Data Sanitization to Reduce Private Information Leakage from Functional Genomics.

The generation of functional genomics datasets is surging, because they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intent behind functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to broadly share raw reads for better statistical power and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, enabling principled privacy-utility trade-offs. Our protocol works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA sequencing. It involves quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.

Rewiring of the Fruit Metabolome in Tomato Breeding.

Humans heavily rely on dozens of domesticated plant species that have been further improved through intensive breeding. To evaluate how breeding changed the tomato fruit metabolome, we have generated and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes from hundreds of tomato genotypes. The combined results illustrate how breeding globally altered fruit metabolite content. Selection for alleles of genes associated with larger fruits altered metabolite profiles as a consequence of linkage with nearby genes. Selection of five major loci reduced the accumulation of anti-nutritional steroidal glycoalkaloids in ripened fruits, rendering the fruit more edible. Breeding for pink tomatoes modified the content of over 100 metabolites. The introgression of resistance genes from wild relatives in cultivars also resulted in major and unexpected metabolic changes. The study reveals a multi-omics view of the metabolic breeding history of tomato, as well as provides insights into metabolome-assisted breeding and plant biology.

Mechanisms of Deubiquitinase Specificity and Regulation.

Protein ubiquitination is one of the most powerful posttranslational modifications of proteins, as it regulates a plethora of cellular processes in distinct manners. Simple monoubiquitination events coexist with more complex forms of polyubiquitination, the latter featuring many different chain architectures. Ubiquitin can be subjected to further posttranslational modifications (e.g., phosphorylation and acetylation) and can also be part of mixed polymers with ubiquitin-like modifiers such as SUMO (small ubiquitin-related modifier) or NEDD8 (neural precursor cell expressed, developmentally downregulated 8). Together, cellular ubiquitination events form a sophisticated and versatile ubiquitin code. Deubiquitinases (DUBs) reverse ubiquitin signals with equally high sophistication. In this review, we conceptualize the many layers of specificity that DUBs encompass to control the ubiquitin code and discuss examples in which DUB specificity has been understood at the molecular level. We further discuss the many mechanisms of DUB regulation with a focus on those that modulate catalytic activity. Our review provides a framework to tackle lingering questions in DUB biology.

Ubiquiton-An inducible, linkage-specific polyubiquitylation tool.

The posttranslational modifier ubiquitin regulates most cellular processes. Its ability to form polymeric chains of distinct linkages is key to its diverse functionality. Yet, we still lack the experimental tools to induce linkage-specific polyubiquitylation of a protein of interest in cells. Here, we introduce a set of engineered ubiquitin protein ligases and matching ubiquitin acceptor tags for the rapid, inducible linear (M1-), K48-, or K63-linked polyubiquitylation of proteins in yeast and mammalian cells. By applying the so-called "Ubiquiton" system to proteasomal targeting and the endocytic pathway, we validate this tool for soluble cytoplasmic and nuclear as well as chromatin-associated and integral membrane proteins and demonstrate how it can be used to control the localization and stability of its targets. We expect that the Ubiquiton system will serve as a versatile, broadly applicable research tool to explore the signaling functions of polyubiquitin chains in many biological contexts.

The Ufd1 cofactor determines the linkage specificity of polyubiquitin chain engagement by the AAA+ ATPase Cdc48.

The AAA+ ATPase Cdc48 utilizes the cofactor Ufd1/Npl4 to bind and thread polyubiquitinated substrates for their extraction from complexes or membranes and often for subsequent proteasomal degradation. Previous studies indicated that Cdc48 engages polyubiquitin chains through the Npl4-mediated unfolding of an initiator ubiquitin; yet, the underlying principles remain largely unknown. Using FRET-based assays, we revealed the mechanisms and kinetics of ubiquitin unfolding, insertion into the ATPase, and unfolding of the ubiquitin-attached substrate. We found that Cdc48 uses Ufd1's UT3 domain to bind a K48-linked ubiquitin on the initiator's proximal side of the chain, thereby directing the initiator toward rapid unfolding by Npl4 and engagement by Cdc48. Ubiquitins on the initiator's distal side increase substrate affinity and facilitate unfolding but impede substrate release from Cdc48-Ufd1/Npl4 in the absence of additional cofactors. Our findings explain how Cdc48-UN efficiently processes substrates with K48-linked chains of 4-6 ubiquitins, which represent most cellular polyubiquitinated proteins.

Linkage reprogramming by tailor-made E3s reveals polyubiquitin chain requirements in DNA-damage bypass.

