections.

Distinguishing between Zika and dengue virus infections is critical for accurate treatment, but we still lack detailed understanding of their impact on their host. To identify new protein signatures of the two infections, we used next-generation proteomics to profile 122 serum samples from 62 Zika and dengue patients. We quantified >500 proteins and identified 13 proteins that were significantly differentially expressed (adjusted p-value < 0.05). These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain function. We successfully validated expression differences with Carbonic Anhydrase 2 in both the original and an independent sample set. Three of the differentially expressed proteins, i.e., Fibrinogen Alpha, Platelet Factor 4 Variant 1, and Pro-Platelet Basic Protein, predicted Zika virus infection at a ∼70% true-positive and 6% false-positive rate. Further, we showed that intraindividual temporal changes in protein signatures can disambiguate diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can provide new resources that serve to distinguish between different viral infections.

Loss of hepatocyte cell division leads to liver inflammation and fibrosis.

The liver possesses a remarkable regenerative capacity based partly on the ability of hepatocytes to re-enter the cell cycle and divide to replace damaged cells. This capability is substantially reduced upon chronic damage, but it is not clear if this is a cause or consequence of liver disease. Here, we investigate whether blocking hepatocyte division using two different mouse models affects physiology as well as clinical liver manifestations like fibrosis and inflammation. We find that in P14 Cdk1Liv-/- mice, where the division of hepatocytes is abolished, polyploidy, DNA damage, and increased p53 signaling are prevalent. Cdk1Liv-/- mice display classical markers of liver damage two weeks after birth, including elevated ALT, ALP, and bilirubin levels, despite the lack of exogenous liver injury. Inflammation was further studied using cytokine arrays, unveiling elevated levels of CCL2, TIMP1, CXCL10, and IL1-Rn in Cdk1Liv-/- liver, which resulted in increased numbers of monocytes. Ablation of CDK2-dependent DNA re-replication and polyploidy in Cdk1Liv-/- mice reversed most of these phenotypes. Overall, our data indicate that blocking hepatocyte division induces biological processes driving the onset of the disease phenotype. It suggests that the decrease in hepatocyte division observed in liver disease may not only be a consequence of fibrosis and inflammation, but also a pathological cue.

Knockout of the non-essential gene SUGCT creates diet-linked, age-related microbiome disbalance with a diabetes-like metabolic syndrome phenotype.

SUGCT (C7orf10) is a mitochondrial enzyme that synthesizes glutaryl-CoA from glutarate in tryptophan and lysine catabolism, but it has not been studied in vivo. Although mutations in Sugct lead to Glutaric Aciduria Type 3 disease in humans, patients remain largely asymptomatic despite high levels of glutarate in the urine. To study the disease mechanism, we generated SugctKO mice and uncovered imbalanced lipid and acylcarnitine metabolism in kidney in addition to changes in the gut microbiome. After SugctKO mice were treated with antibiotics, metabolites were comparable to WT, indicating that the microbiome affects metabolism in SugctKO mice. SUGCT loss of function contributes to gut microbiota dysbiosis, leading to age-dependent pathological changes in kidney, liver, and adipose tissue. This is associated with an obesity-related phenotype that is accompanied by lipid accumulation in kidney and liver, as well as "crown-like" structures in adipocytes. Furthermore, we show that the SugctKO kidney pathology is accelerated and exacerbated by a high-lysine diet. Our study highlights the importance of non-essential genes with no readily detectable early phenotype, but with substantial contributions to the development of age-related pathologies, which result from an interplay between genetic background, microbiome, and diet in the health of mammals.

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.

A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes.

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 232 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 76% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore, providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patient's genetic predisposition interacts with the effect of somatic mutations on survival outcomes in some cancers.

CDK10 Mutations in Humans and Mice Cause Severe Growth Retardation, Spine Malformations, and Developmental Delays.

