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Yu et al. constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. He et al. proposed an NRLMFMDA (neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimental validation of disease-miRNA association. Liu et al. classified muscle-invasive bladder cancer into two conservative subtypes using miRNA, mRNA, and lncRNA expression data; investigated subtype-related biological pathways; and evaluated the subtype classification performance using machine learning methods. [Extracted from the article]

Background: Approximately 50% of cataracts are associated with genetic factors. Genetic etiology and molecular mechanisms based on gene research increase the understanding of cataracts and provide direction for diagnosis and intervention. In the present study, SCIE papers related to the modeling of cataract gene research from 2010–2019 were evaluated and qualitative and quantitative analyses with modeling performed. Methods: The SCIE database was searched on July 6, 2021 for cataract gene publications and relevant papers published since 2010 were considered for review. Subsequently, 1,904 SCIE papers associated with cataract genes from 2010–2019 were analyzed using a bibliometric method. The publication, country, institution, journal, references, knowledgebase, keywords, and research hotspots of the papers were analyzed using an online analysis platform of literature metrology, bibliographic item co-occurrence matrix builder (BICOMB), CiteSpace V, and VOS viewer analysis tool. Results: 78 countries published the related articles, and the United States ranks of America had the most publications. Two thousand seven hundred and eighty three institutions contributed to the related publications. Fudan University had the most publications. The reference clusters of SCI papers were clustered into six categories, namely, causing congenital cataract-microcornea syndrome, functional snp, cataractous lenses, a1 mutation, foxe3 mutation, cell adhesion gene pvrl3, nid1 gene. The key words representing the research frontiers were cerebrotendinous xanthomatosis (2017-2019), oxidative stress (2017–2019). Conclusion: This study provided a systematic, objective and comprehensive analysis of the literature related to gene research of cataract. Moreover, this study demonstrated the current hotspots and the future trends in the field of gene research of cataract. This review will help ophthalmologist to discern the dynamic evolution of cataract gene research, as well as highlight areas for future research. [ABSTRACT FROM AUTHOR]

In the early 1900s, mutation breeding to select varieties with desirable traits using spontaneous mutation was actively conducted around the world, including Japan. In rice, the number of fixed mutations per generation was estimated to be 1.38-2.25. Although this low mutation rate was a major problem for breeding in those days, in the modern era with the development of next-generation sequencing (NGS) technology, it was conversely considered to be an advantage for efficient gene identification. In this paper, we proposed an in silico approach using NGS to compare the whole genome sequence of a spontaneous mutant with that of a closely related strain with a nearly identical genome, to find polymorphisms that differ between them, and to identify the causal gene by predicting the functional variation of the gene caused by the polymorphism. Using this approach, we found four causal genes for the dwarf mutation, the round shape grain mutation and the awnless mutation. Three of these genes were the same as those previously reported, but one was a novel gene involved in awn formation. The novel gene was isolated from Bozu-Aikoku, a mutant of Aikoku with the awnless trait, in which nine polymorphisms were predicted to alter gene function by their whole-genome comparison. Based on the information on gene function and tissue-specific expression patterns of these candidate genes, Os03g0115700/LOC_Os03g02460, annotated as a shortchain dehydrogenase/reductase SDR family protein, is most likely to be involved in the awnless mutation. Indeed, complementation tests by transformation showed that it is involved in awn formation. Thus, this method is an effective way to accelerate genome breeding of various crop species by enabling the identification of useful genes that can be used for crop breeding with minimal effort for NGS analysis. [ABSTRACT FROM AUTHOR]

Background: In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology. Methods: This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA. Results: Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES). Conclusion: We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice. [ABSTRACT FROM AUTHOR]

Background: Today, the blaNDM gene is widely distributed on several plasmids from a variety of Gram-negative bacteria, primarily in transposons and gene cassettes within their multidrug-resistant (MDR) regions. This has led to the global dissemination of the blaNDM gene. Methods: The determination of class A beta-lactamase, class B and D carbapenemases was performed according to the recommendations of the Clinical and Laboratory Standards Institute (CLSI). Antimicrobial susceptibility testing was performed using both the BioMerieux VITEK2 system and antibiotic paper diffusion methods. Plasmid transfer was then evaluated by conjugation experiments and plasmid electroporation assays. To comprehensively analyze the complete genome of K. pneumoniae strain F11 and to investigate the presence of mobile genetic elements associated with antibiotic resistance and virulence genes, Nanopore and Illumina sequencing platforms were used, and bioinformatics methods were applied to analyze the obtained data. Results: Our findings revealed that K. pneumoniae strain F11 carried class A betalactamase and classes B+D carbapenemases, and exhibited resistance to commonly used antibiotics, particularly tigecycline and ceftazidime/avibactam, due to the presence of relevant resistance genes. Plasmid transfer assays demonstrated successful recovery of plasmids pA_F11 and pB_F11, with average conjugation frequencies of 2.91x10-4 and 1.56x10-4, respectively. However, plasmids pC_F11 and pD_F11 failed in both conjugation and electroporation experiments. The MDR region of plasmid pA_F11 contained rare In1765, TnAs2, and TnAs3 elements. The MDR2 region of plasmid pB_F11 functioned as a mobile genetic "island" and lacked the blaNDM-1 gene, serving as a "bridge" connecting the early composite structure of bleMBL and blaNDM-1 to the recent composite structure. Additionally, the MDR1 region of plasmid pB_F11 comprised In27, TnAs1, TnAs3, and Tn2; and plasmid pC_F11 harbored the recent composite structure of bleMBL and blaNDM-1 within Tn3000 which partially contained partial Tn125.Conclusion: This study demonstrated that complex combinations of transposons and integron overlaps, along with the synergistic effects of different drug resistance and virulence genes, led to a lack of effective therapeutic agents for strain F11, therefore its dissemination and prevalence should be strictly controlled. [ABSTRACT FROM AUTHOR]

