High-throughput Genome Sequencing Research Articles

In-depth analysis of genomes and functional genomics of orchid using cutting-edge high-throughput sequencing.

High-throughput sequencing technology has been facilitated the development of new methodologies and approaches for studying the origin and evolution of plant genomes and subgenomes, population domestication, and functional genomics. Orchids have tens of thousands of members in nature. Many of them have promising application potential in the extension and conservation of the ecological chain, the horticultural use of ornamental blossoms, and the utilization of botanical medicines. However, a large-scale gene knockout mutant library and a sophisticated genetic transformation system are still lacking in the improvement of orchid germplasm resources. New gene editing tools, such as the favored CRISPR-Cas9 or some base editors, have not yet been widely applied in orchids. In addition to a large variety of orchid cultivars, the high-precision, high-throughput genome sequencing technology is also required for the mining of trait-related functional genes. Nowadays, the focus of orchid genomics research has been directed to the origin and classification of species, genome evolution and deletion, gene duplication and chromosomal polyploidy, and flower morphogenesis-related regulation. Here, the progressing achieved in orchid molecular biology and genomics over the past few decades have been discussed, including the evolution of genome size and polyploidization. The frequent incorporation of LTR retrotransposons play important role in the expansion and structural variation of the orchid genome. The large-scale gene duplication event of the nuclear genome generated plenty of recently tandem duplicated genes, which drove the evolution and functional divergency of new genes. The evolution and loss of the plastid genome, which mostly affected genes related to photosynthesis and autotrophy, demonstrated that orchids have experienced more separate transitions to heterotrophy than any other terrestrial plant. Moreover, large-scale resequencing provide useful SNP markers for constructing genetic maps, which will facilitate the breeding of novel orchid varieties. The significance of high-throughput sequencing and gene editing technologies in the identification and molecular breeding of the trait-related genes in orchids provides us with a representative trait-improving gene as well as some mechanisms worthy of further investigation. In addition, gene editing has promise for the improvement of orchid genetic transformation and the investigation of gene function. This knowledge may provide a scientific reference and theoretical basis for orchid genome studies.

Next-generation sequencing in the biodiversity conservation of endangered medicinal plants.

Medicinal plants have been used as traditional herbal medicines in the treatment of various types of diseases. However, the increased demand for these plants highlights the importance of conservation specifically for endangered species. Significant advancements in next-generation sequencing (NGS) technologies have accelerated medicinal plant research while reducing costs and time demands. NGS systems enable high-throughput whole genome sequencing as well as direct RNA sequencing and transcriptome analysis. The sequence data sets created can be used in a variety of areas of study, including biodiversity conservation, comparative genomics, transcriptomic analysis, single cell mining, metagenomics, epigenetics, molecular marker discovery, multi genome sequencing, and so on. Commercial sequencing service providers are constantly working to improve technologies to address bioinformatics problems in NGS data analysis. Several genome sequencing projects on medicinal plants have been completed recently and a few more are in the works. In some medicinal plants, massive NGS-based data has been developed. In the present review, we have attempted to briefly discuss advancements in NGS technology on medicinally essential plants in India. The review will also provide ideas for applying NGS technologies for exploring genomes of various endangered medicinal plants whose genome sequences are not normally available and thus provides valuable insights for the conservation of these vulnerable species.

Causal Discovery in High-dimensional, Multicollinear Datasets.

