G3: Genes|Genomes|Genetics最新文献

The ability of oocytes to maintain their quality is essential for successful reproduction. One critical aspect of oocyte quality and successful embryogenesis after fertilization is the proper regulation of the stores of maternal mRNA by RNA-binding proteins. Many RNA-binding proteins undergo regulated phase transitions during oogenesis, and alterations of the protein phase can disrupt its ability to regulate mRNA stability and translation. In Caenorhabditis elegans, regulators of RNA-binding protein phase transitions in maturing oocytes of young adult hermaphrodites remain poorly characterized. However, a few recently identified genes are also required for the clearance of damaged proteins during maturation, suggesting coordination between these processes. To explore this relationship and gain insight into the regulation of phase transitions, we conducted a targeted RNAi screen of genes required for removal of protein aggregates in maturing oocytes. Here, we identify 6 novel regulators of phase transitions of the KH-domain protein MEX-3. We present strong evidence that the regulation of MEX-3 phase transitions in the oocyte overlaps with, but is distinct from, the regulatory network of protein aggregate clearance.

Most genes are transcribed from multiple transcription start sites (TSSs), also known as alternative TSSs, which are highly regulated and can lead to various gene regulatory outcomes, including changes in translation efficiency and protein isoform expression. Transcription factors and chromatin regulators control alternative TSS selection. DNA supercoiling affects multiple aspects of transcription, including transcription initiation; however, its regulatory effect on genes with multiple TSSs is not known. Here, we investigated how depletion of topoisomerases, which resolve DNA supercoiling events, impacts alternative TSS usage in Saccharomyces cerevisiae. We depleted topoisomerases (Top1 and Top2) during early meiosis, where alternative TSS usage is prevalent, and applied an improved TSS sequencing protocol. We show that supercoiling affects alternative TSS usage at almost 600 genes. Increased alternative and aberrant TSS usage was observed near and within open reading frames, likely resulting from transcription-induced supercoiling originating from upstream alternative TSSs. Top1/Top2 co-depletion most strongly affected genes with highly used, widely spaced alternative TSSs. Our correlative analyses support a model where DNA supercoiling release during transcription is critical for correct TSS selection.

Yellow wood sorrel (Oxalis stricta L.), also known as sourgrass, juicy fruit, or sheep weed, is a member of the Oxalidaceae family. Yellow wood sorrel is commonly considered a weed and while native to North America, it is distributed across Europe, Asia, and Africa. To date, only two other genomes from the Oxalidaceae family have been published, star fruit (Averrhoa carambola L.) and Oxalis articulata Savingy. Here, we present a chromosome-scale assembly for O. stricta, revealing its allotetraploid nature and synteny within its two subgenomes as well as synteny with A. carambola and O. articulata. Using Oxford Nanopore Technologies long-read sequences coupled with chromatin capture sequencing, we generated a 436 Mb chromosome-scale assembly of O. stricta with a scaffold N50 length of 36.2 Mb that is anchored to 12 chromosomes across the two subgenomes. Assessment of the final genome assembly using the Long Terminal Repeat Assembly Index yielded a score of 13.12 and assessment of Benchmarking Universal Single Copy Orthologs revealed 99.6% complete orthologs; both metrics are suggestive of a high-quality reference genome. Total repetitive sequence content in the O. stricta genome was 39.7% with retroelements being the largest class of transposable elements. Annotation of protein-coding genes yielded 61,550 high confidence genes encoding 115,089 gene models. Synteny between the two O. stricta subgenomes was present in 93 syntenic blocks containing 40,750 genes, of which, 76.47% were present in 1:1 syntenic relationships between the two subgenomes. The availability of an annotated chromosome-scale high quality genome assembly for O. stricta will provide a launching point to understand the high fecundity of this weed and provide further foundation for comparative genomics within the Oxalidaceae.

The pileus serves as a primary determinant of market grade and commercial value in Lentinula edodes. To elucidate the molecular mechanisms governing pileus development, we conducted comparative transcriptome analysis via RNA sequencing across three distinct developmental stages: Early button stage, Young fruiting body stage, and mature fruiting body stage. Gene expression profiling revealed a substantial number of differentially expressed genes (DEGs) between stages, with 283 conserved DEGs spanning the entire developmental continuum. Systematic mining of these conserved DEGs identified three candidate regulatory genes encoding: Alpha-amylase, Heat shock protein 70 (HSP70), Phosphatidylserine decarboxylase. Quantitative PCR validation confirmed the accuracy of both RNA-Seq data and DEG identification. Enzymatic activity assays demonstrated significant stage-specific variations in: Antioxidant enzyme activities, Membrane lipid peroxidation levels. This study provides valuable insights into the molecular framework underlying pileus morphogenesis in Lentinula edodes.

