Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears.

Polar bears are uniquely adapted to life in the High Arctic and have undergone drastic physiological changes in response to Arctic climates and a hyper-lipid diet of primarily marine mammal prey. We analyzed 89 complete genomes of polar bear and brown bear using population genomic modeling and show that the species diverged only 479-343 thousand years BP. We find that genes on the polar bear lineage have been under stronger positive selection than in brown bears; nine of the top 16 genes under strong positive selection are associated with cardiomyopathy and vascular disease, implying important reorganization of the cardiovascular system. One of the genes showing the strongest evidence of selection, APOB, encodes the primary lipoprotein component of low-density lipoprotein (LDL); functional mutations in APOB may explain how polar bears are able to cope with life-long elevated LDL levels that are associated with high risk of heart disease in humans.

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

PanDepth, an ultrafast and efficient genomic tool for coverage calculation.

Coverage quantification is required in many sequencing datasets within the field of genomics research. However, most existing tools fail to provide comprehensive statistical results and exhibit limited performance gains from multithreading. Here, we present PanDepth, an ultra-fast and efficient tool for calculating coverage and depth from sequencing alignments. PanDepth outperforms other tools in computation time and memory efficiency for both BAM and CRAM-format alignment files from sequencing data, regardless of read length. It employs chromosome parallel computation and optimized data structures, resulting in ultrafast computation speeds and memory efficiency. It accepts sorted or unsorted BAM and CRAM-format alignment files as well as GTF, GFF and BED-formatted interval files or a specific window size. When provided with a reference genome sequence and the option to enable GC content calculation, PanDepth includes GC content statistics, enhancing the accuracy and reliability of copy number variation analysis. Overall, PanDepth is a powerful tool that accelerates scientific discovery in genomics research.

VCF2PCACluster: a simple, fast and memory-efficient tool for principal component analysis of tens of millions of SNPs.

Principal component analysis (PCA) is an important and widely used unsupervised learning method that determines population structure based on genetic variation. Genome sequencing of thousands of individuals usually generate tens of millions of SNPs, making it challenging for PCA analysis and interpretation. Here we present VCF2PCACluster, a simple, fast and memory-efficient tool for Kinship estimation, PCA and clustering analysis, and visualization based on VCF formatted SNPs. We implemented five Kinship estimation methods and three clustering methods for its users to choose from. Moreover, unlike other PCA tools, VCF2PCACluster possesses a clustering function based on PCA result, which enabling users to automatically and clearly know about population structure. We demonstrated the same accuracy but a higher performance of this tool in performing PCA analysis on tens of millions of SNPs compared to another popular PLINK2 software, especially in peak memory usage that is independent of the number of SNPs in VCF2PCACluster.

NGenomeSyn: an easy-to-use and flexible tool for publication-ready visualization of syntenic relationships across multiple genomes.

SUMMARY: Large-scale comparative genomic studies have provided important insights into species evolution and diversity, but also lead to a great challenge to visualize. Quick catching or presenting key information hidden in the vast amount of genomic data and relationships among multiple genomes requires an efficient visualization tool. However, current tools for such visualization remain inflexible in layout and/or require advanced computation skills, especially for visualization of genome-based synteny. Here, we developed an easy-to-use and flexible layout tool, NGenomeSyn [multiple (N) Genome Synteny], for publication-ready visualization of syntenic relationships of the whole genome or local region and genomic features (e.g. repeats, structural variations, genes) across multiple genomes with a high customization. NGenomeSyn provides an easy way for its users to visualize a large amount of data with a rich layout by simply adjusting options for moving, scaling, and rotation of target genomes. Moreover, NGenomeSyn could be applied on the visualization of relationships on non-genomic data with similar input formats. AVAILABILITY AND IMPLEMENTATION: NGenomeSyn is freely available at GitHub (https://github.com/hewm2008/NGenomeSyn) and Zenodo (https://doi.org/10.5281/zenodo.7645148).

IgA nephropathy: gut microbiome regulates the production of hypoglycosilated IgA1via the TLR4 signaling pathway.