Sesame (Sesamum indicum L.) is an ancient diploid oilseed crop with high oil content, quality protein, and antioxidant characteristics that is produced in many countries worldwide. The genes, QTLs, and genetic resources of sesame are utilized by sesame researchers and growers. Researchers have identified the many useful traits of this crop, which are available on different platforms. The genes, genotypes, QTLs, and other genetic diversity data of sesame have been collected and stored in more than nine genomic resources, and five sesame crop marker databases are available online. However, data on phenotypic and genotypic variability, which would contribute to sesame improvements, are limited and not yet accessible. The present study comprehensively reviewed more than 110 original published research papers and scientifically incorporated the results. The candidate genes, genotypes, andQTLs of significantly important traits of sesame were identified. Genetic resources related to grain yield and yield component traits, oil content and quality, drought tolerance, salt tolerance, waterlogging resistance, disease resistance, mineral nutrient, capsule shattering resistance, and other agronomic important traits of sesame were studied. Numerous candidate genotypes, genes, QTLs, and alleles associated with those traits were summarized and discovered. The chromosome regions and linkage groups, maps associated with the best traits, and candidate genes were also included. The variability presented in this paper combined with sesame genetic information will help inform further sesame improvement. [ABSTRACT FROM AUTHOR]

The incidence of esophageal cancer has obvious genetic susceptibility. Identifying esophageal cancer-related genes plays a huge role in the prevention and treatment of esophageal cancer. Through various sequencing methods, researchers have found only a small number of genes associated with esophageal cancer. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. This method fuses graph convolutional network and logical matrix factorization to effectively identify esophageal cancer-related genes through the association between genes. We call this method GCNLMF which achieved AUC as 0.927 and AUPR as 0.86. Compared with other five methods, GCNLMF performed best. We conducted a case study of the top three predicted genes. Although the association of these three genes with esophageal cancer has not been reported in the database, studies by other reseachers have shown that these three genes are significantly associated with esophageal cancer, which illustrates the accuracy of the prediction results of GCNLMF. [ABSTRACT FROM AUTHOR]

The Schlafen5(SLFN5)gene belongs to the third group of the Schlafen protein family. As a tumor suppressor gene, SLFN5 plays a pivotal role in inhibiting tumor growth, orchestrating cell cycle regulation, and modulating the extent of cancer cell infiltration and metastasis in various malignancies. However, the high expression of SLFN 5 in some tumors was positively correlated with lymph node metastasis, tumor stage, and tumor grade. This article endeavors to elucidate the reciprocal relationship between the SLFN5 gene and malignant tumors, thereby enhancing our comprehension of the intricate mechanisms underlying the SLFN5 gene and its implications for the progression, invasive potential, and metastatic behavior of malignant tumors. At the same time, this paper summarizes the basis of SLFN 5 as a new biomarker of tumor diagnosis and prognosis, and provides new ideas for the target treatment of tumor. [ABSTRACT FROM AUTHOR]

Biotic stress is one of the major threats to stable rice production. Climate change affects the shifting of pest outbreaks in time and space. Genetic improvement of biotic stress resistance in rice is a cost-effective and environment-friendly way to control diseases and pests compared to other methods such as chemical spraying. Fast deployment of the available and suitable genes/alleles in local elite varieties through marker-assisted selection (MAS) is crucial for stable high-yield rice production. In this review, we focused on consolidating all the available cloned genes/alleles conferring resistance against rice pathogens (virus, bacteria, and fungus) and insect pests, the corresponding donor materials, and the DNA markers linked to the identified genes. To date, 48 genes (independent loci) have been cloned for only major biotic stresses: seven genes for brown planthopper (BPH), 23 for blast, 13 for bacterial blight, and five for viruses. Physical locations of the 48 genes were graphically mapped on the 12 rice chromosomes so that breeders can easily find the locations of the target genes and distances among all the biotic stress resistance genes and any other target trait genes. For efficient use of the cloned genes, we collected all the publically available DNA markers (~500 markers) linked to the identified genes. In case of no available cloned genes yet for the other biotic stresses, we provided brief information such as donor germplasm, quantitative trait loci (QTLs), and the related papers. All the information described in this review can contribute to the fast genetic improvement of biotic stress resistance in rice for stable high-yield rice production. [ABSTRACT FROM AUTHOR]

To test the equality of several independent multinomial distributions, the chi-square test for count data is applied. The existing test can be applied when complete information about the data is available. The complex process, such as DNA count, the existing test under classical statistics may mislead. To overcome the issue, the modification of the chi-square test for multinomial distribution under neutrosophic statistics is presented in this paper. The modified form of the chi-square test statistic under indeterminacy/uncertainty is presented and applied using the DNA count data. From the DNA count data analysis, simulation, and comparative studies, the proposed test is found to be informative, springy, and good as compared with the existing tests. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an autoimmune disease for which it is difficult to find exact disease-related genes. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of multiple sclerosis. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and pay little attention to the global topological information of the whole protein-protein-interaction (PPI) network. Besides, network representation learning (NRL) has attracted a huge amount of attention in the area of network analysis because of its promising performance in node representation and many downstream tasks. In this paper, we try to introduce NRL into the task of disease-related gene prediction and propose a novel framework for identifying the disease-related genes multiple sclerosis. The proposed framework contains three main steps: capturing the topological structure of the PPI network using NRL-based methods, encoding learned features into low-dimensional space using a stacked autoencoder, and training a support vector machine (SVM) classifier to predict disease-related genes. Compared with three state-of-the-art algorithms, our proposed framework shows superior performance on the task of predicting disease-related genes of multiple sclerosis. [ABSTRACT FROM AUTHOR]

Wild rice (Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation. [ABSTRACT FROM AUTHOR]

Honeybee is a crucial pollinator in nature, and plays an indispensable role in both agricultural production and scientific research. In recent decades, honeybee was challenged with health problems by biotic and abiotic stresses. As a key ecological factor, temperature has been proved to have an impact on the survival and production efficiency of honeybees. Previous studies have demonstrated that low temperature stress can affect honeybee pupation and shorten adult longevity. However, the molecular mechanism underlying the effects of low temperatures on honeybee growth and development during their developmental period remain poorly understood. In this paper, the weighted gene co-expression analysis (WGCNA) was employed to explore the molecular mechanisms underpinnings of honeybees’ respond to low temperatures (20°C) during four distinct developmental stages: large-larvae, prepupae, early-pupae and mid-pupae. Through an extensive transcriptome analysis, thirteen gene co-expression modules were identified and analyzed in relation to honeybee development and stress responses. The darkorange module was found to be associated with low temperature stress, with its genes primarily involved in autophagy-animal, endocytosis and MAPK signaling pathways. Four hub genes were identified within this module, namely, loc726497, loc409791, loc410923, and loc550857, which may contribute to honeybee resistance to low temperature and provide insight into the underlying mechanism. The gene expression patterns of grey60 and black modules were found to correspond to the developmental stages of prepupae and early-pupae, respectively, with the hub genes loc409494, loc725756, loc552457, loc726158, Ip3k and Lcch3 in grey60 module likely involved in brain development, and the hub genes loc410555 in black module potentially related to exoskeleton development. The brown module genes exhibited a distinct pattern of overexpression in mid-pupae specimens, with genes primarily enriched in oxidative phosphorylation, citrate cycle and other pathways, which may be related to the formation of bee flying muscle. No related gene expression module was found for mature larvae stage. These findings provide valuable insights into the developmental process of honeybees at molecular level during the capped brood stage. [ABSTRACT FROM AUTHOR]