Spontaneous mutations display biases in their relative frequencies with important consequences for genome structure and composition. While laboratory studies have provided important insights into the spontaneous mutation spectrum, laboratory environments for optimal growth may engender biases that are not representative of natural populations. We analyzed the mitochondrial genomes of 1,524 Caenorhabditis elegans natural isolates comprising 531 unique isotypes to investigate mtDNA polymorphism in the wild. Ancestral reconstruction was used to polarize 2,464 variants (88 indels, 2,376 SNPs) and the results were compared to mutations identified in experimental lines under relaxed selection. MtDNA variant distribution in natural isolates is strongly dependent on site-degeneracy in a manner consistent with purifying selection. There is significant variation in the proportion of synonymous and nonsynonymous polymorphism between genes. Specifically, ETC complex I genes are enriched for nonsynonymous polymorphism. The probability of synonymous mutation is higher at sites with neighboring G/C nucleotides and the per gene synonymous polymorphism is negatively correlated with A+T-content at the 1st and 2nd codon positions. Furthermore, the 5' and 3' ends of genes have both higher A+T-content and less synonymous polymorphism than central regions. There is evidence of natural selection for preferred codons. We identify the first cases of large heteroplasmic mtDNA structural variants in C. elegans natural isolates, comprising deletions and duplications. Although some patterns of mtDNA mutational bias are similar between laboratory and natural populations, there exist significant differences. In particular, G/C → T/A transversions typically associated with oxidative damage and 8-oxoguanine are strikingly rarer at four-fold degenerate sites in natural populations relative to laboratory populations suggesting that the latter are more prone to mtDNA oxidative damage. Hence, mutational spectra observed in laboratory strains may differ in important aspects from those of natural populations.

Applying antimicrobial compounds derived from microorganisms for plant disease management is one of the objectives of sustainable agriculture. The genus Talaromyces is known for its species' ability to produce a diverse group of antimicrobial compounds. For example, T. trachyspermus has been reported to produce secondary metabolites, "cell wall degrading enzymes", and plant growth-promoting factors. Identification of novel promising metabolites and enzymes from T. trachyspermus is still in its infancy. Also, there is a lack of information about the genomic resources for its secondary metabolites and hydrolytic enzymes. Therefore, this study aimed to analyze the genome of a biocontrol isolate of this species to investigate its biocontrol mechanisms at the genomic level, focusing on secondary metabolites and "cell wall degrading enzymes". The whole genome of T. trachyspermus isolate IRAN 3054C, obtained from necrotic Orobanch ramosa stems in Iran with biocontrol ability, was sequenced using the Illumina platform. We performed both de novo and resequencing analyses of the genome, obtaining a 31.3 Mb assembly. The abundance of protein groups associated with biocontrol activities was assessed in the studied genome. Fungismash was used to detect and annotate secondary metabolites. The analysis revealed the presence of several secondary metabolite biosynthesis gene clusters (BGCs), with a high frequency of polyketide synthases (T1PKSs) and nonribosomal peptide synthetases (NRPSs), which are known to produce bioactive compounds with antimicrobial properties. Among the identified putative secondary metabolites, Fusarin, YWA1, Dimethylcoprogen, and Squalestatin S1 exhibited the highest similarity to known compounds. Furthermore, sequences similar to Phyllostictine A/B indicate putative potential herbicidal properties. The genome also had domains for enzymes involved in phosphate solubilization, siderophore production, and fungal cell wall degradation, which are essential for biocontrol and plant growth promotion. Our findings highlight the genomic richness of T. trachyspermus IRAN 3054C for biocontrol. Further metabolomics studies are needed to validate the actual production of these secondary metabolites and explore their functional roles in biocontrol.

This study investigates the integration of Bayesian networks (BN) and structural equation models (SEM) to explore genomic relationships among nine traits related to productivity, defense, and climate-adaptability in an interior lodgepole pine breeding program. Data from 392 open-pollinated trees, genotyped with 25,099 SNP markers, were analyzed. The traditional multi-trait model (MTM) served as a benchmark for comparing SEM in estimating genetic (co)variance components, genetic correlations, breeding value (BV) predictions, and predictive ability, using both pedigree- (ABLUP) and genomic-based (GBLUP) individual-tree mixed models. The Hill-Climbing algorithm identified 12 significant causal structures (λ) among traits. Strong positive causal effects included tree height (HT) on wood density (WD) (λHT→WD = 0.413) and on stable carbon isotope ratio (C13) (λHT→C13 = 0.565), and limonene (LIMO) on carbon assimilation rate (CAR) (λLIMO→CAR = 0.368). The most influential causal relationship was HT → C13, followed by resistance to western gall rust (WGR) → CAR, CAR → LIMO, and WGR → C13. SEM incorporated these relationships, capturing both direct and indirect effects. Compared with MTM, SEM yielded lower residual variances, higher additive variances, and higher heritability estimates for all traits. The λ values from SEM correlated strongly with genetic correlations (0.932), with similarly high correlations between models (0.929), though SEM produced lower posterior mean correlations. BV correlations between models were high (ABLUP > 0.82, GBLUP > 0.84), but some reranking occurred among the top 39-trees (ABLUP > 0.71, GBLUP > 0.42). ABLUP and GBLUP-SEM models outperformed MTM in predictive ability, with mean gains of 6.62% and 6.03%, mainly for conditioned traits. BN-SEM enhances understanding of trait networks, improving genomic evaluations and breeding strategies in forest trees.

