Genome-wide Prediction Research Articles

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants.

BackgroundGenome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline.ResultsBy comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships.ConclusionsGOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.

Though genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants. We will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets. https://github.com/lichen-lab/TLVar. Supplementary data are available at Bioinformatics online.

Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry.

High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.

Genome-wide risk prediction of common diseases across ancestries in one million people

SummaryPolygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.

Exploration of Target Spaces in the Human Genome for Protein and Peptide Drugs

After decades of development, protein and peptide drugs have now grown into a major drug class in the marketplace. Target identification and validation are crucial for the discovery of protein and peptide drugs, and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection. However, owing to the developmental history in the pharmaceutical industry, previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs, while studies related to protein and peptide drugs are lacking. Here, we systematically explore the target spaces in the human genome specifically for protein and peptide drugs. Compared with other proteins, both successful protein and peptide drug targets have many special characteristics, and are also significantly different from those of small-molecule drugs in many aspects. Based on these features, we develop separate effective genome-wide target prediction models for protein and peptide drugs. Finally, a user-friendly web server, Predictor Of Protein and PeptIde drugs’ therapeutic Targets (POPPIT) (http://poppit.ncpsb.org.cn/), is established, which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women

Polygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women. To develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors. This diagnostic study included women of Asian ancestry from the Asia Breast Cancer Consortium. PRSs were developed using data from genomewide association studies (GWASs) of breast cancer conducted among 123 041 women with Asian ancestry (including 18 650 women with breast cancer) using 3 approaches: (1) reported PRS for women with European ancestry; (2) breast cancer-associated single-nucleotide variations (SNVs) identified by fine-mapping of GWAS-identified risk loci; and (3) genomewide risk prediction algorithms. A nongenetic risk score (NGRS) was built, including 7 well-established nongenetic risk factors, using data of 416 case participants and 1558 control participants from a prospective cohort study. PRSs were initially validated in an independent data set including 1426 case participants and 1323 control participants and further evaluated, along with the NGRS, in the second data set including 368 case participants and 736 control participants nested within a prospective cohort study. Logistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% CIs and area under the receiver operating characteristic curve (AUC). A total of 126 894 women of Asian ancestry were included; 20 444 (16.1%) had breast cancer. The mean (SD) age ranged from 49.1 (10.8) to 54.4 (10.4) years for case participants and 50.6 (9.5) to 54.0 (7.4) years for control participants among studies that provided demographic characteristics. In the prospective cohort, a PRS with 111 SNVs developed using the fine-mapping approach (PRS111) showed a prediction performance comparable with a genomewide PRS that included more than 855 000 SNVs. The OR per SD increase of PRS111 score was 1.67 (95% CI, 1.46-1.92), with an AUC of 0.639 (95% CI, 0.604-0.674). The NGRS had a limited predictive ability (AUC, 0.565; 95% CI, 0.529-0.601). Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and NGRS were at a 3.84-fold (95% CI, 2.30-6.46) and 2.10-fold (95% CI, 1.22-3.62) higher risk of breast cancer, respectively. The prediction model including both PRS111 and NGRS achieved the highest prediction accuracy (AUC, 0.648; 95% CI, 0.613-0.682). In this study, PRSs derived using breast cancer risk-associated SNVs had similar predictive performance in Asian and European women. Including nongenetic risk factors in models further improved prediction accuracy. These findings support the utility of these models in developing personalized screening and prevention strategies.

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility

Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions and then incorporates such features with expression levels and binding activities of human core TFs to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In a detailed analysis of TF activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a TF to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.

GWAS and genomic prediction revealed potential for genetic improvement of large yellow croaker adapting to high plant protein diet

Fish meal as an indispensable feed source for aquaculture, seriously affects the development of wild fish resources. As an alternative aquafeed, plant-based diets are very important for healthy and sustainable culture of large yellow croaker. Genomic selection, as a method of genetic improvement of economic traits, has great potential and value in breeding new lines of large yellow croaker that are highly adaptable to plant proteins. In this study, 2048 large yellow croakers were injected with passive integrated transponder (PIT) tags and divided into two groups with the same feeding management. One group (C group) replaced 30% fish meal with plant protein, and the other group (P group) replaced 70% fish meal with plant protein. Body weight and body length of group C and P were measured respectively at 0, 84 and 206 days. To eliminate differences in initial body weight and influence on weight gain, weight gain rate (WGR) was used as the primary analysis trait. Fin clips were collected and were sequenced using Double-digest restriction-associated (ddRAD) approach. A total of 891 individuals and 34,994 SNPs passed the quality control procedure for Genome wide association study (GWAS) and genomic prediction (GP) analyses. The heritability of each trait was also calculated. The result shows that there is no difference in growth performance when 70% fish meal was replaced with plant protein compared with 30% fish meal replaced. The heritability of WGR1 (weight gain rate from 0 to 84 days) in the P group was 0.16. A total of 36 SNPs associated with WGR were identified, and a significant peak was found at 11.9–18.9 Mb on chromosome 18. The predictive ability of GBLUP and BayesB for WGR1 in P group was 0.10 and 0.11, respectively. This study provided reference for genetic improvement of efficient plant protein utilization in large yellow croaker by using genomic selection.