BACKGROUND: IgA nephropathy (IgAN) is a major cause of primary glomerulonephritis characterized by mesangial deposits of galactose-deficient IgA1 (Gd-IgA1). Toll-like receptors (TLRs), particularly TLR4 are involved in the pathogenesis of IgAN. The role of gut microbiota on IgAN patients was recently investigated. However, whether gut microbial modifications of Gd-IgA1 through TLR4 play a role in IgAN remains unclear. METHODS: We recruited subjects into four groups, including 48 patients with untreated IgAN, 22 treated IgAN patients (IgANIT), 22 primary membranous nephropathy (MN), and 31 healthy controls (HCs). Fecal samples were collected to analyze changes in gut microbiome. Gd-IgA1 levels, expression of TLR4, B-cell stimulators, and intestinal barrier function were evaluated in all subjects. C57BL/6 mice were treated with a broad-spectrum antibiotic cocktail to deplete the gut microbiota and then gavaged with fecal microbiota transplanted fromclinical subjects of every group. Gd-IgA1 and TLR4 pathway were detected in peripheral blood mononuclear cells (PBMCs) from IgAN and HCs co-incubated with Lipopolysaccharide (LPS) and TLR4 inhibitor. RESULTS: Compared with other three groups, different compositions and decreased diversity demonstrated gut dysbiosis in un-treated IgAN, especially the enrichment of Escherichia -Shigella. Elevated Gd-IgA1 levels were found in un-treated IgAN patients and correlated with gut dysbiosis, TLR4, B-cell stimulators, indexes of intestinal barrier damage, and proinflammatory cytokines. In vivo, mice colonized with gut microbiota from IgAN and IgANIT patients, copied the IgAN phenotype with the activation of TLR4/MyD88/NF-κB pathway, B-cell stimulators in the intestine, and complied with enhanced proinflammatory cytokines. In vitro, LPS activated TLR4/MyD88/NF-κB pathway, B-cell stimulators and proinflammatory cytokines in the PBMCs from IgAN patients, which resulted in overproduction of Gd-IgA1 and inhibited by TLR4 inhibitor. CONCLUSIONS: Our results illustrated that gut-kidney axis was involved in the pathogenesis of IgAN. Gut dysbiosis could stimulate the overproduction of Gd-IgA1 by TLR4 signaling pathway production and B-cell stimulators.

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files.

The triangular correlation heatmap aiming to visualize the linkage disequilibrium (LD) pattern and haplotype block structure of SNPs is ubiquitous component of population-based genetic studies. However, current tools suffered from the problem of time and memory consuming. Here, we developed LDBlockShow, an open source software, for visualizing LD and haplotype blocks from variant call format files. It is time and memory saving. In a test dataset with 100 SNPs from 60 000 subjects, it was at least 10.60 times faster and used only 0.03-13.33% of physical memory as compared with other tools. In addition, it could generate figures that simultaneously display additional statistical context (e.g. association P-values) and genomic region annotations. It can also compress the SVG files with a large number of SNPs and support subgroup analysis. This fast and convenient tool will facilitate the visualization of LD and haplotype blocks for geneticists.

The chromosome-scale assembly of endive (Cichorium endivia) genome provides insights into the sesquiterpenoid biosynthesis.

Endive (Cichorium endivia L.) is a leafy vegetable in the Asteraceae family. Sesquiterpene lactones (STLs) in endive leaves bring a bitter taste that varies between varieties. Despite their importance in breeding varieties with unique flavours, sesquiterpenoid biosynthesis pathways in endive are poorly understood. We assembled a chromosome-scale endive genome of 641 Mb with a contig N50 of 5.16 Mb and annotated 46,711 protein-coding genes. Several gene families, especially terpene synthases (TPS) genes, expanded significantly in the C. endivia genome. STLs biosynthesis-related genes and TPS genes in more bitter varieties have shown a higher level of expression, which could be attributed to genomic variations. Our results penetrate the origin and diversity of bitter taste and facilitate the molecular breeding of endive varieties with unique bitter tastes. The high-quality endive assembly would provide a reference genome for studying the evolution and diversity of Asteraceae.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.

Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

Arbuscular Mycorrhizal Fungi Compete Asymmetrically for Amino Acids with Native and Invasive Solidago.

Arbuscular mycorrhizal fungi (AMF) and soil amino acids both affect plant performance. However, little is known about how AMF compete for amino acids with native and invasive congeners. We conducted a factorial experiment (inoculation, native and invasive species, and amino acids) to examine the competition for amino acids between soil microbes and both native and invasive congeners. The competition for amino acids between AMF and invasive Solidago canadensis was weaker than that observed between AMF and native S. decurrens. This asymmetric competition increased the growth advantage of S. canadensis over S. decurrens. The efficacy (biomass production per unit of nitrogen supply) of amino acids compared to ammonium was smaller in S. canadensis than in S. decurrens when both species were grown without inoculation, but the opposite was the case when both species were grown with AMF. AMF and all microbes differentially altered four phenotypic traits (plant height, leaf chlorophyll content, leaf number, and root biomass allocation) and the pathways determining the effects of amino acids on growth advantages. These findings suggest that AMF could enhance plant invasiveness through asymmetric competition for amino acids and that amino acid-driven invasiveness might be differentially regulated by different microbial guilds.

The seahorse genome and the evolution of its specialized morphology.

Seahorses have a specialized morphology that includes a toothless tubular mouth, a body covered with bony plates, a male brood pouch, and the absence of caudal and pelvic fins. Here we report the sequencing and de novo assembly of the genome of the tiger tail seahorse, Hippocampus comes. Comparative genomic analysis identifies higher protein and nucleotide evolutionary rates in H. comes compared with other teleost fish genomes. We identified an astacin metalloprotease gene family that has undergone expansion and is highly expressed in the male brood pouch. We also find that the H. comes genome lacks enamel matrix protein-coding proline/glutamine-rich secretory calcium-binding phosphoprotein genes, which might have led to the loss of mineralized teeth. tbx4, a regulator of hindlimb development, is also not found in H. comes genome. Knockout of tbx4 in zebrafish showed a 'pelvic fin-loss' phenotype similar to that of seahorses.

Development and validation of a novel nomogram to predict overall survival of patients with moderate to severe chronic kidney disease.

INTRODUCTION: The risk of death significantly increased from stage 3 chronic kidney disease (CKD) onward. We aimed to construct a novel nomogram to predict the overall survival (OS) of patients afflicted with CKD from stage 3-5. METHODS: A total of 882 patients with stage 3-5 CKD were enrolled from the NHANES 2001-2004 survey. Data sets from the 2003-2004 survey population were used to develop a nomogram that would predict the risk of OS. The 2001-2002 survey population was used to validate the nomogram. Least absolute shrinkage and selection operator (Lasso) regression was conducted to screen the significant predictors relative to all-cause death. The multivariate Cox regression based on the screened factors was applied to effectively construct the nomogram. The performance of the nomogram was evaluated according to the C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve with 1000 bootstraps resample. Kaplan-Meier's curves were used for testing the discrimination of the prediction model. RESULTS: Five variables (age, urinary albumin-to-creatinine ratio (UACR), potassium, cystatin C (Cys C), and homocysteine) were screened by the Lasso regression. The nomogram was constructed using these factors, as well as the CKD stage. The included factors (age, CKD stage, UACR, potassium, Cys C, and homocysteine) were all significantly related to the death of CKD patients, according to the multivariate Cox regression analysis. The internal validation showed that this nomogram demonstrates good discrimination and calibration (adjusted C-index: 0.70; AUC of 3-, 5-, and 10-year OS were 0.75, 0.78, and 0.77, respectively). External validation also demonstrated exceedingly similar results (C-index: 0.72, 95% CI: 0.69-0.76; AUC of 3-, 5-, and 10-year OS were 0.76, 0.79, and 0.80, respectively). CONCLUSIONS: This study effectively constructed a novel nomogram that incorporates CKD stage, age, UACR, potassium, Cys C, and homocysteine, which can be conveniently used to facilitate the individualized prediction of survival probability in patients with stage 3-5 CKD. It displays valuable potential for clinical application.