A polyubiquitin chain can adopt a variety of shapes, depending on how the ubiquitin monomers are joined. However, the relevance of linkage for the signaling functions of polyubiquitin chains is often poorly understood because of our inability to control or manipulate this parameter in vivo. Here, we present a strategy for reprogramming polyubiquitin chain linkage by means of tailor-made, linkage- and substrate-selective ubiquitin ligases. Using the polyubiquitylation of the budding yeast replication factor PCNA in response to DNA damage as a model case, we show that altering the features of a polyubiquitin chain in vivo can change the fate of the modified substrate. We also provide evidence for redundancy between distinct but structurally similar linkages, and we demonstrate by proof-of-principle experiments that the method can be generalized to targets beyond PCNA. Our study illustrates a promising approach toward the in vivo analysis of polyubiquitin signaling.

A powerful approach to identify replicable variants in genome-wide association studies.

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Thermodynamic modulation of gephyrin condensation by inhibitory synapse components.

Phase separation drives compartmentalization of intracellular contents into various biomolecular condensates. Individual condensate components are thought to differentially contribute to the organization and function of condensates. However, how intermolecular interactions among constituent biomolecules modulate the phase behaviors of multicomponent condensates remains unclear. Here, we used core components of the inhibitory postsynaptic density (iPSD) as a model system to quantitatively probe how the network of intra- and intermolecular interactions defines the composition and cellular distribution of biomolecular condensates. We found that oligomerization-driven phase separation of gephyrin, an iPSD-specific scaffold, is critically modulated by an intrinsically disordered linker region exhibiting minimal homotypic attractions. Other iPSD components, such as neurotransmitter receptors, differentially promote gephyrin condensation through distinct binding modes and affinities. We further demonstrated that the local accumulation of scaffold-binding proteins at the cell membrane promotes the nucleation of gephyrin condensates in neurons. These results suggest that in multicomponent systems, the extent of scaffold condensation can be fine-tuned by scaffold-binding factors, a potential regulatory mechanism for self-organized compartmentalization in cells.

Crosstalk between Lys63- and Lys11-polyubiquitin signaling at DNA damage sites is driven by Cezanne.

The establishment of polyubiquitin conjugates with distinct linkages play important roles in the DNA damage response. Much remains unknown about the regulation of linkage-specific ubiquitin signaling at sites of DNA damage. Here we reveal that Cezanne (also known as Otud7B) deubiquitinating enzyme promotes the recruitment of Rap80/BRCA1-A complex by binding to Lys63-polyubiquitin and targeting Lys11-polyubiquitin. Using a ubiquitin binding domain protein array screen, we identify that the UBA domains of Cezanne and Cezanne2 (also known as Otud7A) selectively bind to Lys63-linked polyubiquitin. Increased Lys11-linkage ubiquitination due to lack of Cezanne DUB activity compromises the recruitment of Rap80/BRCA1-A. Cezanne2 interacts with Cezanne, facilitating Cezanne in the recruitment of Rap80/BRCA1-A, Rad18, and 53BP1, in cellular resistance to ionizing radiation and DNA repair. Our work presents a model that Cezanne serves as a "reader" of the Lys63-linkage polyubiquitin at DNA damage sites and an "eraser" of the Lys11-linkage ubiquitination, indicating a crosstalk between linkage-specific ubiquitination at DNA damage sites.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

UV-Protection Timer Controls Linkage between Stress and Pigmentation Skin Protection Systems.

Skin sun exposure induces two protection programs: stress responses and pigmentation, the former within minutes and the latter only hours afterward. Although serving the same physiological purpose, it is not known whether and how these programs are coordinated. Here, we report that UVB exposure every other day induces significantly more skin pigmentation than the higher frequency of daily exposure, without an associated increase in stress responses. Using mathematical modeling and empirical studies, we show that the melanocyte master regulator, MITF, serves to synchronize stress responses and pigmentation and, furthermore, functions as a UV-protection timer via damped oscillatory dynamics, thereby conferring a trade-off between the two programs. MITF oscillations are controlled by multiple negative regulatory loops, one at the transcriptional level involving HIF1α and another post-transcriptional loop involving microRNA-148a. These findings support trait linkage between the two skin protection programs, which, we speculate, arose during furless skin evolution to minimize skin damage.

The genomics of linkage drag in inbred lines of sunflower.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units.