Pharmacogenomics (PGx) is considered an emergent field in developing countries. Research on PGx in the Latin American and the Caribbean (LAC) region remains scarce, with limited information in some populations. Thus, extrapolations are complicated, especially in mixed populations. In this paper, we reviewed and analyzed pharmacogenomic knowledge among the LAC scientific and clinical community and examined barriers to clinical application. We performed a search for publications and clinical trials in the field worldwide and evaluated the contribution of LAC. Next, we conducted a regional structured survey that evaluated a list of 14 potential barriers to the clinical implementation of biomarkers based on their importance. In addition, a paired list of 54 genes/drugs was analyzed to determine an association between biomarkers and response to genomic medicine. This survey was compared to a previous survey performed in 2014 to assess progress in the region. The search results indicated that Latin American and Caribbean countries have contributed 3.44% of the total publications and 2.45% of the PGx-related clinical trials worldwide thus far. A total of 106 professionals from 17 countries answered the survey. Six major groups of barriers were identified. Despite the region's continuous efforts in the last decade, the primary barrier to PGx implementation in LAC remains the same, the "need for guidelines, processes, and protocols for the clinical application of pharmacogenetics/pharmacogenomics". Cost-effectiveness issues are considered critical factors in the region. Items related to the reluctance of clinicians are currently less relevant. Based on the survey results, the highest ranked (96%-99%) gene/drug pairs perceived as important were CYP2D6/tamoxifen, CYP3A5/tacrolimus, CYP2D6/opioids, DPYD/fluoropyrimidines, TMPT/thiopurines, CYP2D6/tricyclic antidepressants, CYP2C19/tricyclic antidepressants, NUDT15/thiopurines, CYP2B6/efavirenz, and CYP2C19/clopidogrel. In conclusion, although the global contribution of LAC countries remains low in the PGx field, a relevant improvement has been observed in the region. The perception of the usefulness of PGx tests in biomedical community has drastically changed, raising awareness among physicians, which suggests a promising future in the clinical applications of PGx in LAC. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring the response after treatment of liver cancer and timely adjusting the treatment strategy are crucial to improve the survival rate of liver cancer. At present, the clinical monitoring of liver cancer after treatment is mainly based on serum markers and imaging. Morphological evaluation has limitations, such as the inability to measure small tumors and the poor repeatability of measurement, which is not applicable to cancer evaluation after immunotherapy or targeted treatment. The determination of serum markers is greatly affected by the environment and cannot accurately evaluate the prognosis. With the development of single cell sequencing technology, a large number of immune cell-specific genes have been identified. Immune cells and microenvironment play an important role in the process of prognosis. We speculate that the expression changes of immune cell-specific genes can indicate the process of prognosis. Method: Therefore, this paper first screened out the immune cell-specific genes related to liver cancer, and then built a deep learning model based on the expression of these genes to predict metastasis and the survival time of liver cancer patients. We verified and compared the model on the data set of 372 patients with liver cancer. Result: The experiments found that our model is significantly superior to other methods, and can accurately identify whether liver cancer patients have metastasis and predict the survival time of liver cancer patients according to the expression of immune cell-specific genes. Discussion: We found these immune cell-specific genes participant multiple cancer-related pathways. We fully explored the function of these genes, which would support the development of immunotherapy for liver cancer. [ABSTRACT FROM AUTHOR]

Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. However, scRNAseq data are often high dimensional and sparse, and manual cell type identification can be time-consuming, subjective, and lack reproducibility. Consequently, analyzing scRNA-seq data remains a computational challenge. With the increasing availability of well-annotated scRNA-seq datasets, advanced methods are emerging to aid in cell type identification by leveraging this information. Deep learning neural networks have great potential for analyzing single-cell data. This paper proposes MulCNN, a multi-level convolutional neural network that uses a unique cell type-specific gene expression feature extraction method. This method extracts critical features through multi-scale convolution while filtering noise. Extensive testing using datasets from various species and comparisons with popular classification methods show that MulCNN has outstanding performance and offers a new and scalable direction for scRNAseq analysis. [ABSTRACT FROM AUTHOR]

Antibiotic resistance genes (ARGs) are a new environmental contaminant that poses a major hazard to humans and the environment. This research discusses the methods and drawbacks of two ARG removal approaches, constructed wetlands (CWs) and photocatalysis. CWs primarily rely on the synergistic effects of substrate adsorption, plant uptake, and microbial processes to remove ARGs. The removal of ARGs can be influenced by wetland plants, substrate type, wetland type, and hydraulic conditions. The absolute abundance of ARGs in effluent decreased, but their relative abundance increased. Photocatalysis deactivates ARGs predominantly through reactive oxygen species, with removal effectiveness determined by catalyst type, radiation type, and radiation intensity. The drawback is that it exposes intracellular resistance genes, perhaps increasing the risk of ARG spread. To address the current shortcomings, this paper proposes the feasibility of combining a constructed wetland with photocatalysis technology, which provides a novel strategy for ARG removal. [ABSTRACT FROM AUTHOR]

An analysis of the phylogeny of Cephaloziellaceae was carried out based on trees constructed for previously and newly obtained sequences of five genes: nuclear ITS1-2 and chloroplast trnL-F, trnG, rbcL, and psbA. Phylogenetic trees inferred from different genes are congruent for the main details; however, the position of several taxa is variable. As a result, a new phylogenetic system of the family was proposed. The narrow genus concept seems to be more appropriate for the family. Cephaloziella spinicaulis is segregated into the new genus Douiniella, the generic status for Prionolobus and Metacephalozia is confirmed, and the dubious generic status of Kymatocalyx is substantiated. The generic independence of Cylindrocolea from Cephaloziella s. str. is confirmed. The small amount of data hinders the description of two more genera from Cephaloziella s.l. [ABSTRACT FROM AUTHOR]