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

ProbeTools: designing hybridization probes for targeted genomic sequencing of diverse and hypervariable viral taxa

BackgroundSequencing viruses in many specimens is hindered by excessive background material from hosts, microbiota, and environmental organisms. Consequently, enrichment of target genomic material is necessary for practical high-throughput viral genome sequencing. Hybridization probes are widely used for enrichment in many fields, but their application to viral sequencing faces a major obstacle: it is difficult to design panels of probe oligo sequences that broadly target many viral taxa due to their rapid evolution, extensive diversity, and genetic hypervariability. To address this challenge, we created ProbeTools, a package of bioinformatic tools for generating effective viral capture panels, and for assessing coverage of target sequences by probe panel designs in silico. In this study, we validated ProbeTools by designing a panel of 3600 probes for subtyping the hypervariable haemagglutinin (HA) and neuraminidase (NA) genome segments of avian-origin influenza A viruses (AIVs). Using in silico assessment of AIV reference sequences and in vitro capture on egg-cultured viral isolates, we demonstrated effective performance by our custom AIV panel and ProbeTools’ suitability for challenging viral probe design applications.ResultsBased on ProbeTool’s in silico analysis, our panel provided broadly inclusive coverage of 14,772 HA and 11,967 NA reference sequences. For each reference sequence, we calculated the percentage of nucleotide positions covered by our panel in silico; 90% of HA and NA references sequences had at least 90.8 and 95.1% of their nucleotide positions covered respectively. We also observed effective in vitro capture on a representative collection of 23 egg-cultured AIVs that included isolates from wild birds, poultry, and humans and representatives from all HA and NA subtypes. Forty-two of forty-six HA and NA segments had over 98.3% of their nucleotide positions significantly enriched by our custom panel. These in vitro results were further used to validate ProbeTools’ in silico coverage assessment algorithm; 89.2% of in silico predictions were concordant with in vitro results.ConclusionsProbeTools generated an effective panel for subtyping AIVs that can be deployed for genomic surveillance, outbreak prevention, and pandemic preparedness. Effective probe design against hypervariable AIV targets also validated ProbeTools’ design and coverage assessment algorithms, demonstrating their suitability for other challenging viral capture applications.

GREAP: a comprehensive enrichment analysis software for human genomic regions.

The rapid development of genomic high-throughput sequencing has identified a large number of DNA regulatory elements with abundant epigenetics markers, which promotes the rapid accumulation of functional genomic region data. The comprehensively understanding and research of human functional genomic regions is still a relatively urgent work at present. However, the existing analysis tools lack extensive annotation and enrichment analytical abilities for these regions. Here, we designed a novel software, Genomic Region sets Enrichment Analysis Platform (GREAP), which provides comprehensive region annotation and enrichment analysis capabilities. Currently, GREAP supports 85 370 genomic region reference sets, which cover 634 681 107 regions across 11 different data types, including super enhancers, transcription factors, accessible chromatins, etc. GREAP provides widespread annotation and enrichment analysis of genomic regions. To reflect the significance of enrichment analysis, we used the hypergeometric test and also provided a Locus Overlap Analysis. In summary, GREAP is a powerful platform that provides many types of genomic region sets for users and supports genomic region annotations and enrichment analyses. In addition, we developed a customizable genome browser containing >400000000 customizable tracks for visualization. The platform is freely available at http://www.liclab.net/Greap/view/index.

Phytoplasma Taxonomy: Nomenclature, Classification, and Identification.

Simple SummaryPhytoplasmas are vector-borne and graft-transmissible bacteria that cause various plant diseases, leading to severe economic losses. Since phytoplasmas cannot be cultured in cell-free media, their identification and taxonomy rely on molecular techniques and gene sequences. In this article, we summarize the recent advances in phytoplasma taxonomy from three different aspects, including (i) nomenclature (naming Candidatus Phytoplasma species); (ii) classification (group and subgroup assignment based on 16S rRNA gene sequences); and (iii) identification (fine differentiation of phytoplasma strains). In addition, some important issues, especially those related to recognizing new ‘Candidatus Phytoplasma’ species, are discussed. This information will be helpful for rapid diagnosis of phytoplasma diseases and accurate taxonomic identification of both emerging and known phytoplasma strains.Phytoplasmas are pleomorphic, wall-less intracellular bacteria that can cause devastating diseases in a wide variety of plant species. Rapid diagnosis and precise identification of phytoplasmas responsible for emerging plant diseases are crucial to preventing further spread of the diseases and reducing economic losses. Phytoplasma taxonomy (identification, nomenclature, and classification) has lagged in comparison to culturable bacteria, largely due to lack of axenic phytoplasma culture and consequent inaccessibility of phenotypic characteristics. However, the rapid expansion of molecular techniques and the advent of high throughput genome sequencing have tremendously enhanced the nucleotide sequence-based phytoplasma taxonomy. In this article, the key events and milestones that shaped the current phytoplasma taxonomy are highlighted. In addition, the distinctions and relatedness of two parallel systems of ‘Candidatus phytoplasma’ species/nomenclature system and group/subgroup classification system are clarified. Both systems are indispensable as they serve different purposes. Furthermore, some hot button issues in phytoplasma nomenclature are also discussed, especially those pertinent to the implementation of newly revised guidelines for ‘Candidatus Phytoplasma’ species description. To conclude, the challenges and future perspectives of phytoplasma taxonomy are briefly outlined.