Structural Predictions of the SNX-RGS Proteins Suggest They Belong to a New Class of Lipid Transfer Proteins.

Recent advances in protein structure prediction using machine learning such as AlphaFold2 and RosettaFold presage a revolution in structural biology. Genome-wide predictions of protein structures are providing unprecedented insights into their architecture and intradomain interactions, and applications have already progressed towards assessing protein complex formation. Here we present detailed analyses of the sorting nexin proteins that contain regulator of G-protein signalling domains (SNX-RGS proteins), providing a key example of the ability of AlphaFold2 to reveal novel structures with previously unsuspected biological functions. These large proteins are conserved in most eukaryotes and are known to associate with lipid droplets (LDs) and sites of LD-membrane contacts, with key roles in regulating lipid metabolism. They possess five domains, including an N-terminal transmembrane domain that anchors them to the endoplasmic reticulum, an RGS domain, a lipid interacting phox homology (PX) domain and two additional domains named the PXA and PXC domains of unknown structure and function. Here we report the crystal structure of the RGS domain of sorting nexin 25 (SNX25) and show that the AlphaFold2 prediction closely matches the experimental structure. Analysing the full-length SNX-RGS proteins across multiple homologues and species we find that the distant PXA and PXC domains in fact fold into a single unique structure that notably features a large and conserved hydrophobic pocket. The nature of this pocket strongly suggests a role in lipid or fatty acid binding, and we propose that these molecules represent a new class of conserved lipid transfer proteins.

Genome-Wide Prediction of Transcription Start Sites in Conifers.

The identification of promoters is an essential step in the genome annotation process, providing a framework for gene regulatory networks and their role in transcription regulation. Despite considerable advances in the high-throughput determination of transcription start sites (TSSs) and transcription factor binding sites (TFBSs), experimental methods are still time-consuming and expensive. Instead, several computational approaches have been developed to provide fast and reliable means for predicting the location of TSSs and regulatory motifs on a genome-wide scale. Numerous studies have been carried out on the regulatory elements of mammalian genomes, but plant promoters, especially in gymnosperms, have been left out of the limelight and, therefore, have been poorly investigated. The aim of this study was to enhance and expand the existing genome annotations using computational approaches for genome-wide prediction of TSSs in the four conifer species: loblolly pine, white spruce, Norway spruce, and Siberian larch. Our pipeline will be useful for TSS predictions in other genomes, especially for draft assemblies, where reliable TSS predictions are not usually available. We also explored some of the features of the nucleotide composition of the predicted promoters and compared the GC properties of conifer genes with model monocot and dicot plants. Here, we demonstrate that even incomplete genome assemblies and partial annotations can be a reliable starting point for TSS annotation. The results of the TSS prediction in four conifer species have been deposited in the Persephone genome browser, which allows smooth visualization and is optimized for large data sets. This work provides the initial basis for future experimental validation and the study of the regulatory regions to understand gene regulation in gymnosperms.

Genome-wide association, prediction and heritability in bacteria with application to Streptococcus pneumoniae.

Whole-genome sequencing has facilitated genome-wide analyses of association, prediction and heritability in many organisms. However, such analyses in bacteria are still in their infancy, being limited by difficulties including genome plasticity and strong population structure. Here we propose a suite of methods including linear mixed models, elastic net and LD-score regression, adapted to bacterial traits using innovations such as frequency-based allele coding, both insertion/deletion and nucleotide testing and heritability partitioning. We compare and validate our methods against the current state-of-art using simulations, and analyse three phenotypes of the major human pathogen Streptococcus pneumoniae, including the first analyses of minimum inhibitory concentrations (MIC) for penicillin and ceftriaxone. We show that the MIC traits are highly heritable with high prediction accuracy, explained by many genetic associations under good population structure control. In ceftriaxone MIC, this is surprising because none of the isolates are resistant as per the inhibition zone criteria. We estimate that half of the heritability of penicillin MIC is explained by a known drug-resistance region, which also contributes a quarter of the ceftriaxone MIC heritability. For the within-host carriage duration phenotype, no associations were observed, but the moderate heritability and prediction accuracy indicate a moderately polygenic trait.

