Explore the vast range of titles available.

Renal aging, marked by the accumulation of senescent cells and chronic low‐grade inflammation, leads to renal interstitial fibrosis and impaired function. In this study, we investigate the role of macrophages, a key regulator of inflammation, in renal aging by analyzing kidney single‐cell RNA sequencing data of C57BL/6J mice from 8 weeks to 24 months. Our findings elucidate the dynamic changes in the proportion of kidney cell types during renal aging and reveal that increased macrophage infiltration contributes to chronic low‐grade inflammation, with these macrophages exhibiting senescence and activation of ferroptosis signaling. CellChat analysis indicates enhanced communications between macrophages and tubular cells during aging. Suppressing ferroptosis alleviates macrophage‐mediated tubular partial epithelial‐mesenchymal transition in vitro, thereby mitigating the expression of fibrosis‐related genes. Using SCENIC analysis, we infer Stat1 as a key age‐related transcription factor promoting iron dyshomeostasis and ferroptosis in macrophages by regulating the expression of Pcbp1, an iron chaperone protein that inhibits ferroptosis. Furthermore, through virtual screening and molecular docking from a library of anti‐aging compounds, we construct a docking model targeting Pcbp1, which indicates that the natural small molecule compound Rutin can suppress macrophage senescence and ferroptosis by preserving Pcbp1. In summary, our study underscores the crucial role of macrophage iron dyshomeostasis and ferroptosis in renal aging. Our results also suggest Pcbp1 as an intervention target in aging‐related renal fibrosis and highlight Rutin as a potential therapeutic agent in mitigating age‐related renal chronic low‐grade inflammation and fibrosis. During renal aging, the accumulation of senescent macrophages promotes tubular partial epithelial‐mesenchymal transition through the secretion of the senescence‐associated secretory phenotype, ultimately contributing to age‐related renal fibrosis. We propose that Pcbp1 is a key factor for iron homeostasis, and its downregulation promotes renal macrophage senescence by inducing iron dyshomeostasis. While the transcription factor Stat1 contributes to Pcbp1 downregulation, Rutin can preserve the level of Pcbp1 under senescent conditions. This suggests that Rutin is a promising agent for modulating the function of Pcbp1 during renal aging.

As an invaluable Chinese sheep germplasm resource, Hu sheep are renowned for their high fertility and beautiful wavy lambskins. Their distinctive characteristics have evolved over time through a combination of artificial and natural selection. Identifying selection signatures in Hu sheep can provide a straightforward insight into the mechanism of selection and further uncover the candidate genes associated with breed‐specific traits subject to selection. Here, we conducted whole‐genome resequencing on 206 Hu sheep individuals, each with an approximate 6‐fold depth of coverage. And then we employed three complementary approaches, including composite likelihood ratio, integrated haplotype homozygosity score and the detection of runs of homozygosity, to detect selection signatures. In total, 10 candidate genomic regions displaying selection signatures were simultaneously identified by multiple methods, spanning 88.54 Mb. After annotating, these genomic regions harbored collectively 92 unique genes. Interestingly, 32 candidate genes associated with reproduction were distributed in nine genomic regions detected. Out of them, two stood out as star candidates: BMPR1B and GNRH2, both of which have documented associations with fertility, and a HOXA gene cluster (HOXA1‐5, HOXA9, HOXA10, HOXA11 and HOXA13) had also been linked to fertility. Additionally, we identified other genes that are related to hair follicle development (LAMTOR3, EEF1A2), ear size (HOXA1, KCNQ2), fat tail formation (HOXA10, HOXA11), growth and development (FAF1, CCNDBP1, GJB2, GJA3), fat deposition (ACOXL, JAZF1, HOXA3, HOXA4, HOXA5, EBF4), immune (UBR1, FASTKD5) and feed intake (DAPP1, RNF17, NPBWR2). Our results offer novel insights into the genetic mechanisms underlying the selection of breed‐specific traits in Hu sheep and provide a reference for sheep genetic improvement programs.

The detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups. When working with high-throughput sequencing data, calling genotypes accurately is not always possible; therefore, the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes. We provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors, and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate. We prove that the D-statistic is approximated by a standard normal distribution. Furthermore, we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low and medium sequencing depth (1–10×), and performances are as good as with perfectly called genotypes at a sequencing depth of 2×. We show the reliability of error correction in scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.

Objective: Investigation of Klebsiella pneumoniae in respiratory-diseased pet cats, estimation of antibiotic sensitivity, and molecular phylogeny of local K. pneumoniae to identify its identity to global isolates. Methods: Totally, 127 feline cases with various respiratory signs were selected for the collection of the nasal swabs that were cultured to isolate K. pneumoniae and detect the antibiotic sensitivity. Further molecular phylogeny of positive K. pneumoniae isolates was done. Results: Findings of culture media and biochemical tests showed that 26.77% of nasal swabs were positive samples for K. pneumoniae. The screening for the antibiotic susceptibility reported a higher sensitivity to ceftiofur, ciprofloxacin, cefepime, amikacin, gentamicin, cefotaxime, and meropenem, as well as ceftazidime, ceftriaxone, and doxycycline, imipenem, as well as clotrimazole and tetracycline. In contrast, the more significant resistant K. pneumoniae isolates were detected to clarithromycin, clindamycin, amoxicillin, cefixime, chloramphenicol, erythromycin, cephalexin, cefadroxil, azithromycin, and nalidixic acid, whereas, significant semi-sensitivity was shown to tylosin. Molecular testing by polymerase chain reaction demonstrated that all isolates were K. pneumoniae. The genetics-based analysis of local K. pneumoniae isolates recorded an overall similarity (95.47%–100%) and changes/mutations (0.0004%–0.0084%), in particular to the National Center for Biotechnology Information-Iraqi isolate (Lc732203.1). Conclusion: This study indicates the high prevalence of K. pneumoniae in respiratory-diseased cats with significant appearance of antibiotic resistance in study isolates. Sequencing data referred to the close related association of study isolates to human K. pneumoniae isolates, suggesting the increased prevalence of nosocomial infections in veterinary medicine.