In five separate families, we identified nine individuals affected by a previously unidentified syndrome characterized by growth retardation, spine malformation, facial dysmorphisms, and developmental delays. Using homozygosity mapping, array CGH, and exome sequencing, we uncovered bi-allelic loss-of-function CDK10 mutations segregating with this disease. CDK10 is a protein kinase that partners with cyclin M to phosphorylate substrates such as ETS2 and PKN2 in order to modulate cellular growth. To validate and model the pathogenicity of these CDK10 germline mutations, we generated conditional-knockout mice. Homozygous Cdk10-knockout mice died postnatally with severe growth retardation, skeletal defects, and kidney and lung abnormalities, symptoms that partly resemble the disease's effect in humans. Fibroblasts derived from affected individuals and Cdk10-knockout mouse embryonic fibroblasts (MEFs) proliferated normally; however, Cdk10-knockout MEFs developed longer cilia. Comparative transcriptomic analysis of mutant and wild-type mouse organs revealed lipid metabolic changes consistent with growth impairment and altered ciliogenesis in the absence of CDK10. Our results document the CDK10 loss-of-function phenotype and point to a function for CDK10 in transducing signals received at the primary cilia to sustain embryonic and postnatal development.

MRMkit: Automated Data Processing for Large-Scale Targeted Metabolomics Analysis.

MRMkit is an open-source software package designed for automated processing of large-scale targeted mass spectrometry-based metabolomics data. With improvements in the automation of sample preparation for LC-MS analysis, a challenging next step is to fully automate the workflow to process raw data and ensure the quality of measurements in large-scale analysis settings. MRMkit capitalizes on the richness of large-sample data in capturing peak shapes and interference patterns of transitions across many samples and delivers fully automated, reproducible peak integration results in a scalable and time-efficient manner. In addition to fast and accurate peak integration, the tool also provides reliable data normalization functions and quality metrics along with visualizations for fast data quality evaluation. In addition, MRMkit learns retention time offset patterns by user-specified compound classes and makes recommendations for peak picking in multimodal ion chromatograms. In summary, MRMkit offers highly consistent and scalable data processing capacity for targeted metabolomics, substantially curtailing the time required to produce the final quantification results after LC-MS analysis.

SLIDE - a web-based tool for interactive visualization of large-scale - omics data.

Summary: Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDE's interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen. Availability and implementation: SLIDE is publicly available under BSD license both as an online version as well as a stand-alone version at https://github.com/soumitag/SLIDE. Supplementary information: Supplementary data are available at Bioinformatics online.

Plasma sphingolipids and risk of cardiovascular diseases: a large-scale lipidomic analysis.

INTRODUCTION: Sphingolipids are a diverse class of lipids with various roles in cell functions and subclasses such as ceramides have been associated with cardiovascular diseases (CVD) in previous studies. OBJECTIVES: We aimed to measure molecularly-distinct sphingolipids via a large-scale lipidomic analysis and expand the literature to an Asian population. METHODS: We performed a lipidomics evaluation of 79 molecularly distinct sphingolipids in the plasma of 2627 ethnically-Chinese Singaporeans. RESULTS: During a mean follow-up of 12.9 years, we documented 152 cases of major CVD (non-fatal myocardial infarction, stroke and cardiovascular death). Total ceramide concentrations were not associated with CVD risk [hazard ratio (HR), 0.99; 95% CI 0.81-1.21], but higher circulating total monohexosylceramides (HR, 1.22; 95% CI 1.03, 1.45), total long-chain sphingolipids (C16-C18) (HR, 1.22; 95% CI 1.02, 1.45) and total 18:1 sphingolipids (HR, 1.21; 95% CI 1.01, 1.46) were associated with higher CVD risk after adjusting for conventional CVD risk factors. CONCLUSIONS: Our results do not support the hypothesis that higher ceramide concentrations are linked to higher CVD risk, but suggest that other classes of sphingolipids may affect CVD risk.

Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data.

In mass spectrometry (MS) experiments, more than thousands of peaks are detected in the space of mass-to-charge ratio and chromatographic retention time, each associated with an abundance measurement. However, a large proportion of the peaks consists of experimental noise and low abundance compounds are typically masked by noise peaks, compromising the quality of the data. In this paper, we propose a new measure of similarity between a pair of MS experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. A comprehensive numerical study suggests that TRC outperforms traditional sample correlation and Kendall's τ. We apply TRC to measuring test-retest reliability of two MS experiments, including biological replicate analysis of the metabolome in HEK293 cells and metabolomic profiling of benign prostate hyperplasia (BPH) patients. An R package trc of the proposed TRC and related functions is available at https://sites.google.com/site/dhyeonyu/software.

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.