Genome-Wide Prediction, Functional Divergence, and Characterization of Stress-Responsive BZR Transcription Factors in B. napus.

BRASSINAZOLE RESISTANT (BZR) are transcriptional factors that bind to the DNA of targeted genes to regulate several plant growth and physiological processes in response to abiotic and biotic stresses. However, information on such genes in Brassica napus is minimal. Furthermore, the new reference Brassica napus genome offers an excellent opportunity to systematically characterize this gene family in B. napus. In our study, 21 BnaBZR genes were distributed across 19 chromosomes of B. napus and clustered into four subgroups based on Arabidopsis thaliana orthologs. Functional divergence analysis among these groups evident the shifting of evolutionary rate after the duplication events. In terms of structural analysis, the BnaBZR genes within each subgroup are highly conserved but are distinctive within groups. Organ-specific expression analyses of BnaBZR genes using RNA-seq data and quantitative real-time polymerase chain reaction (qRT-PCR) revealed complex expression patterns in plant tissues during stress conditions. In which genes belonging to subgroups III and IV were identified to play central roles in plant tolerance to salt, drought, and Sclerotinia sclerotiorum stress. The insights from this study enrich our understanding of the B. napus BZR gene family and lay a foundation for future research in improving rape seed environmental adaptability.

Genome-wide development of interspecific microsatellite markers for Saccharum officinarum and Saccharum spontaneum

Sugarcane has a large, complex, polyploid genome that has hindered the progress of genomic research and molecular marker-assisted selection. The user-friendly SSR markers have attracted considerable attention owing to their ideal genetic attributes. However, these markers were not characterized and developed at the genome-wide scale due to the previously lacking high-quality chromosome-level assembled sugarcane genomes. In this present study, 744305 and 361638 candidate SSRs were identified from the genomes of S. officinarum and S. spontaneum, respectively. We verified the reliability of the predicted SSRs by using 1200 interspecific SSR primer pairs to detect polymorphisms among 11 representative accessions of Saccharum, including S. spontaneum, S. officinarum, S. robustum, and modern sugarcane hybrid. The results showed that 660 SSR markers displayed interspecific polymorphisms among these accessions. Furthermore, 100 SSRs were randomly selected to detect the genetic diversity for 39 representative Saccharum accessions. A total of 320 alleles were generated using 100 polymorphic primers, with each marker ranging from two to seven alleles. The genetic diversity analysis revealed that these accessions were distributed in four main groups, including group I (14 S. spontaneum accessions), group II (two S. officinarum accessions), group III (18 modern sugarcane hybrid accessions), and group IV (five S. robustum accessions). Experimental verification supported the reliability of the SSR markers based on genome-wide predictions. The development of a large number of SSR markers based on wet experiments is valuable for genetic studies, including genetic linkage maps, comparative genome analysis, genome-wide association studies, and marker-assisted selection in Saccharum.

Fasim-LongTarget enables fast and accurate genome-wide lncRNA/DNA binding prediction

Many long noncoding RNAs (lncRNAs) can bind to DNA sequences proximal and distal to abundant genes, thereby regulating gene expression by recruiting epigenomic modification enzymes to binding sites. Because a lncRNA's target genes scattering in a genome have correlated functions, epigenetic analyses should often be genome-wide on both genome and transcriptome levels. Multiple tools have been developed for predicting lncRNA/DNA binding, but fast and accurate genome-wide prediction remains a challenge. Here we report Fasim-LongTarget (a revised version of LongTarget), compare its performance with TDF and LongTarget using the experimental data of the lncRNA MEG3, NEAT1, and MALAT1, and describe a case of genome-wide prediction. Fasim-LongTarget is as accurate as LongTarget and more accurate than TDF and is 200 times faster than LongTarget, making accurate genome-wide prediction feasible. The code is available on the Github website (https://github.com/LongTarget/Fasim-LongTarget), and the online service is available on the LongTarget website (https://lncRNA.smu.edu.cn).

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence

ObjectiveWe previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of...