Single‐cell RNA sequencing (scRNA‐seq) is a powerful tool for investigating paediatric cancers, but individual studies often profile a small number of individuals. It is now the standard practice to upload the scRNA‐seq data to data repositories to support scientific reproducibility. Public data deposition is a cost‐effective and sustainability‐conscious solution that allows any researcher to download and analyse existing scRNA‐seq data to develop new ideas. This is incredibly valuable, especially in the context of paediatric cancer research, where access to funding and to patient cohorts may be prohibitive. However, standards for data deposition are absent, leading to significant issues that may slow progress. As a consequence, it is difficult, even impossible, for other researchers to validate findings or utilise these data for tailored analyses. Here, we systematically accessed and reviewed publicly available scRNA‐seq data sets from various paediatric cancer studies, covering over 1.3 million cells across 488 clinical samples. We highlight striking inconsistencies with study design and data availability across several levels, which hinder downstream analyses and data reproducibility. To address these challenges, we propose a recommendations framework to improve data deposition practices that promote more effective use of scRNA‐seq data sets deposited on public repositories and accelerate discoveries in paediatric cancer research and beyond. We urge data standards institutes and repositories, such as NCBI Gene Expression Omnibus (GEO) and European Genome‐Phenome Archive (EGA), to strictly enforce these standardised data practices.

Advantages and diagnostic effectiveness of the two most widely used resequencing approaches, whole exome (WES) and whole genome (WGS) sequencing, are often debated. WES dominated large-scale resequencing projects because of lower cost and easier data storage and processing. Rapid development of 3 rd generation sequencing methods and novel exome sequencing kits predicate the need for a robust statistical framework allowing informative and easy performance comparison of the emerging methods. In our study we developed a set of statistical tools to systematically assess coverage of coding regions provided by several modern WES platforms, as well as PCR-free WGS. We identified a substantial problem in most previously published comparisons which did not account for mappability limitations of short reads. Using regression analysis and simple machine learning, as well as several novel metrics of coverage evenness, we analyzed the contribution from the major determinants of CDS coverage. Contrary to a common view, most of the observed bias in modern WES stems from mappability limitations of short reads and exome probe design rather than sequence composition. We also identified the ~ 500 kb region of human exome that could not be effectively characterized using short read technology and should receive special attention during variant analysis. Using our novel metrics of sequencing coverage, we identified main determinants of WES and WGS performance. Overall, our study points out avenues for improvement of enrichment-based methods and development of novel approaches that would maximize variant discovery at optimal cost.

Background The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. Results We develop DrImpute to impute dropout events in scRNA-seq data. We show that DrImpute has significantly better performance on the separation of the dropout zeros from true zeros than existing imputation algorithms. We also demonstrate that DrImpute can significantly improve the performance of existing tools for clustering, visualization and lineage reconstruction of nine published scRNA-seq datasets. Conclusions DrImpute can serve as a very useful addition to the currently existing statistical tools for single cell RNA-seq analysis. DrImpute is implemented in R and is available at https://github.com/gongx030/DrImpute .

Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

Long non-coding RNAs (lncRNAs) are emerging as a fundamental class of biological effect or molecules that perform pivotal functions in the regulation of the genome. With advances in bioinformatics and genomics, extensive identification and characterization of lncRNAs is now possible. They regulate cellular growth, differentiation and apoptosis. Dysregulation of lncRNAs has been associated with numerous types of human cancer. In the present study, the expression profile of differentially expressed genes (DEGs) and lncRNAs in gastric cancer (GC) samples and normal tissue samples was evaluated with bioinformatics. The biological functions of the predicted lncRNA TCONS_00068220 were focused on; the DEGs co-expressed with TCONS_00068220 were enriched in cancer-associated pathways. TCONS_00068220 was demonstrated to be upregulated in GC tissues and cell lines compared with normal controls. In addition, an increased rate of apoptosis was observed in NCI-N87 cells following transfection with small interfering RNA against TCONS_00068220. These data suggest that TCONS_00068220 may be associated with the pathogenesis of GC, and it may serve as a potential therapeutic target.

Aggressive natural killer-cell (NK-cell) leukemia (ANKL) is an extremely aggressive malignancy with dismal prognosis and lack of targeted therapies. Here, we elucidate the molecular pathogenesis of ANKL using a combination of genomic and drug sensitivity profiling. We study 14 ANKL patients using whole-exome sequencing (WES) and identify mutations in STAT3 (21%) and RAS-MAPK pathway genes (21%) as well as in DDX3X (29%) and epigenetic modifiers (50%). Additional alterations include JAK-STAT copy gains and tyrosine phosphatase mutations, which we show recurrent also in extranodal NK/T-cell lymphoma, nasal type (NKTCL) through integration of public genomic data. Drug sensitivity profiling further demonstrates the role of the JAK-STAT pathway in the pathogenesis of NK-cell malignancies, identifying NK cells to be highly sensitive to JAK and BCL2 inhibition compared to other hematopoietic cell lineages. Our results provide insight into ANKL genetics and a framework for application of targeted therapies in NK-cell malignancies. Aggressive natural killer-cell leukemia (ANKL) has few targeted therapies. Here ANKL patients are reported to harbor STAT3, RAS-MAPK pathway, DDX3X and epigenetic modifier mutations; and drug sensitivity profiling uncovers the importance of the JAK-STAT pathway, revealing potential ANKL therapeutic targets.