It is realized that the first intron plays a key role in regulating gene expression, and the interactions between the first introns and other introns must be related to the regulation of gene expression. In this paper, the sequences of mitochondrial ribosomal protein genes were selected as the samples, based on the Smith-Waterman method, the optimal matched segments between the first intron and the reverse complementary sequences of other introns of each gene were obtained, and the characteristics of the optimal matched segments were analyzed. The results showed that the lengths and the ranges of length distributions of the optimal matched segments are increased along with the evolution of eukaryotes. For the distributions of the optimal matched segments with different GC contents, the peak values are decreased along with the evolution of eukaryotes, but the corresponding GC content of the peak values are increased along with the evolution of eukaryotes, it means most introns of higher organisms interact with each other though weak bonds binding. By comparing the lengths and matching rates of optimal matched segments with those of siRNA and miRNA, it is found that some optimal matched segments may be related to non-coding RNA with special biological functions, just like siRNA and miRNA, they may play an important role in the process of gene expression and regulation. For the relative position of the optimal matched segments, the peaks of relative position distributions of optimal matched segments are increased during the evolution of eukaryotes, and the positions of the first two peaks exhibit significant conservatism. [ABSTRACT FROM AUTHOR]

The cyclic GMP-AMP synthase-stimulator of interferon genes signal transduction pathway is critical in innate immunity, infection, and inflammation. In response to pathogenic microbial infections and other conditions, cyclic GMP-AMP synthase (cGAS) recognizes abnormal DNA and initiates a downstream type I interferon response. This paper reviews the pathogenic mechanisms of stimulator of interferon genes (STING) in different organs, including changes in fibrosis-related biomarkers, intending to systematically investigate the effect of the cyclic GMP-AMP synthasestimulator of interferon genes signal transduction in inflammation and fibrosis processes. The effects of stimulator of interferon genes in related auto-inflammatory and neurodegenerative diseases are described in this article, in addition to the application of stimulator of interferon genesrelated drugs in treating fibrosis. [ABSTRACT FROM AUTHOR]

TP53-induced glycolysis and apoptosis regulator (TIGAR) mainly regulates pentose phosphate pathway by inhibiting glycolysis, so as to synthesize ribose required by DNA, promote DNA damage repair and cell proliferation, maintain cell homeostasis and avoid body injury. Its physiological functions include anti-oxidative stress, reducing inflammation, maintaining mitochondrial function, inhibiting apoptosis, reducing autophagy etc. This paper reviews the research of TIGAR in neurological diseases, including stroke, Parkinson's disease (PD), Alzheimer's disease (AD), seizures and brain tumors, aiming to provide reference for the development of new therapeutic targets. [ABSTRACT FROM AUTHOR]

In this paper, the whole genome of themultidrug-resistant Aeromonas hydrophila MX16A was comprehensively analyzed and compared after sequencing by PacBio RS II. To shed light on the drug resistance mechanism of A. hydrophila MX16A, a Kirby-Bauer disk diffusion method was used to assess the phenotypic drug susceptibility. Importantly, resistance against β-lactam, sulfonamides, rifamycins, macrolides, tetracyclines and chloramphenicols was largely consistent with the prediction analysis results of drug resistance genes in the CARD database. The varied types of resistance genes identified from A. hydrophila MX16A revealed multiple resistance mechanisms, including enzyme inactivation, gene mutation and active effusion. The publicly available complete genomes of 35 Aeromonas hydrophila strains on NCBI, including MX16A, were downloaded for genomic comparison and analysis. The analysis of 33 genomes with ANI greater than 95% showed that the pan-genome consisted of 9556 genes, and the core genes converged to 3485 genes. In summary, the obtained results showed that A. hydrophila exhibited a great genomic diversity as well as diverse metabolic function and it is believed that frequent exchanges between strains lead to the horizontal transfer of drug resistance genes. [ABSTRACT FROM AUTHOR]

Background: A recent paper has revealed a novel cell death pathway, cuproptosis, a programmed cell death based on copper. This study aimed to evaluate the pancancer genomics and clinical association of cuproptosis and copper metabolismrelated cell death genes, including SLC25A3, SLC25A37, SLC31A1, FDX1, DLAT, LIAS, ATP7A, ATP7B, COX17, SCO1, SCO2, COX11, and COX19. Methods: By mining multi-omics profiling data, we performed a comprehensive and systematic characterization of cuproptosis genes across more than 9,000 samples of over 30 types of cancer. Results: ATP7B and ATP7A were the two most frequently mutated copper cell death genes in cancer. UCEC and SKCM were the two cancer types that have the highest mutation rates while the mutation of LIAS was associated with worse survival of BRCA. Brain cancer was potentially affected by copper cell death because of the difference in copper cell death gene expression among subtypes and stages. On the contrary, KIRC might have a lower cuproptosis activity because of the decrease in copper cell death gene expression. In lung cancer and kidney cancer, most of the cancer–noncancer expression patterns of copper cell death genes were consistent between mRNA and protein levels. Some of the cuproptosis gene expression was associated with the survival of LGG, KIRC, and ACC. The top five expression-copy numbers correlating cancer types were BRCA, OV, LUSC, HNSC, BLCA, and LUAD. Generally, the copy number variations of these genes in KIRC, UCEC, and LGG were associated with survival. The expression of DLAT, LIAS, and ATP7B was negatively correlated with the methylation in most of the cancer types. The copper cell death genes regulating miRNA and pathway regulation networks were constructed. The copper cell death genes were correlated with immune cell infiltration levels of multiple immune cells. These genes were correlated with the sensitivity of cancer cells to multiple drugs. Conclusion: Copper cell death genes are potentially involved in many cancer types and can be developed as candidates for cancer diagnosis, prognosis, and therapeutic biomarkers. [ABSTRACT FROM AUTHOR]

