Genomic identification and functional analysis of essential genes in Caenorhabditis elegans

Impact of Molecular Weight Variations in Dendrobium officinale Polysaccharides on Antioxidant Activity and Anti-Obesity in Caenorhabditis elegans

This research investigates the impact of Dendrobium officinale polysaccharides (DOP) with different molecular weights on antioxidant effects, lifespan enhancement, and obesity reduction, utilizing both in vitro analyses and the Caenorhabditis elegans (C. elegans) model. Through a series of experiments-ranging from the extraction and modification of polysaccharides, Gel Permeation Chromatography (GPC), and analysis of composition to the evaluation of antioxidant capabilities, this study thoroughly examines DOP and its derivatives (DOP5, DOP15, DOP25) produced via H2O2-Fe2+ degradation. The results reveal a direct relationship between the molecular weight of polysaccharides and their bioactivity. Notably, DOP5, with its intermediate molecular weight, demonstrated superior antioxidant properties, significantly extended the lifespan, and improved the health of C. elegans. Furthermore, DOP15 appeared to regulate lipid metabolism by affecting crucial lipid metabolism genes, including fat-4, fat-5, fat-6, sbp-1, and acs-2. These findings highlight the potential application of DOP derivatives as natural antioxidants and agents against obesity, contributing to the development of functional foods and dietary supplements.

Essential genes identification model based on sequence feature map and graph convolutional neural network

BACKGROUND

Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes.

RESULTS

In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training.

CONCLUSIONS

Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome

Protein products of essential genes, indispensable for organismal survival, are highly conserved and bring about fundamental functions. Interestingly, proteins that contain amino acid homorepeats that tend to evolve rapidly are enriched in eukaryotic essentialomes. Why are proteins with hypermutable homorepeats enriched in conserved and functionally vital essential proteins? We solve this functional versus evolutionary paradox by demonstrating that human essential proteins with homorepeats bring about crosstalk across biological processes through high interactability and have distinct regulatory functions affecting expansive global regulation. Importantly, essential proteins with homorepeats rapidly diverge with the amino acid substitutions frequently affecting functional sites, likely facilitating rapid adaptability. Strikingly, essential proteins with homorepeats influence human-specific embryonic and brain development, implying that the presence of homorepeats could contribute to the emergence of human-specific processes. Thus, we propose that homorepeat-containing essential proteins affecting species-specific traits can be potential intervention targets across pathologies, including cancers and neurological disorders.

Antioxidant and Antiaging Activity of Fermented Coix Seed Polysaccharides on Caenorhabditis elegans

Aging is closely related to many diseases and is a long-term challenge that humans face. The oxidative damage caused by the imbalance of free radicals is an important factor in aging. In this study, we investigate the antioxidant and antiaging activities of fermented coix seed polysaccharides (FCSPs) via in vitro and in vivo experiments. The FCSPs were extracted by fermenting coix seed with Saccharomyces cerevisiae for 48 h and utilizing water-extracted coix seed polysaccharides (WCSPs) as a control. Their antiaging activity and mechanism were evaluated based on the antiaging model organism Caenorhabditis elegans (C. elegans). The results showed that the molecular weight of the FCSPs extracted by fermentation was smaller than that of the WCSPs, making them more easily absorbed and utilized. At a concentration of 5 g/L, the FCSPs' capacity to scavenge the DPPH·, ABTS⁺·, OH·, and O₂^-· radicals was greater than the WCSPs' capacity by 10.09%, 14.40%, 49.93%, and 12.86%, respectively. Moreover, C. elegans treated with FCSPs exhibited higher antioxidant enzyme activities and a lower accumulation of malonaldehyde. By inhibiting the expression of the pro-aging genes daf-2 and age-1, and upregulating the expression of the antiaging genes daf-16, sod-3, skn-1, and gcs-1 in the insulin/insulin-like growth factor-1 (IIS) signaling pathway, the FCSPs could effectively enhance stress tolerance and delay C. elegans aging. The lifespan of C. elegans in the FCSPs group was 5.91% higher than that of the WCSPs group. In conclusion, FCSPs exert better antioxidant and antiaging effects than WCSPs, which can act as a potential functional ingredient or supplement in food.