Biomolecular condensates form via multivalent interactions among key macromolecules and are regulated through ligand binding and/or posttranslational modifications. One such modification is ubiquitination, the covalent addition of ubiquitin (Ub) or polyubiquitin chains to target macromolecules. Specific interactions between polyubiquitin chains and partner proteins, including hHR23B, NEMO, and UBQLN2, regulate condensate assembly or disassembly. Here, we used a library of designed polyubiquitin hubs and UBQLN2 as model systems for determining the driving forces of ligand-mediated phase transitions. Perturbations to either the UBQLN2-binding surface of Ub or the spacing between Ub units reduce the ability of hubs to modulate UBQLN2 phase behavior. By developing an analytical model based on polyphasic linkage principles that accurately described the effects of different hubs on UBQLN2 phase separation, we determined that introduction of Ub to UBQLN2 condensates incurs a significant inclusion energetic penalty. This penalty antagonizes the ability of polyUb hubs to scaffold multiple UBQLN2 molecules and cooperatively amplify phase separation. The extent to which polyubiquitin hubs promote UBQLN2 phase separation is encoded in the spacings between Ub units. This spacing is modulated by chains of different linkages and designed chains of different architectures, thus illustrating how the ubiquitin code regulates functionality via the emergent properties of the condensate. The spacing in naturally occurring linear polyubiquitin chains is already optimized to promote phase separation with UBQLN2. We expect our findings to extend to other condensates, emphasizing the importance of ligand properties, including concentration, valency, affinity, and spacing between binding sites in studies and designs of condensates.

Identification of anther thermotolerance genes by the integration of linkage and association analysis in maize.

Maize is one of the world's most important staple crops, yet its production is increasingly threatened by the rising frequency of high-temperature stress (HTS). To investigate the genetic basis of anther thermotolerance under field conditions, we performed linkage and association analysis to identify HTS response quantitative trait loci (QTL) using three recombinant inbred line (RIL) populations and an association panel containing 375 diverse maize inbred lines. These analyses resulted in the identification of 16 co-located large QTL intervals. Among the 37 candidate genes identified in these QTL intervals, five have rice or Arabidopsis homologs known to influence pollen and filament development. Notably, one of the candidate genes, ZmDUP707, has been subject to selection pressure during breeding. Its expression is suppressed by HTS, leading to pollen abortion and barren seeds. We also identified several additional candidate genes potentially underly QTL previously reported by other researchers. Taken together, our results provide a pool of valuable candidate genes that could be employed by future breeding programs aiming at enhancing maize HTS tolerance.

Genetic basis of maize stalk strength decoded via linkage and association mapping.

Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.

Association Analysis Provides Insights into Plant Mitonuclear Interactions.

Cytonuclear interaction refers to the complex and ongoing process of coevolution between nuclear and organelle genomes, which are responsible for cellular respiration, photosynthesis, lipid metabolism, etc. and play a significant role in adaptation and speciation. There have been a large number of studies to detect signatures of cytonuclear interactions. However, identification of the specific nuclear and organelle genetic polymorphisms that are involved in these interactions within a species remains relatively rare. The recent surge in whole genome sequencing has provided us an opportunity to explore cytonuclear interaction from a population perspective. In this study, we analyzed a total of 3,439 genomes from 7 species to identify signals of cytonuclear interactions by association (linkage disequilibrium) analysis of variants in both the mitochondrial and nuclear genomes across flowering plants. We also investigated examples of nuclear loci identified based on these association signals using subcellular localization assays, gene editing, and transcriptome sequencing. Our study provides a novel perspective on the investigation of cytonuclear coevolution, thereby enriching our understanding of plant fitness and offspring sterility.

Inference of Host-Pathogen Interaction Matrices from Genome-Wide Polymorphism Data.

Host-pathogen coevolution is defined as the reciprocal evolutionary changes in both species due to genotype × genotype (G×G) interactions at the genetic level determining the outcome and severity of infection. While co-analyses of hosts and pathogen genomes (co-genome-wide association studies) allow us to pinpoint the interacting genes, these do not reveal which host genotype(s) is/are resistant to which pathogen genotype(s). The knowledge of this so-called infection matrix is important for agriculture and medicine. Building on established theories of host-pathogen interactions, we here derive four novel indices capturing the characteristics of the infection matrix. These indices can be computed from full genome polymorphism data of randomly sampled uninfected hosts, as well as infected hosts and their pathogen strains. We use these indices in an approximate Bayesian computation method to pinpoint loci with relevant G×G interactions and to infer their underlying interaction matrix. In a combined single nucleotide polymorphism dataset of 451 European humans and their infecting hepatitis C virus (HCV) strains and 503 uninfected individuals, we reveal a new human candidate gene for resistance to HCV and new virus mutations matching human genes. For two groups of significant human-HCV (G×G) associations, we infer a gene-for-gene infection matrix, which is commonly assumed to be typical of plant-pathogen interactions. Our model-based inference framework bridges theoretical models of G×G interactions with host and pathogen genomic data. It, therefore, paves the way for understanding the evolution of key G×G interactions underpinning HCV adaptation to the European human population after a recent expansion.

Inference of Locus-Specific Population Mixtures from Linked Genome-Wide Allele Frequencies.

Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and f-statistics in terms of accuracy and sensitivity to detect introgressed loci.