The role of long non-coding RNA (lncRNA) in human tumors has gradually received increasing attention in recent years. Particularly, the different functions of lncRNAs in different subcellular localizations have been widely investigated. The upregulation of lncRNA small nucleolar RNA host gene 17 (SNHG17) has been observed in various human tumors. Growing evidence has proved that SNHG17 plays a tumor-promoting role in tumorigenesis and development. This paper describes the molecular mechanisms by which SNHG17 contributes to tumor formation and development. The different functions of SNHG17 in various subcellular localizations are also emphasized: its function in the cytoplasm as a competing endogenous RNA (ceRNA), its action in the nucleus as a transcriptional coactivator, and its function through the polycomb repressive complex 2 (PRC2)-dependent epigenetic modifications that regulate transcriptional processes. Finally, the correlation between SNHG17 and human tumors is summarized. Its potential as a novel prognostic and diagnostic biomarker for cancer is explored especially. [ABSTRACT FROM AUTHOR]

Gastric cancer is a complex multifactorial and multistage process that involves a large number of tumor-related gene structural changes and abnormal expression. Therefore, knowing the related genes of gastric cancer can further understand the pathogenesis of gastric cancer and provide guidance for the development of targeted drugs. Traditional methods to discover gastric cancer-related genes based on biological experiments are time-consuming and expensive. In recent years, a large number of computational methods have been developed to identify gastric cancer-related genes. In addition, a large number of experiments show that establishing a biological network to identify disease-related genes has higher accuracy than ordinary methods. However, most of the current computing methods focus on the processing of homogeneous networks, and do not have the ability to encode heterogeneous networks. In this paper, we built a heterogeneous network using a disease similarity network and a gene interaction network. We implemented the graph transformer network (GTN) to encode this heterogeneous network. Meanwhile, the deep belief network (DBN) was applied to reduce the dimension of features. We call this method "DBN-GTN", and it performed best among four traditional methods and five similar methods. [ABSTRACT FROM AUTHOR]

Rhizoctonia solani is a pathogen that causes considerable harm to plants worldwide. In the absence of hosts, R. solani survives in the soil by forming sclerotia, and management methods, such as cultivar breeding, crop rotations, and fungicide sprays, are insufficient and/or inefficient in controlling R. solani. One of the most challenging problems facing agriculture in the twenty-first century besides with the impact of global warming. Environmentally friendly techniques of crop production and improved agricultural practices are essential for long-term food security. Trichoderma spp. could serve as an excellent example of a model fungus to enhance crop productivity in a sustainable way. Among biocontrol mechanisms, mycoparasitism, competition, and antibiosis are the fundamental mechanisms by which Trichoderma spp. defend against R. solani , thereby preventing or obstructing its proliferation. Additionally, Trichoderma spp. induce a mixed induced systemic resistance (ISR) or systemic acquired resistance (SAR) in plants against R. solani , known as Trichoderma -ISR. Stimulation of every biocontrol mechanism involves Trichoderma spp. genes responsible for encoding secondary metabolites, siderophores, signaling molecules, enzymes for cell wall degradation, and plant growth regulators. Rhizoctonia solani biological control through genes of Trichoderma spp. is summarized in this paper. It also gives information on the Trichoderma -ISR in plants against R. solani. Nonetheless, fast-paced current research on Trichoderma spp. is required to properly utilize their true potential against diseases caused by R. solani. [ABSTRACT FROM AUTHOR]

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc. [ABSTRACT FROM AUTHOR]

IntroductionDevelopmental language disorder (DLD) is a common childhood condition negatively influencing communication and psychosocial development. An increasing number of pathogenic variants or chromosomal anomalies possibly related to DLD have been identified. To provide a base for accurate clinical genetic diagnostic work-up for DLD patients, understanding the specific genetic background is crucial. This study aims to give a systematic literature overview of pathogenic variants or chromosomal anomalies causative for DLD in children.MethodsWe conducted a systematic search in PubMed and Embase on available literature related to the genetic background of diagnosed DLD in children. Included papers were critically appraised before data extraction. An additional search in OMIM was performed to see if the described DLD genes are associated with a broader clinical spectrum.ResultsThe search resulted in 15,842 papers. After assessing eligibility, 47 studies remained, of which 25 studies related to sex chromosome aneuploidies and 15 papers concerned other chromosomal anomalies (SCAs) and/or Copy Number Variants (CNVs), including del15q13.1–13.3 and del16p11.2. The remaining 7 studies displayed a variety of gene variants. 45 (candidate) genes related to language development, including FOXP2, GRIN2A, ERC1, and ATP2C2. After an additional search in the OMIM database, 22 of these genes were associated with a genetic disorder with a broader clinical spectrum, including intellectual disability, epilepsy, and/or autism.ConclusionOur study illustrates that DLD can be related to SCAs and specific CNV's. The reported (candidate) genes (n = 45) in the latter category reflect the genetic heterogeneity and support DLD without any comorbidities and syndromic language disorder have an overlapping genetic etiology.

Genomic copy number variations (CNVs) are among the most important structural variations of genes found to be related to the risk of individual cancer and therefore they can be utilized to provide a clue to the research on the formation and progression of cancer. In this paper, an improved computational gene selection algorithm called CRIA (correlation-redundancy and interaction analysis based on gene selection algorithm) is introduced to screen genes that are closely related to cancer from the whole genome based on the value of gene CNVs. The CRIA algorithm mainly consists of two parts. Firstly, the main effect feature is selected out from the original feature set that has the largest correlation with the class label. Secondly, after the analysis involving correlation, redundancy and interaction for each feature in the candidate feature set, we choose the feature that maximizes the value of the custom selection criterion and add it into the selected feature set and then remove it from the candidate feature set in each selection round. Based on the real datasets, CRIA selects the top 200 genes to predict the type of cancer. The experiments' results of our research show that, compared with the state-of-the-art related methods, the CRIA algorithm can extract the key features of CNVs and a better classification performance can be achieved based on them. In addition, the interpretable genes highly related to cancer can be known, which may provide new clues at the genetic level for the treatment of the cancer. [ABSTRACT FROM AUTHOR]

In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits. [ABSTRACT FROM AUTHOR]