Application of CRISPR/Cas9 Genome Editing System to Reduce the Pre- and Post-Harvest Yield Losses in Cereals

Cereals are an important source of food for millions of people across low-middle-income countries. Cereals are considered a staple food for poor people. The majority of the people are depending upon agricultural occupation. Agriculture provides a primary source of income for many farmers in low-middle-income countries. The pre- and post-harvest loss of crop yield affects farmers and is a major problem in achieving food security. Biotic and abiotic factors cause pre- and post-harvest loss of crop yield worldwide. It significantly affects the economic status of farmers as well as low-middle-income countries. Many advanced technologies are available for resolving the pre- and post-harvest loss of crop yield. The past few decades have seen remarkable progress in crop improvement. Especially high-throughput genome sequencing approaches contributed to advancement in the crop improvement. Genome-editing has also been considered a key tool for crop improvement. The clustered, regularly interspaced, short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a potent genome editing system for modifying key traits in cereal crops. CRISPR/Cas9 system offers new opportunities for addressing pre-and post-harvest constraints affecting cereal grain production and storage. In this review, we discuss the application of the CRISPR/Cas9 genome editing system to reduce pre-and post-harvest yield loss in cereal crops. It may promote the economic status of farmers and reduce food demand in the coming decades.

Technologies for HIV-1 drug resistance testing: inventory and needs.

HIV-1 drug resistance (HIV DR) testing is routinely performed by genotyping plasma viruses using Sanger population sequencing. Next-generation sequencing (NGS) is increasingly replacing standardized Sanger sequencing. This opens up new opportunities, but also brings challenges. The number of NGS applications and protocols for HIV DR testing is increasing. All of them are noninferior to Sanger sequencing when comparing NGS-derived consensus sequences to Sanger sequencing-derived sequences. In addition, NGS enables high-throughput sequencing of near full-length HIV-1 genomes and detection of low-abundance drug-resistant HIV-1 variants, although their clinical implications need further investigation. Several groups have defined remaining challenges in implementing NGS protocols for HIV-1 resistance testing. Some of them are already being addressed. One of the most important needs is quality management and consequently, if possible, standardization. The use of NGS technologies on HIV DR testing will allow unprecedented insights into genomic structures of virus populations that may be of immediate relevance to both clinical and research areas such as personalized antiretroviral treatment. Efforts continue to tackle the remaining challenges in NGS-based HIV DR testing.

Genome-Guided Analysis of Seven Weed Species Reveals Conserved Sequence and Structural Features of Key Gene Targets for Herbicide Development.