Candidate gene prioritization for chronic obstructive pulmonary disease using expression information in protein-protein interaction networks.

BACKGROUND: Identifying or prioritizing genes for chronic obstructive pulmonary disease (COPD), one type of complex disease, is particularly important for its prevention and treatment. METHODS: In this paper, a novel method was proposed to Prioritize genes using Expression information in Protein-protein interaction networks with disease risks transferred between genes (abbreviated as PEP). A weighted COPD PPI network was constructed using expression information and then COPD candidate genes were prioritized based on their corresponding disease risk scores in descending order. RESULTS: Further analysis demonstrated that the PEP method was robust in prioritizing disease candidate genes, and superior to other existing prioritization methods exploiting either topological or functional information. Top-ranked COPD candidate genes and their significantly enriched functions were verified to be related to COPD. The top 200 candidate genes might be potential disease genes in the diagnosis and treatment of COPD. CONCLUSIONS: The proposed method could provide new insights to the research of prioritizing candidate genes of COPD or other complex diseases with expression information from sequencing or microarray data.

Network-based integration method for potential breast cancer gene identification.

Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.

The improved assembly of 7DL chromosome provides insight into the structure and evolution of bread wheat.

Wheat is one of the most important staple crops worldwide and also an excellent model species for crop evolution and polyploidization studies. The breakthrough of sequencing the bread wheat genome and progenitor genomes lays the foundation to decipher the complexity of wheat origin and evolutionary process as well as the genetic consequences of polyploidization. In this study, we sequenced 3286 BACs from chromosome 7DL of bread wheat cv. Chinese Spring and integrated the unmapped contigs from IWGSC v1 and available PacBio sequences to close gaps present in the 7DL assembly. In total, 8043 out of 12 825 gaps, representing 3 491 264 bp, were closed. We then used the improved assembly of 7DL to perform comparative genomic analysis of bread wheat (Ta7DL) and its D donor, Aegilops tauschii (At7DL), to identify domestication signatures. Results showed a strong syntenic relationship between Ta7DL and At7DL, although some small rearrangements were detected at the distal regions. A total of 53 genes appear to be lost genes during wheat polyploidization, with 23% (12 genes) as RGA (disease resistance gene analogue). Furthermore, 86 positively selected genes (PSGs) were identified, considered to be domestication-related candidates. Finally, overlapping of QTLs obtained from GWAS analysis and PSGs indicated that TraesCS7D02G321000 may be one of the domestication genes involved in grain morphology. This study provides comparative information on the sequence, structure and organization between bread wheat and Ae. tauschii from the perspective of the 7DL chromosome, which contribute to better understanding of the evolution of wheat, and supports wheat crop improvement.

PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files.

MOTIVATION: Linkage disequilibrium (LD) decay is of great interest in population genetic studies. However, no tool is available now to do LD decay analysis from variant call format (VCF) files directly. In addition, generation of pair-wise LD measurements for whole genome SNPs usually resulting in large storage wasting files. RESULTS: We developed PopLDdecay, an open source software, for LD decay analysis from VCF files. It is fast and is able to handle large number of variants from sequencing data. It is also storage saving by avoiding exporting pair-wise results of LD measurements. Subgroup analyses are also supported. AVAILABILITY AND IMPLEMENTATION: PopLDdecay is freely available at https://github.com/BGI-shenzhen/PopLDdecay.

Unusual ectopic ACTH syndrome in a patient with orbital neuroendocrine tumor, resulted false-positive outcome of BIPSS:a case report.