Ssy1 in Saccharomyces cerevisiae is an amino acid receptor evolved from amino acid transporters. It is situated in the plasma membrane in the SPS complex, together with the WD40-repeat protein Ptr3 and the endoprotease Ssy5. Binding of extracellular amino acids to Ssy1 triggers liberation of the catalytic domain of Ssy5, which removes an inhibitory domain from the transcription factor Stp1, freeing it to activate genes encoding amino acid transporters. We mapped 7 constitutively signaling and hyper-responsive SSY1 mutations onto AlphaFold- and Phyre2-based 3D-models of Ssy1 to inform conformational steps involved in signaling. The predictions suggest a model in which an occluded, inward-facing conformation of Ssy1 leads to signaling. The mutations suggest a hinge in TM12 which, combined with a C-terminal 'latch', offers a mechanism for signaling. AlphaFold 3 modeling suggests that conserved sequence boxes in the N-terminal cytoplasmic domain of Ssy1 serve as interaction faces for binding of Ptr3, Ssy5 and casein kinases Yck1 and Yck2 (Yck). In addition, interaction faces between Ptr3 and Ssy5 were predicted. Antagonism between phosphorylation and dephosphorylation of Ptr3 and Ssy5 by Yck and Protein Phosphatase 2A (PP2A) is key in signaling. We found Yck phosphorylation motifs as well as binding motifs for regulatory subunit Rts1 of PP2A, in both Ptr3 and Ssy5. These motifs, together with sites of PTR3 and SSY5 gain-of-function mutations, were mapped onto AlphaFold models of Ptr3 and Ssy5. The results constitute a basis for predicting novel aspects of phosphorylation in the signaling mechanism.

Genomic prediction (GP) has catalyzed increased rates of genetic gain in animal and plant breeding. Recently, deep learning (DL) has been explored to increase GP accuracy by incorporating diverse data types and learning complex, non-linear patterns in datasets. However, DL consistently fails to significantly improvement prediction accuracy over gold standard genomic BLUP (gBLUP) models. In this study, we first review the theory behind neural networks and reproducing kernel Hilbert spaces (RKHS) regression to contextualize three claimed benefits of DL over linear models: incorporation of diverse data types, avoidance of feature engineering, and universal approximation behavior. We also propose a taxonomy of prediction problems so that model comparisons do not confound differences in the predictive skill of different model classes with differences in the input data. Second, we leverage a maize multi-environment trial dataset to train DL and RKHS models that implicitly capture non-linear patterns between genomic, soil, weather, and management inputs and grain yield. The results demonstrate that feature engineering using principal components of SNPs generally degrades prediction accuracy across model classes. Furthermore, DL models persistently fail to outperform RKHS models across prediction problems. Finally, we evaluate the theoretical critiques with the empirical results, confirming the theoretical arguments. Nevertheless, a small portion of the possible DL model space has been explored, leaving open the possibility of DL making significant contributions to GP problems through additional aspects not considered here. We conclude by suggesting several avenues for further theoretical and practical research, including the resolution of several disciplinary differences.

Bumble bees (Bombus) are considered to be essential pollinators of a wide range of flowering plants, within both agricultural and natural ecosystems. Bombus pauloensis and Bombus pullatus are two closely related Neotropical species with a wide altitudinal and latitudinal distribution that belong to the Thoracobombus genus. To the best of our knowledge, there is no genome assembly available for any species of Neotropical Bombus. Therefore, the goal of this study is to produce high-quality genomes of B. pauloensis and B. pullatus. In order to achieve this objective, we obtained long-read sequences using the Oxford Nanopore Technologies platform. We then proceeded to assemble the genomes and annotate these assemblies. As a result, we obtained assemblies of ∼240Mb represented in 72 contigs with an N50 of ∼ 9.08Mb for Bombus pullatus and ∼239Mb represented in 66 contigs with an N50 of ∼9Mb for Bombus pauloensis. The completeness evaluated by compleasm return a score >99% for both species. It is hoped that these genomes will facilitate a more profound comprehension of the biology of Neotropical bumblebees.