The evolution of plant life has left its footprints in various ways, either as fossils, as morphology and/or anatomy, or as genes embodied in cells of living organisms. Extant plants are the consequence of over-400-million-years of evolution that are reflected in the innovations of reproductive organs throughout their evolutionary history, such as the occurrence of seeds, ovaries, flowers, inflorescences, and their functions. Thus, understanding different plant reproductive organs and their innovations is essential for the correct interpretation of plant evolution and their phylogenetic relationships. This eBook aims to shed light on the evolution of reproductive organs through papers documenting variations of reproductive organs at different levels, ranging from molecular to morphological, in hopes of triggering further investigations in this field. Below, we highlight the progresses covered in this thematic special volume. Scientific observation is an active process in which an observer recognizes objects that fall within pre-existing concepts. Considering this framework, it is assumed that (1) perspective decides the outcome of observations and that (2) a new perspective could create the foundation for a new observation. In light of new concepts of sexual reproduction and "Plant Morphogenesis 123," Bai organized the major morphological traits into five categories, and viewed a plant as a colony of integrated plant developmental units that are each produced via one life cycle. This function-based perspective allows us to view plants in a new way. Embryogenesis and seed formation are key events in the history of plants. Three papers in this eBook address these issues from different perspectives. To elucidate the seed maturation program, Han et al. surveyed the evolution of the LAFL [L: LEAFY COTYLEDON1 (LEC1) and LEC1-LIKE (L1L), belonging to NF-YB gene family; A: ABSCISIC ACID INSENSITIVE3 (ABI3); F: FUSCA3 (FUS3); L: LEC2 (LEAFY COTYLEDON2). The latter three genes belong to B3-AFL gene family related to embryo development] gene network, which is thought to orchestrate the accumulation of storage compounds and acquisition of desiccation tolerance in seed maturation, among various major plant lineages. Their result indicates that the origin of the embryo-development-related AFL gene family dates back to a common ancestor of the bryophytes and vascular plants. LAFL genes are likely related to spore and seed maturation, and the varying expression patterns of LAFL genes across the major vascular plant lineages may shed new light on the diversifying history of seed plants. Meanwhile, Li et al. and Fang et al. performed related studies using Adiantumcapillus-veneris fromdifferent perspectives. Li et al. characterized the expression patterns of six embryogenesis-associated genes during the somatic embryogenesis of A. capillus-veneris. Some of these gene families diversified rapidly in embryophytes, suggesting a rapid evolution of embryogenesis-associated genes in the tracheophyte development. Fang et al. examined the [ABSTRACT FROM AUTHOR]

Essential gene prediction models built so far are heavily reliant on sequence-based features, and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we apply our approach for the prediction of essential genes to organisms from the STRING database and host the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2,700+ bacteria predicted using features derived from STRING protein–protein functional association networks. Housing a total of over 2.1 million genes, NetGenes offers various features like essentiality scores, annotations, and feature vectors for each gene. NetGenes database is available from https://rbc-dsai-iitm.github.io/NetGenes/. [ABSTRACT FROM AUTHOR]

Catecholaminergic polymorphic ventricular tachycardia is a primary arrhythmogenic syndrome with genetic features most commonly seen in adolescents, with syncope and sudden death following exercise or agitation as the main clinical manifestations. The mechanism of its occurrence is related to the aberrant release of Ca2+ from cardiomyocytes caused by abnormal RyR2 channels or CASQ2 proteins under conditions of sympathetic excitation, thus inducing a delayed posterior exertional pole, manifested by sympathetic excitation inducing adrenaline secretion, resulting in bidirectional or polymorphic ventricular tachycardia. The mortality rate of the disease is high, but patients usually do not have organic heart disease, the clinical manifestations may not be obvious, and no significant abnormal changes in the QT interval are often observed on electrocardiography. Therefore, the disease is often easily missed and misdiagnosed. A number of genetic mutations have been linked to the development of this disease, and the mechanisms are different. In this paper, we would like to summarize the possible genes related to catecholaminergic polymorphic ventricular tachycardia in order to review the genetic tests currently performed, and to further promote the development of genetic testing techniques and deepen the research on the molecular level of this disease. [ABSTRACT FROM AUTHOR]

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods. [ABSTRACT FROM AUTHOR]

Genes often work together to perform complex biological processes, and "networks" provide a versatile framework for representing the interactions between multiple genes. Differential network analysis (DiNA) quantifies how this network structure differs between two or more groups/phenotypes (e.g., disease subjects and healthy controls), with the goal of determining whether differences in network structure can help explain differences between phenotypes. In this paper, we focus on gene co-expression networks, although in principle, the methods studied can be used for DiNA for other types of features (e.g., metabolome, epigenome, microbiome, proteome, etc.). Three common applications of DiNA involve (1) testing whether the connections to a single gene differ between groups, (2) testing whether the connection between a pair of genes differs between groups, or (3) testing whether the connections within a "module" (a subset of 3 or more genes) differs between groups. This article focuses on the latter, as there is a lack of studies comparing statistical methods for identifying differentially co-expressed modules (DCMs). Through extensive simulations, we compare several previously proposed test statistics and a new p-norm difference test (PND). We demonstrate that the true positive rate of the proposed PND test is competitive with and often higher than the other methods, while controlling the false positive rate. The R package discoMod (differentially co-expressed modules) implements the proposed method and provides a full pipeline for identifying DCMs: clustering tools to derive gene modules, tests to identify DCMs, and methods for visualizing the results. [ABSTRACT FROM AUTHOR]

The past 10 years has seen a remarkable advance in our understanding of the disease traditionally referred to as "essential tremor" (ET). First, the clinical phenotype of ET has been expanded from that of a bland, unidimensional, and monosymptomatic entity to one with a host of heterogeneous features. These features include a broader and more nuanced collection of tremors, non-tremor motor features (e.g., gait abnormalities) and a range of non-motor features, including cognitive, psychiatric, sleep, and other abnormalities. The natural history of these features, as well as their relationships with one another and with disease duration and severity, are better appreciated than they were previously. Studies of disease etiology have identified a number of candidate genes as well as explored several environmental determinants of disease. In addition, the decade has seen the beginnings and expansion of rigorous postmortem studies that have identified and described the postmortem changes in the brains of patients with ET. This emerging science has given rise to a new notion that the disease, in many cases, is one of cerebellar system degeneration. Across all of these studies (clinical, etiological, and pathophysiological) is the observation that there is heterogeneity across patients and that "essential tremor" is likely not a single disease but, rather, a family of diseases. The time has come to use the more appropriate terminology, "the essential tremors," to fully describe and encapsulate what is now apparent. In this paper, the author will review the clinical, etiological, and pathophysiological findings, referred to above, and make the argument that the terminology should evolve to reflect advances in science and that "the essential tremors" is a more scientifically appropriate term. [ABSTRACT FROM AUTHOR]