BACKGROUND: Cushing's syndrome has been described as a complex endocrine disorder characterized with high cortisol concentration. Correct and early diagnosis of Cushing's syndrome is challenging. According to the latest guideline, bilateral inferior petrosal sinus sampling (BIPSS) is considered to be the gold standard for the differential diagnosis. However, in some unusual cases, this method may be false positive. Here, we presented a rare case of orbital neuroendocrine tumor secreting adrenocorticotrophic hormone with false positive inferior petrosal sinus sampling. CASE PRESENTATION: A 48-year-old woman was admitted to West China Hospital of Sichuan University, presenting with fatigue, whole body edema for 1 year, alopecia and skin pigmentation for 5 months. Hormonal profiles including plasma cortisol and adrenocorticotrophic hormone (ACTH) measurements and low-dose dexamethasone inhibition test suggested that the patient had Cushing's syndrome. However, during tumor location phase, the results of high-dose dexamethasone inhibition test (HDDST) contradicted desmopressin (DDAVP) stimulation test. Thus, BIPSS was employed, and its results indicated a pituitary origin. Interestingly, MRI of sellar region showed an innocent pituitary but caught a serendipitous lesion in the lateral rectus muscle of left eye, which was later proved to be an orbital neuroendocrine tumor secreting ACTH by pathological and immunohistochemical results. ACTH level of the patients was < 0.1 ng/L and cortisol level was 51.61 nmol/L 1 week after surgery. At 24 months follow-up, the patient appeared stable with no complaints nor any symptoms of Cushing's syndrome, including moon face, purple striate and central obesity. The patient's life quality also improved significantly. CONCLUSION: We reported a rare case of endogenous Cushing's syndrome due to ectopic ACTH secreting from an orbital neuroendocrine tumor. This unique case of orbital EAS suggests that orbital venous blood backflow, owning to abnormal anatomic structures, may possibly lead to false positive BIPSS results.

Prioritizing complex disease risk genes by integrating multiple data.

Complex diseases, such as obesity, type II diabetes and chronic obstructive pulmonary disease (COPD) as metabolic disorder-related diseases are major concern for worldwide public health in the 21st century. The identification of these disease risk genes has attracted increasing interest in computational systems biology. In this paper, a novel method was proposed to prioritize disease risk genes (PDRG) by integrating functional annotations, protein interactions and gene expression information to assess similarity between genes in a disease-related metabolic network. The gene prioritization method was successfully carried out for obesity and COPD, the effectiveness of which was superior to those of ToppGene and ToppNet in both literature validation and recall rate by LOOCV. Our method could be applied broadly to other metabolism-related diseases, helping to prioritize novel disease risk genes, and could shed light on diagnosis and effective therapies.

CXCR4-Receptor-Targeted Liposomes for the Treatment of Peritoneal Fibrosis.

Peritoneal fibrosis (PF) is a common complication of long-term peritoneal dialysis (PD). It is considered as the main reason for dialysis inadequacy and PD withdrawal. Transforming growth factor beta (TGF-ß) regulates the expression of stromal cell-derived factor 1 (SDF-1α) and its receptor C-X-C chemokine receptor type 4 (CXCR4) on human peritoneal mesothelial cells (HPMCs), resulting in an increased migratory potential of HPMCs and extracellular matrix (ECM) deposition in the scar tissue and eventually fibrosis. Because SDF-1α/CXCR4 activation has a vital role in the pathogenesis of PF, codelivery of a CXCR4-receptor targeting agent with an antifibrotic agent in a single nanocarrier can be a promising strategy for treating PF. Here, for the first time, AMD3100 (AMD), a CXCR4-receptor antagonist, was coformulated with sulfotanshinone IIA sodium (STS IIA) into a liposome (STS-AMD-Lips) to develop a CXCR4 receptor targeting form of combination therapy for PF. CXCR4 targeting increased the ability of liposomes to target fibrotic peritoneal mesothelial cells overexpressing CXCR4 and facilitated the ability of STS IIA treatment at the fibrotic site. The liposome had an average diameter of 103 nm with encapsulated efficiencies of above 50%. The in vivo studies confirmed the reversal of PD solution-induced epithelial-to-mesenchymal transition by STS-AMD-Lips in HPMCs. The in vivo studies also revealed the precise biodistribution of the liposomes to peritoneum. Significant reduction of the morphological lesions and decreased level of ECM proteins were observed in rats treated with STS-AMD-Lips, proving that the liposomal nanocarrier has excellent ability to reverse PF. It has been concluded that the STS-AMD-Lips exhibit specific peritoneal targeting ability and could be used to improve STS-AMD combination delivery for the treatment of PF.