Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.

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.

Despite the critical role of bacterial cell walls in maintaining cell shapes, certain environmental stressors can induce the transition of many bacterial species into a wall-deficient state called L-form. Long-term induced Escherichia coli L-forms lose their rod shape and usually hold significant mutations that affect cell division and growth. Besides this, the genetic background of L-form bacteria is still poorly understood. In the present study, the genomes of two stable L-form strains of E. coli (NC-7 and LWF+) were sequenced and their gene mutation status was determined and compared with their parental strains. Comparative genomic analysis between two L-forms reveals both unique adaptions and common mutated genes, many of which belong to essential gene categories not involved in cell wall biosynthesis, indicating that L-form genetic adaptation impacts crucial metabolic pathways. Missense variants from L-forms and Lenski\'s long-term evolution experiment (LTEE) were analyzed in parallel using an optimized DeepSequence pipeline to investigate predicted mutation effects (α) on protein functions. We report that the two L-form strains analyzed display a frequency of 6-10% (0% for LTEE) in mutated essential genes where the missense variants have substantial impact on protein functions (α<0.5). This indicates the emergence of different survival strategies in L-forms through changes in essential genes during adaptions to cell wall deficiency. Collectively, our results shed light on the detailed genetic background of two E. coli L-forms and pave the way for further investigations of the gene functions in L-form bacterial models.

Bacteria with streamlined genomes, that harbor full functional genes for essential metabolic networks, are able to synthesize the desired products more effectively and thus have advantages as production platforms in industrial applications. To obtain streamlined chassis genomes, a large amount of effort has been made to reduce existing bacterial genomes. This work falls into two categories: rational and random reduction. The identification of essential gene sets and the emergence of various genome-deletion techniques have greatly promoted genome reduction in many bacteria over the past few decades. Some of the constructed genomes possessed desirable properties for industrial applications, such as: increased genome stability, transformation capacity, cell growth, and biomaterial productivity. The decreased growth and perturbations in physiological phenotype of some genome-reduced strains may limit their applications as optimized cell factories. This review presents an assessment of the advancements made to date in bacterial genome reduction to construct optimal chassis for synthetic biology, including: the identification of essential gene sets, the genome-deletion techniques, the properties and industrial applications of artificially streamlined genomes, the obstacles encountered in constructing reduced genomes, and the future perspectives.

It is documented that osteoarthritis can promote the progression of breast cancer (BC).
This study aims to search for the essential genes associated with breast cancer (BC) and osteoarthritis (OA), explore the relationship between epithelial-mesenchymal transition (EMT)- related genes and the two diseases, and identify the candidate drugs.
The genes related to both BC and OA were determined by text mining. Protein-protein Interaction (PPI) analysis was carried out, and as a result, the exported genes were found to be related to EMT. PPI and the correlation of mRNA of these genes were also analyzed. Different kinds of enrichment analyses were performed on these genes. A prognostic analysis was performed on these genes for examining their expression levels at different pathological stages, in different tissues, and in different immune cells. Drug-gene interaction database was employed for potential drug discovery.
A total number of 1422 genes were identified as common to BC and OA and 58 genes were found to be related to EMT. We found that HDAC2 and TGFBR1 were significantly poor in overall survival. High expression of HDAC2 plays a vital role in the increase of pathological stages. Four immune cells might play a role in this process. Fifty-seven drugs were identified that could potentially have therapeutic effects.
EMT may be one of the mechanisms by which OA affects BC. Using the drugs can have potential therapeutic effects, which may benefit patients with both diseases and broaden the indications for drug use.

Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

Studying genomic variation in rapidly evolving pathogens potentially enables identification of genes supporting their \"core biology\", being present, functional and expressed by all strains or \"flexible biology\", varying between strains. Genes supporting flexible biology may be considered to be \"accessory\", whilst the \"core\" gene set is likely to be important for common features of a pathogen species biology, including virulence on all host genotypes. The wheat-pathogenic fungus Zymoseptoria tritici represents one of the most rapidly evolving threats to global food security and was the focus of this study.
We constructed a pangenome of 18 European field isolates, with 12 also subjected to RNAseq transcription profiling during infection. Combining this data, we predicted a \"core\" gene set comprising 9807 sequences which were (1) present in all isolates, (2) lacking inactivating polymorphisms and (3) expressed by all isolates. A large accessory genome, consisting of 45% of the total genes, was also defined. We classified genetic and genomic polymorphism at both chromosomal and individual gene scales. Proteins required for essential functions including virulence had lower-than average sequence variability amongst core genes. Both core and accessory genomes encoded many small, secreted candidate effector proteins that likely interact with plant immunity. Viral vector-mediated transient in planta overexpression of 88 candidates failed to identify any which induced leaf necrosis characteristic of disease. However, functional complementation of a non-pathogenic deletion mutant lacking five core genes demonstrated that full virulence was restored by re-introduction of the single gene exhibiting least sequence polymorphism and highest expression.
These data support the combined use of pangenomics and transcriptomics for defining genes which represent core, and potentially exploitable, weaknesses in rapidly evolving pathogens.

Over 300 essential genes are predicted using transposon sequencing in the genome of Pseudomonas aeruginosa. However, methods for reverse genetic analysis of essential genes are scarce. To address this issue, we developed a three-step protocol consisting of integration of deletion plasmid, introduction of temperature-sensitive rescue plasmid, and excision of integrated-deletion plasmid to construct the plasmid-based temperature-sensitive allele of essential genes. Using PA0006 as an example, we showed that PA0006(Ts) exhibited wild-type cell morphology at permissive temperature but filamentous form at restrictive temperatures. We further showed that the glycerol-mannoheptose-bisphosphate phosphatase GmhB in Escherichia coli shared 32.4% identity with that of PA0006p and functionally complemented the defect of PA0006(Ts) at 42°C. SDS-PAGE and Western blotting indicated the presence and absence of the complete core lipopolysaccharide (LPS) and B-band O-antigen in PA0006(Ts) at 30 and 42°C, respectively. An isolated suppressor sup displayed wild-type-like cell morphology but no complete core LPS or O-antigen. Genome resequencing together with comparative transcriptomic profiling identified a candidate suppressor fructose-bisphosphate phosphatase in which the promoter harbored a SNP and the transcription level was not downregulated at 42°C compared to 30°C in sup. It was further validated that fbp overexpression suppressed the lethality of PA0006(Ts) at 42°C. Taken together, our results demonstrate that PA0006 plays a role in regulation of cell morphology and biosynthesis of core LPS. This three-step protocol for construction of conditional lethal allele in P. aeruginosa should be widely applicable for genetic analysis of other essential genes of interest, including analysis of bypass suppressibility. IMPORTANCE Microbial essential genes encode nondispensable function for cell growth and therefore are ideal targets for the development of new drugs. Essential genes are readily identified using transposon-sequencing technology at the genome scale. However, genetic analysis of essential genes of interest was hampered by limited methodologies. To address this issue, we developed a three-step protocol for construction of conditional allele of essential genes in the opportunistic pathogen Pseudomonas aeruginosa. Using PA0006 as an example, we demonstrated that the plasmid-based PA0006(Ts) mutant exhibited defects in regulation of cell morphology, formation of intact core LPS, and attachment of the O-antigen at restrictive temperatures but not at permissive temperatures. A suppressor of PA0006(Ts) was isolated through spontaneous mutations and showed restored cell morphology but not core oligosaccharide or O-antigen. This method should be widely applicable for phenotype and suppressibility analyses of other essential genes of interest in P. aeruginosa.

Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method\'s potential for application in identifying driver gene candidates for further biological experimental verification.

Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body\'s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.