Herbicides are commonly deployed as the front-line treatment to control infestations of weeds in native ecosystems and among crop plants in agriculture. However, the prevalence of herbicide resistance in many species is a major global challenge. The specificity and effectiveness of herbicides acting on diverse weed species are tightly linked to targeted proteins. The conservation and variance at these sites among different weed species remain largely unexplored. Using novel genome data in a genome-guided approach, 12 common herbicide-target genes and their coded proteins were identified from seven species of Weeds of National Significance in Australia: Alternanthera philoxeroides (alligator weed), Lycium ferocissimum (African boxthorn), Senecio madagascariensis (fireweed), Lantana camara (lantana), Parthenium hysterophorus (parthenium), Cryptostegia grandiflora (rubber vine), and Eichhornia crassipes (water hyacinth). Gene and protein sequences targeted by the acetolactate synthase (ALS) inhibitors and glyphosate were recovered. Compared to structurally resolved homologous proteins as reference, high sequence conservation was observed at the herbicide-target sites in the ALS (target for ALS inhibitors), and in 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase (target for glyphosate). Although the sequences are largely conserved in the seven phylogenetically diverse species, mutations observed in the ALS proteins of fireweed and parthenium suggest resistance of these weeds to ALS-inhibiting and other herbicides. These protein sites remain as attractive targets for the development of novel inhibitors and herbicides. This notion is reinforced by the results from the phylogenetic analysis of the 12 proteins, which reveal a largely consistent vertical inheritance in their evolutionary histories. These results demonstrate the utility of high-throughput genome sequencing to rapidly identify and characterize gene targets by computational methods, bypassing the experimental characterization of individual genes. Data generated from this study provide a useful reference for future investigations in herbicide discovery and development.

Epidermal Patterning Factor 2-like (McEPFL2): A Putative Candidate for the Continuous Ridge (cr) Fruit Skin Locus in Bitter Gourd (Momordica charantia L.).

Bitter gourd (Momordica charantia L.) is an economically important vegetable and medicinal crop in many Asian countries. Limited work has been conducted in understanding the genetic basis of horticulturally important traits in bitter gourd. Bitter gourd is consumed primarily for its young, immature fruit, and fruit appearance plays an important role in market acceptability. One such trait is the ridges on the fruit skin. In the present study, molecular mapping of a locus underlying fruit ridge continuity was conducted. Genetic analysis in segregating populations, derived from the crosses between two inbred lines Y1 with continuous ridges (CR) and Z-1-4 with discontinuous ridges (DCR), suggested that CR was controlled by a single recessive gene (cr). High-throughput genome sequencing of CR and DCR bulks combined with high-resolution genetic mapping in an F2 population delimited cr into a 108 kb region with 16 predicted genes. Sequence variation analysis and expression profiling supported the epidermal patterning factor 2-like (McEPFL2) gene as the best candidate of the cr locus. A 1 bp deletion in the first exon of McEPFL2 in Y1 which would result in a truncated McEPFL2 protein may be the causal polymorphism for the phenotypic difference between Y1 and Z-1-4. The association of this 1 bp deletion with CR was further supported by gDNA sequencing of McEPFL2 among 31 bitter gourd accessions. This work provides a foundation for understanding the genetic and molecular control of fruit epidermal pattering and development, which also facilitates marker-assisted selection in bitter melon breeding.

Machine-Learning Prospects for Detecting Selection Signatures Using Population Genomics Data.

Natural selection has been given a lot of attention because it relates to the adaptation of populations to their environments, both biotic and abiotic. An allele is selected when it is favored by natural selection. Consequently, the favored allele increases in frequency in the population and neighboring linked variation diminishes, causing so-called selective sweeps. A high-throughput genomic sequence allows one to disentangle the evolutionary forces at play in populations. With the development of high-throughput genome sequencing technologies, it has become easier to detect these selective sweeps/selection signatures. Various methods can be used to detect selective sweeps, from simple implementations using summary statistics to complex statistical approaches. One of the important problems of these statistical models is the potential to provide inaccurate results when their assumptions are violated. The use of machine learning (ML) in population genetics has been introduced as an alternative method of detecting selection by treating the problem of detecting selection signatures as a classification problem. Since the availability of population genomics data is increasing, researchers may incorporate ML into these statistical models to infer signatures of selection with higher predictive accuracy and better resolution. This article describes how ML can be used to aid in detecting and studying natural selection patterns using population genomic data.