Identification of Essential Genes in Caenorhabditis elegans with Lethal Mutations Maintained by Genetic Balancers

Genetic balancer systems, which allow effective capture and maintenance of lethal mutations stably, play an important role in identifying essential genes. Whole-genome sequencing (WGS) followed by bioinformatics analysis, combined with genetic mapping data analysis, allows for an efficient and economical means of identifying genomic mutations in essential genes. Using this approach, we successfully identified 104 essential genes on ChrI, ChrIII, and ChrV in C. elegans. In this report, we described a protocol that sequences the genome of prebalanced Caenorhabditis elegans (C. elegans) strains to carry lethal mutations and identifies candidate causal mutations and candidate essential genes using a robust bioinformatics procedure.

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools

Essential genes refer to genes that are required by an organism to survive under specific conditions. Studies of the minimal-gene-set for bacteria have elucidated fundamental cellular processes that sustain life. The past five years have seen a significant progress in identifying human essential genes, primarily due to the successful use of CRISPR/Cas9 in various types of human cells. DEG 15, a new release of the Database of Essential Genes (www.essentialgene.org), has provided major advancements, compared to DEG 10. Specifically, the number of eukaryotic essential genes has increased by more than fourfold, and that of prokaryotic ones has more than doubled. Of note, the human essential-gene number has increased by more than tenfold. Moreover, we have developed built-in analysis modules by which users can perform various analyses, such as essential-gene distributions between bacterial leading and lagging strands, sub-cellular localization distribution, enrichment analysis of gene ontology and KEGG pathways, and generation of Venn diagrams to compare and contrast gene sets between experiments. Additionally, the database offers customizable BLAST tools for performing species- and experiment-specific BLAST searches. Therefore, DEG comprehensively harbors updated human-curated essential-gene records among prokaryotes and eukaryotes with built-in tools to enhance essential-gene analysis.

Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning

Defining genes that are essential for life has major implications for understanding critical biological processes and mechanisms. Although essential genes have been identified and characterised experimentally using functional genomic tools, it is challenging to predict with confidence such genes from molecular and phenomic data sets using computational methods. Using extensive data sets available for the model organism Caenorhabditis elegans, we constructed here a machine-learning (ML)-based workflow for the prediction of essential genes on a genome-wide scale. We identified strong predictors for such genes and showed that trained ML models consistently achieve highly-accurate classifications. Complementary analyses revealed an association between essential genes and chromosomal location. Our findings reveal that essential genes in C. elegans tend to be located in or near the centre of autosomal chromosomes; are positively correlated with low single nucleotide polymorphim (SNP) densities and epigenetic markers in promoter regions; are involved in protein and nucleotide processing; are transcribed in most cells; are enriched in reproductive tissues or are targets for small RNAs bound to the argonaut CSR-1. Based on these results, we hypothesise an interplay between epigenetic markers and small RNA pathways in the germline, with transcription-based memory; this hypothesis warrants testing. From a technical perspective, further work is needed to evaluate whether the present ML-based approach will be applicable to other metazoans (including Drosophila melanogaster) for which comprehensive data sets (i.e. genomic, transcriptomic, proteomic, variomic, epigenetic and phenomic) are available.

Kettin, the large actin-binding protein with multiple immunoglobulin domains, is essential for sarcomeric actin assembly and larval development in Caenorhabditis elegans

Among many essential genes in the nematode Caenorhabditis elegans, let-330 is located on the left arm of chromosome V and was identified as the largest target of a mutagen in this region. However, let-330 gene has not been characterized at the molecular level. Here, we report that two sequenced let-330 alleles are nonsense mutations of ketn-1, a previously characterized gene encoding kettin. Kettin is a large actin-binding protein of 472 kDa with 31 immunoglobulin domains and is expressed in muscle cells in C. elegans. let-330/ketn-1 mutants are homozygous lethal at the first larval stage with mild defects in body elongation. These mutants have severe defects in sarcomeric actin and myosin assembly in striated muscle. However, α-actinin and vinculin, which are components of the dense bodies anchoring actin to the membranes, were not significantly disorganized by let-330/ketn-1 mutation. Kettin localizes to embryonic myofibrils before α-actinin is expressed, and α-actinin deficiency does not affect kettin localization in larval muscle. Depletion of vinculin minimally affects kettin localization but significantly reduces colocalization of actin with kettin in embryonic muscle cells. These results indicate that kettin is an essential protein for sarcomeric assembly of actin filaments in muscle cells.