A systematic genome-wide account of binding sites for the model transcription factor Gcn4

Sequence-specific DNA-binding transcription factors are central to gene regulation. They are often associated with consensus binding sites that predict far more genomic sites than are bound in vivo. One explanation is that most sites are blocked by nucleosomes, such that only sites in nucleosome-depleted regulatory regions are bound. We compared the binding of the yeast transcription factor Gcn4 in vivo using published ChIP-seq data (546 sites) and in vitro, using a modified SELEX method (“G-SELEX”), which utilizes short genomic DNA fragments to quantify binding at all sites. We confirm that Gcn4 binds strongly to an AP-1-like sequence (TGACTCA) and weakly to half-sites. However, Gcn4 binds only some of the 1078 exact matches to this sequence, even in vitro. We show that there are only 166 copies of the high-affinity RTGACTCAY site (exact match) in the yeast genome, all occupied in vivo, largely independently of whether they are located in nucleosome-depleted or nucleosomal regions. Generally, RTGACTCAR/YTGACTCAY sites are bound much more weakly and YTGACTCAR sites are unbound, with biological implications for determining induction levels. We conclude that, to a first approximation, Gcn4 binding can be predicted using the high-affinity site, without reference to chromatin structure. We propose that transcription factor binding sites should be defined more precisely using quantitative data, allowing more accurate genome-wide prediction of binding sites and greater insight into gene regulation.

Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

Detection of Melanogenesis and Anti-Apoptosis-Associated Melanoma Factors: Array CGH and PPI Mapping Integrating Study.

We investigated melanogenesis- and anti-apoptosis-related melanoma factors in melanoma cells (TXM1, TXM18, A375P, and A375SM). To find melanoma associated hub factor, high-throughput screening-based techniques integrating with bioinformatics were investigated. Array CGH analysis was conducted with a commercial system. Total genomic DNAs prepared individually from each cell line with control DNA were properly labeled with Cy3-dCTP and Cy5-dCTP and hybridizations and subsequently performed data treatment by the log2 green (G; test) to red (R; reference) fluorescence ratios (G/R). Gain or loss of copy number was judged by spots with log2-transformed ratios. PPI mapping analysis of detected candidate genes based on the array CGH results was conducted using the human interactome in the STRING database. Energy minimization and a short Molecular Dynamics (MD) simulation using the implicit solvation model in CHARMM were performed to analyze the interacting residues between YWHAZ and YWHAB. Three genes (BMP-4, BFGF, LEF-1) known to be involved in melanogenesis were found to lose chromosomal copy numbers, and Chr. 6q23.3 was lost in all tested cell lines. Ten hub genes (CTNNB1, PEX13, PEX14, PEX5, IFNG, EXOSC3, EXOSC1, EXOSC8, UBC, and PEX10) were predicted to be functional interaction factors in the network of the 6q23.3 locus. The apoptosis-associated genes E2F1, p50, BCL2L1, and BIRC7 gained, and FGF2 lost chromosomal copy numbers in the tested melanoma cell lines. YWHAB, which gained chromosomal copy numbers, was predicted to be the most important hub protein in melanoma cells. Molecular dynamics simulations for binding YWHAB and YWHAZ were conducted, and the complex was predicted to be energetically and structurally stable through its 3 hydrogen-bond patterns. The number of interacting residues is 27. Our study compares genome-wide screening interactomics predictions for melanoma factors and offers new information for understanding melanogenesis- and anti-apoptosis-associated mechanisms in melanoma. Especially, YWHAB was newly detected as a core factor in melanoma cells.

Genome-wide identification and expression profiling of duplicated flavonoid 3'-hydroxylase gene family in Carthamus tinctorius L.

Flavonoid 3′-hydroxylase (F3’H) enzyme is essential in determining the flavonoids B-ring hydroxylation pattern. It is mainly implicated in the biosynthetic pathway of cyaniding-based anthocyanins, flavonols, and flavan-3-ols. However, the evolution and regulatory mechanism of these important flavonoid hydroxylases have not been systematically investigated in safflower (Carthamus tinctorius L.). In this study, we identified 22 duplicatedCtF3'H-encoding genes from safflower through genome-wide prediction and conservation analysis. Phylogenetic analysis revealed the pattern of conservation and divergence of CtF3'Hs encoding proteins and their homologs from different plant species. The distribution of conserved protein motifs and cis-regulatory units suggested several structural components that could be crucial in deciphering the final function of CtF3'H proteins. Furthermore, the results of RNA-seq and qRT-PCR assay in different flowering tissues suggested differential expression level of CtF3’H genes during flower development. Based on the unique homology of CtF3’H5 with flavonoid 3’ hydroxylases from other plant species, further validation of CtF3’H5 was carried out. The transient expression of CtF3’H5 in onion epidermal cells implied that the subcellular localization of the fusion construct containing CtF3’H5 and GFP was predominantly detected in the plasma membrane. Similarly, the prokaryotic expression and western blot hybridization of CtF3’H5 demonstrated the detection of a stable 50.3kD target protein. However, more efforts are needed to further extend the functional validation of CtF3’H5 in safflower. This study provides a fundamental gateway for future functional studies and understanding the genetic evolution of F3'Hs in plants.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.