Discovery of new therapeutic targets in ovarian cancer through identifying significantly non-mutated genes

BackgroundMutated and non-mutated genes interact to drive cancer growth and metastasis. While research has focused on understanding the impact of mutated genes on cancer biology, understanding non-mutated genes that are essential to tumor development could lead to new therapeutic strategies. The recent advent of high-throughput whole genome sequencing being applied to many different samples has made it possible to calculate if genes are significantly non-mutated in a specific cancer patient cohort.MethodsWe carried out random mutagenesis simulations of the human genome approximating the regions sequenced in the publicly available Cancer Growth Atlas Project for ovarian cancer (TCGA-OV). Simulated mutations were compared to the observed mutations in the TCGA-OV cohort and genes with the largest deviations from simulation were identified. Pathway analysis was performed on the non-mutated genes to better understand their biological function. We then compared gene expression, methylation and copy number distributions of non-mutated and mutated genes in cell lines and patient data from the TCGA-OV project. To directly test if non-mutated genes can affect cell proliferation, we carried out proof-of-concept RNAi silencing experiments of a panel of nine selected non-mutated genes in three ovarian cancer cell lines and one primary ovarian epithelial cell line.ResultsWe identified a set of genes that were mutated less than expected (non-mutated genes) and mutated more than expected (mutated genes). Pathway analysis revealed that non-mutated genes interact in cancer associated pathways. We found that non-mutated genes are expressed significantly more than mutated genes while also having lower methylation and higher copy number states indicating that they could be functionally important. RNAi silencing of the panel of non-mutated genes resulted in a greater significant reduction of cell viability in the cancer cell lines than in the non-cancer cell line. Finally, as a test case, silencing ANKLE2, a significantly non-mutated gene, affected the morphology, reduced migration, and increased the chemotherapeutic response of SKOV3 cells.ConclusionWe show that we can identify significantly non-mutated genes in a large ovarian cancer cohort that are well-expressed in patient and cell line data and whose RNAi-induced silencing reduces viability in three ovarian cancer cell lines. Targeting non-mutated genes that are important for tumor growth and metastasis is a promising approach to expand cancer therapeutic options.

GpemDB: A Scalable Database Architecture with the Multi-omics Entity-relationship Model to Integrate Heterogeneous Big-data for Precise Crop Breeding.

With the development of high-throughput genome sequencing and phenotype screening techniques, there is a possibility of leveraging multi-omics to speed up the breeding process. However, the heterogeneity of big data handicaps the progress and the lack of a comprehensive database supporting end-to-end association analysis impedes the efficient use of these data. In response to this problem, a scalable entity-relationship model and a database architecture are firstly proposed in this paper to manage the cross-platform data sets and explore the relationship among multi-omics, and finally accelerate our breeding efficiency. First, the targeted omics data of crops should be normalized before being stored in the database. A typical breeding data content and structure is demonstrated with the case study of rice (Oryza sativa L). Second, the structure, patterns and hierarchy of multi-omics data are described with the entity-relationship modeling technique. Third, some statistical tools used frequently in the agricultural analysis have been embedded into the database to help breeding. As a result, a general-purpose scalable database, called GpemDB integrating genomics, phenomics, enviromics and management, is developed. It is the first database designed to manage all these four omics data together. The GpemDB involving Gpem metadata-level layer and informative-level layer provides a visualized scheme to display the content of the database and facilitates users to manage, analyze and share breeding data. GpemDB has been successfully applied to a rice population, which demonstrates this database architecture and model are promising to serve as a powerful tool to utilize the big data for high precise and efficient research and breeding of crops.