Compound heterozygous (CH) variants occur when two recessive alleles are inherited and the variants are located at different loci within the same gene in a given individual. CH variants are important contributors to many different types of recessively inherited diseases. However, many studies overlook CH variants because identification of this type of variant requires knowing the parent of origin for each nucleotide. Using computational methods, haplotypes can be inferred using a process called "phasing," which estimates the chromosomal origin of most nucleotides. In this paper, we used germline, phased, whole-genome sequencing (WGS) data to identify CH variants across seven pediatric diseases (adolescent idiopathic scoliosis: n = 16, congenital heart defects: n = 709, disorders of sex development: n = 79, ewing sarcoma: n = 287, neuroblastoma: n = 259, orofacial cleft: n = 107, and syndromic cranial dysinnervation: n = 172), available as parent-child trios in the Gabriella Miller Kids First Data Resource Center. Relatively little is understood about the genetic underpinnings of these diseases. We classified CH variants as "potentially damaging" based on minor allele frequencies (MAF), Combined Annotation Dependent Depletion scores, variant impact on transcription or translation, and gene-level frequencies in the disease group compared to a healthy population. For comparison, we also identified homozygous alternate (HA) variants, which affect both gene copies at a single locus; HA variants represent an alternative mechanism of recessive disease development and do not require phasing. Across all diseases, 2.6% of the samples had a potentially damaging CH variant and 16.2% had a potentially damaging HA variant. Of these samples with potentially damaging variants, the average number of genes per sample was 1 with a CH variant and 1.25 with a HA variant. Across all samples, 5.1 genes per disease had a CH variant, while 35.6 genes per disease had a HA variant; on average, only 4.3% of these variants affected common genes. Therefore, when seeking to identify potentially damaging variants of a putatively recessive disease, CH variants should be considered as potential contributors to disease development. If CH variants are excluded from analysis, important candidate genes may be overlooked. [ABSTRACT FROM AUTHOR]

Toxoplasma gondii is a resilient parasite that infects a multitude of warm-blooded hosts and results in a lifelong chronic infection requiring continuous responses by the host. Chronic infection is characterized by a balanced immune response and neuropathology that are driven by changes in gene expression. Previous research pertaining to these processes has been conducted in various mouse models, and much knowledge of infection-induced gene expression changes has been acquired through the use of high throughput sequencing techniques in different mouse strains and post-mortem human studies. However, lack of infection time course data poses a prominent missing link in the understanding of chronic infection, and there is still much that is unknown regarding changes in genes specifically relating to neuropathology and resulting repair mechanisms as infection progresses throughout the different stages of chronicity. In this paper, we present a targeted approach to gene expression analysis during T. gondii infection through the use of NanoString nCounter gene expression assays. Wild type C57BL/6 and BALB/c background mice were infected, and transcriptional changes in the brain were evaluated at 14, 28, and 56 days post infection. Results demonstrate a dramatic shift in both previously demonstrated and novel gene expression relating to neuropathology and resolution in C57BL/6 mice. In addition, comparison between BALB/c and C57BL/6 mice demonstrate initial differences in gene expression that evolve over the course of infection and indicate decreased neuropathology and enhanced repair in BALB/c mice. In conclusion, these studies provide a targeted approach to gene expression analysis in the brain during infection and provide elaboration on previously identified transcriptional changes and also offer insights into further understanding the complexities of chronic T. gondii infection. [ABSTRACT FROM AUTHOR]

Background: This paper aims to identify alternative RNA splicing landscape and its prognostic value in adrenocortical carcinoma. Methods: The alternative splicing events data with corresponding clinical information data of 79 ACC patients were obtained from the Cancer Genome Atlas and SpliceSeq package. Prognosis-associated AS events by using univariate Cox regression analysis were selected. Gene functional enrichment analysis demonstrated the potential pathways enriched by survival-associated AS. Prognosis-related splicing events were submitted to develop moderate predictors using Lasso regression model. Results: One thousand five survival-associated alternative splicing events were identified. The prognostic genes included ATXN2L, MEIS1 , IKBKB , COX4I1. Functional enrichment analysis suggested that prognostic splicing events are associated with Wnt signaling pathway. A prediction model including 12 alternative splicing events was constructed by Lasso regression using train set. ROC analysis showed good performance of the prediction model in test set. Then, a nomogram integrating the clinical-pathological factors and riskscore was constructed for predicting 1‐ and 3‐year survival rate. Conclusion: Our data provide a comprehensive bioinformatics analysis of AS events in ACC, providing biomarkers for disease progression and a potentially rich source of novel therapeutic targets. [ABSTRACT FROM AUTHOR]

Lack of demonstrable mutations affecting JAK2, CALR , or MPL driver genes within the spectrum of BCR-ABL1 -negative myeloproliferative neoplasms (MPNs) is currently referred to as a triple-negative genotype, which is found in about 10% of patients with essential thrombocythemia (ET) and 5–10% of those with primary myelofibrosis (PMF). Very few papers are presently available on triple-negative ET, which is basically described as an indolent disease, differently from triple-negative PMF, which is an aggressive myeloid neoplasm, with a significantly higher risk of leukemic evolution. The aim of the present study was to evaluate the bone marrow morphology and the clinical-laboratory parameters of triple-negative ET patients, as well as to determine their molecular profile using next-generation sequencing (NGS) to identify any potential clonal biomarkers. We evaluated a single-center series of 40 triple-negative ET patients, diagnosed according to the 2017 WHO classification criteria and regularly followed up at the Hematology Unit of our Institution, between January 1983 and January 2019. In all patients, NGS was performed using the Illumina Ampliseq Myeloid Panel; morphological and immunohistochemical features of the bone marrow trephine biopsies were also thoroughly reviewed. Nucleotide variants were detected in 35 out of 40 patients. In detail, 29 subjects harbored one or two variants and six cases showed three or more concomitant nucleotide changes. The most frequent sequence variants involved the TET2 gene (55.0%), followed by KIT (27.5%). Histologically, most of the cases displayed a classical ET morphology. Interestingly, prevalent megakaryocytes morphology was more frequently polymorphic with a mixture of giant megakaryocytes with hyperlobulated nuclei, normal and small sized maturing elements, and naked nuclei. Finally, in five cases a mild degree of reticulin fibrosis (MF-1) was evident together with an increase in the micro-vessel density. By means of NGS we were able to identify nucleotide variants in most cases, thus we suggest that a sizeable proportion of triple-negative ET patients do have a clonal disease. In analogy with driver genes-mutated MPNs, these observations may prevent issues arising concerning triple-negative ET treatment, especially when a cytoreductive therapy may be warranted. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner. [ABSTRACT FROM AUTHOR]

The first complete genome that was sequenced at the beginning of the sequencing era was that of a phage, since then researchers throughout the world have been steadily describing and publishing genomes from a wide array of phages, uncovering the secrets of the most abundant and diverse biological entities known to man. Currently, we are experiencing an unprecedented rate of novel bacteriophage discovery, which can be seen from the fact that the amount of complete bacteriophage genome entries in public sequence repositories has more than doubled in the past 3 years and is steadily growing without showing any sign of slowing down. The amount of publicly available phage genome-related data can be overwhelming and has been summarized in literature before but quickly becomes out of date. Thus, the aim of this paper is to briefly outline currently available phage diversity data for public acknowledgment that could possibly encourage and stimulate future "depth" studies of particular groups of phages or their gene products. [ABSTRACT FROM AUTHOR]

The first complete genome that was sequenced at the beginning of the sequencing era was that of a phage, since then researchers throughout the world have been steadily describing and publishing genomes from a wide array of phages, uncovering the secrets of the most abundant and diverse biological entities known to man. Currently, we are experiencing an unprecedented rate of novel bacteriophage discovery, which can be seen from the fact that the amount of complete bacteriophage genome entries in public sequence repositories has more than doubled in the past 3 years and is steadily growing without showing any sign of slowing down. The amount of publicly available phage genome-related data can be overwhelming and has been summarized in literature before but quickly becomes out of date. Thus, the aim of this paper is to briefly outline currently available phage diversity data for public acknowledgment that could possibly encourage and stimulate future "depth" studies of particular groups of phages or their gene products. [ABSTRACT FROM AUTHOR]

One of the biggest problems in cucumber cultivation is cucurbit downy mildew (DM), caused by the obligate biotroph Pseudoperonospora cubensis. Whereas DM in cucumber was previously efficiently controlled by the dm-1 gene from Indian cucumber accession PI 197087, this resistance was broken by new DM strains, prompting the search for novel sources of resistance. A promising source of resistance is the wild cucumber accession PI 197088. It was previously shown that DM resistance in this genotype inherits polygenically. In this paper, we put the focus on one of the QTL, DM4.1 that is located on chromosome 4. QTL DM4.1 was shown to consist of three subQTL: DM4.1.1 affected pathogen-induced necrosis, DM4.1.2 was shown to have an additive effect on sporulation, and DM4.1.3 had a recessive effect on chlorosis as well as an effect on sporulation. Near-isogenic lines (NILs) were produced by introgressing the subQTLs into a susceptible cucumber line (HS279) with good horticultural traits. Transcriptomic analysis revealed that many genes in general, and defense pathway genes in particular, were differentially expressed in NIL DM4.1.1/.2 compared to NIL DM4.1.3 and the susceptible parent HS279. This indicates that the resistance from subQTL DM4.1.1 and/or subQTL DM4.1.2 likely involves defense signaling pathways, whereas resistance due to subQTL DM4.1.3 is more likely to be independent of known defense pathways. Based on fine-mapping data, we identified the RLK gene CsLRK10L2 as a likely candidate for subQTL DM4.1.2, as this gene was found to have a loss-of-function mutation in the susceptible parent HS279, and was strongly upregulated by P. cubensis inoculation in NIL DM4.1.1/.2. Heterologous expression of this gene triggered necrosis, providing further evidence that this gene is indeed causal for subQTL DM4.1.2. [ABSTRACT FROM AUTHOR]

Salvia miltiorrhiza Bunge (Lamiaceae) is an economically important medicinal plant as well as an emerging model plant. Our previous studies indicate that Sm MYC2b is a positive transcription factor that can affect the biosynthesis of phenolic acids and tanshinones in S. miltiorrhiza. Moreover, MYC2s are well known to induce the development of lateral roots. As tanshinones are mainly distributed in the periderm, the promotion of lateral root development probably leads to increased accumulation of tanshinones. In this paper, we firstly discovered that Sm MYC2b played a dual regulatory role in effectively enhancing the tanshinone accumulation by activating tanshinone biosynthetic pathway and promoting lateral root development. The expression levels of the previously studied pathway genes SmCPS1 , SmKSL1 , SmCYP76AH1 , SmCYP76AH3 , and SmCYP76AK1 dramatically increased. In addition, Sm MYC2b was proved to exhibit a similar function as other homologs in promoting lateral root development, which increased the tanshinone produced tissue and further enhanced the biosynthesis of tanshinones. RNA-seq assays revealed that Sm MYC2b-regulated genes comprised 30.6% (1,901 of 6,210) of JA-responsive genes, confirming that Sm MYC2b played a crucial role in transcriptional regulation of JA-regulated genes. Overall, we concluded that Sm MYC2b could enhance tanshinone accumulation by activating the tanshinone biosynthetic pathway and promoting lateral root development. Our study provides an effective approach to enhance the production of desired tanshinones and enriches our knowledge of the related regulatory network. [ABSTRACT FROM AUTHOR]

The formyltetrahydrofolate synthetase (FTHFS) gene is a molecular marker of choice to study the diversity of acetogenic communities. However, current analyses are limited due to lack of a high-throughput sequencing approach for FTHFS gene amplicons and a dedicated bioinformatics pipeline for data analysis, including taxonomic annotation and visualization of the sequence data. In the present study, we combined the barcode approach for multiplexed sequencing with unsupervised data analysis to visualize acetogenic community structure. We used samples from a biogas digester to develop proof-of-principle for our combined approach. We successfully generated high-throughput sequence data for the partial FTHFS gene and performed unsupervised data analysis using the novel bioinformatics pipeline "AcetoScan" presented in this study, which resulted in taxonomically annotated OTUs, phylogenetic tree, abundance plots and diversity indices. The results demonstrated that high-throughput sequencing can be used to sequence the FTHFS amplicons from a pool of samples, while the analysis pipeline AcetoScan can be reliably used to process the raw sequence data and visualize acetogenic community structure. The method and analysis pipeline described in this paper can assist in the identification and quantification of known or potentially new acetogens. The AcetoScan pipeline is freely available at https://github.com/abhijeetsingh1704/AcetoScan. [ABSTRACT FROM AUTHOR]