GFI1 KO can develop allergic reaction independently from IL5 and ILC2 cells

Abstract The number of people living with a chronic inflammatory lung disease, such as asthma, is constantly increasing. The development of resistance to existing therapies and the fact that exposure to air pollution and particular matter are continuously increasing have made asthma a major health problem worldwide. We have observed that in mice lacking the transcriptional factor GFI1 (short for “Growth factor independent 1”) where ILC2 are not functional and IL5 is almost absent, a severe asthmatic inflammatory reaction can still be provoked with increased numbers of eosinophils in the airways. This finding strongly suggests that pathways other than those initiated by IL5/IL5R and ILC2 cells exist that play a major role in severe allergic asthma. We hypothesize that the situation in GFI1 KO mice reflects what it is observed in asthmatic patients that are resistant to the recent therapies. We further posit that GFI1 KO mice is a tool to discover new pathways involved in asthmatic reactions different from the known and established mechanisms involving IL5. We are using this unique mouse model (Gfi1 deficient mice) and high throughput genomic sequencing technologies with a specific bioinformatics approach to identify new molecules pathways mediating the inflammatory reaction in asthma. We expect to reveal new molecular signaling pathways that cause lung inflammation during an allergic asthma reaction, specifically those regulating eosinophil recruitment and activities. Our first results show that GFI1 KO seem to be able to support an allergic reaction though IL17 pathway and Th17 cells. We anticipate to identify of molecular targets, which could be used to develop new, more effective therapies for patients with severe allergic asthma.

Microevolution of the mexT and lasR Reinforces the Bias of Quorum Sensing System in Laboratory Strains of Pseudomonas aeruginosa PAO1.

The Pseudomonas aeruginosa strain PAO1 has routinely been used as a laboratory model for quorum sensing (QS). However, the microevolution of P. aeruginosa laboratory strains resulting in genetic and phenotypic variations have caused inconsistencies in QS research. To investigate the underlying causes of these variations, we analyzed 5 Pseudomonas aeruginosa PAO1 sublines from our laboratory using a combination of phenotypic characterization, high throughput genome sequencing, and bioinformatic analysis. The major phenotypic variations among the sublines spanned across the levels of QS signals and virulence factors such as pyocyanin and elastase. Furthermore, the sublines exhibited distinct variations in motility and biofilm formation. Most of the phenotypic variations were mapped to mutations in the lasR and mexT, which are key components of the QS circuit. By introducing these mutations in the subline PAO1-E, which is devoid of such mutations, we confirmed their influence on QS, virulence, motility, and biofilm formation. The findings further highlight a possible divergent regulatory mechanism between the LasR and MexT in the P. aeruginosa. The results of our study reveal the effects of microevolution on the reproducibility of most research data from QS studies and further highlight mexT as a key component of the QS circuit of P. aeruginosa.

Sequencing for novel mutation screening in juvenile polyposis syndrome

Juvenile polyposis syndrome (JPS), a rare polyposis syndrome in childhood, is characterized by multiple diffuse hamartomatous polyps in the gastrointestinal tract (GI) with an estimated incidence of one in 50,000 to 100,000 live births. Although a juvenile polyp is a non-cancerous lesion, affected individuals have an increased risk of GI malignancy. JPS is a disease related to loss-of-function mutations in BMPR1A and SMAD4 involved in the biological maintenance of cellular growth. Pathogenic mutations in those genes have been identified in approximately 50–60% of JPS cases. Our report describes two JPS cases with loss-of-function mutations of BMPR1A identified by a high-throughput nucleotide sequencing. Both cases had a similar clinical manifestation, symptomatic lower GI bleeding. Both patients underwent a total colectomy after histological confirmation. Consequently, Family members who had had genetic confirmation of these mutations were invited to be screened by enteroscopy. JPS is a hereditary disease caused by dominantly inherited loss-of-function of the BMPR1A and SMAD4 genes . Identifying loss-of-function mutations in particular genes allows early screening, diagnosis, and prevention of malignancy among the index cases and their families. High-throughput genome sequencing is a current molecular approach for improving the precision of mutation detection and minimizing time to diagnosis.

Prognostic Bone Metastasis-Associated Immune-Related Genes Regulated by Transcription Factors in Mesothelioma.

Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data.

The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail.

Prediction and Experimental Verification of a Hierarchical Transcription Factor Regulatory Network of Porcine Myoglobin (Mb).

Myoglobin is a key chemical component that determines meat's color and affects consumers' purchase intentions. In this work, we firstly identified the promoter sequence of the Mb gene from the primary assembly of high-throughput genome sequencing in pigs, and predicted its potential transcription factors by LASAGNA. Through the data mining of the mRNA expression profile of longissimus dorsi muscle of different pig breeds, we constructed a hierarchical interplay network of Mb-TFs (Myoglobin-Transcription Factors), consisting of 16 adaptive transcription factors and 23 secondary transcription factors. The verification of gene expression in longissimus dorsi muscle showed that the Mb mRNA and encoded protein were significantly (p < 0.05) more abundant in Bama pigs than Yorkshire pigs. The qRT-PCR (Real-Time Quantitative Reverse Transcription PCR) validation on genes of the Mb-TFs network showed that FOS, STAT3, STAT1, NEFL21, NFE2L2 and MAFB were significant positive regulatory core transcription factors of Mb-TFs network in Bama pigs, whereas ATF3 was the secondary transcription factor most responsible for the activation of the above transcription factors. Our study provides a new strategy to unravel the mechanism of pork color formation, based on public transcriptome and genome data analysis.

Genomic Determinants of Pathogenicity and Antimicrobial Resistance for 60 Global Listeria monocytogenes Isolates Responsible for Invasive Infections.

Listeria monocytogenes remains a significant public health threat, causing invasive listeriosis manifested as septicemia, meningitis, and abortion, with up to 30% of cases having a fatal outcome. Tracking the spread of invasive listeriosis requires an updated knowledge for virulence factors (VFs) and antimicrobial resistance features, which is an essential step toward its clinical diagnosis and treatment. Taking advantage of high-throughput genomic sequencing, we proposed that the differential genes based on the pathogenomic composition could be used to evaluate clinical observations and therapeutic options for listeriosis. Here, we performed the comparative genomic analysis of 60 strains from five continents with a diverse range of sources, representing serotypes 1/2a, 1/2b, 1/2c, and 4b, comprising lineage I and lineage II and including 13 newly contributed Chinese isolates from clinical cases. These strains were associated with globally distributed clonal groups linked with confirmed foodborne listeriosis outbreak and sporadic cases. We found that L. monocytogenes strains from clonal complex (CC) CC8, CC7, CC9, and CC415 carried most of the adherence and invasive genes. Conversely, CC1, CC2, CC4, and CC6 have the least number of adherence and invasive genes. Additionally, Listeria pathogenicity island-1 (LIPI-1), LIPI-2, intracellular survival, surface anchoring, and bile salt resistance genes were detected in all isolates. Importantly, LIPI-3 genes were harbored in CC3, CC224, and ST619 of the Chinese isolates and in CC1, CC4, and CC6 of other worldwide isolates. Notably, Chinese isolates belonging to CC14 carried antibiotic resistance genes (ARGs) against β-lactams (bla TEM-101 , bla TEM-105) and macrolide (ermC-15), whereas CC7 and CC8 isolates harbored ARGs against aminoglycoside (aadA10_2, aadA6_1), which may pose a threat to therapeutic efficacy. Phylogenomic analysis showed that CC8, CC7, and CC5 of Chinese isolates, CC8 (Swiss and Italian isolates), and CC5 and CC7 (Canadian isolates) are closely clustered together and belonged to the same CC. Additionally, CC381 and CC29 of Chinese isolates shared the same genomic pattern as CC26 of Swiss isolate and CC37 of Canadian isolate, respectively, indicating strong phylogenomic relation between these isolates. Collectively, this study highlights considerable clonal diversity with well-recognized virulence and antimicrobial-resistant determinants among Chinese and worldwide isolates that stress to design improved strategies for clinical therapies.