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Abstract Immune thrombocytopenia (ITP) is an autoimmune disorder, in which megakaryocyte dysfunction caused by an autoimmune reaction can lead to thrombocytopenia, although the underlying mechanisms remain unclear. Here, we performed single-cell transcriptome profiling of bone marrow CD34+ hematopoietic stem and progenitor cells (HSPCs) to determine defects in megakaryopoiesis in ITP. Gene expression, cell-cell interactions, and transcriptional regulatory networks varied in HSPCs of ITP, particularly in immune cell progenitors. Differentially expressed gene (DEG) analysis indicated that there was an impaired megakaryopoiesis of ITP. Flow cytometry confirmed that the number of CD9+ and HES1+ cells from Lin−CD34+CD45RA− HSPCs decreased in ITP. Liquid culture assays demonstrated that CD9+Lin−CD34+CD45RA− HSPCs tended to differentiate into megakaryocytes; however, this tendency was not observed in ITP patients and more erythrocytes were produced. The percentage of megakaryocytes differentiated from CD9+Lin−CD34+CD45RA− HSPCs was 3-fold higher than that of the CD9− counterparts from healthy controls (HCs), whereas, in ITP patients, the percentage decreased to only 1/4th of that in the HCs and was comparable to that from the CD9− HSPCs. Additionally, when co-cultured with pre-B cells from ITP patients, the differentiation of CD9+Lin−CD34+CD45RA− HSPCs toward the megakaryopoietic lineage was impaired. Further analysis revealed that megakaryocytic progenitors (MkP) can be divided into seven subclusters with different gene expression patterns and functions. The ITP-associated DEGs were MkP subtype-specific, with most DEGs concentrated in the subcluster possessing dual functions of immunomodulation and platelet generation. This study comprehensively dissects defective hematopoiesis and provides novel insights regarding the pathogenesis of ITP.

Abstract Background Retrotransposons have been implicated as causes of Mendelian disease, but their role in autism spectrum disorder (ASD) has not been systematically defined, because they are only called with adequate sensitivity from whole genome sequencing (WGS) data and a large enough cohort for this analysis has only recently become available. Results We analyzed WGS data from a cohort of 2288 ASD families from the Simons Simplex Collection by establishing a scalable computational pipeline for retrotransposon insertion detection. We report 86,154 polymorphic retrotransposon insertions—including > 60% not previously reported—and 158 de novo retrotransposition events. The overall burden of de novo events was similar between ASD individuals and unaffected siblings, with 1 de novo insertion per 29, 117, and 206 births for Alu, L1, and SVA respectively, and 1 de novo insertion per 21 births total. However, ASD cases showed more de novo L1 insertions than expected in ASD genes. Additionally, we observed exonic insertions in loss-of-function intolerant genes, including a likely pathogenic exonic insertion in CSDE1, only in ASD individuals. Conclusions These findings suggest a modest, but important, impact of intronic and exonic retrotransposon insertions in ASD, show the importance of WGS for their analysis, and highlight the utility of specific bioinformatic tools for high-throughput detection of retrotransposon insertions.

Identification of transposable element (TE) insertions from whole genome sequencing data remains challenging. Here the authors developed a comprehensive TE insertion detection algorithm xTea that can be applied to both short-read and long-read sequencing data.

Understanding the spatio-temporal patterns of the disease burden attributable to ambient PM2.5 across the world is essential for the prevention of related diseases, as well as ambient PM2.5 control. Following the framework and methodology of the Global Burden of Disease Study (GBD) in 2019, the global, regional, and national data on ambient PM2.5-attributable death and disability-adjusted life years (DALYs), and the age-standardized rates of mortality (ASMR) and disability-adjusted life years (ASDR) were summarized based on age, gender, year, location and specific diseases. We calculated the average annual percentage change (AAPC) to depict the secular trends of ASMR and ASDR from 1990 to 2019. In 2019, the global ambient PM2.5-related deaths and DALYs were 4,140,970 and 118.2 million, respectively, with 1,702,150 deaths and 47.5 million DALYs for females and 2,438,820 deaths and 70.7 million DALYs for male. In the 13 level-three causes, ischemic heart disease, stroke, chronic obstructive and pulmonary disease (COPD) were the leading three causes of deaths and DALYs attributable to ambient PM2.5. The number of global deaths and DALYs attributable to ambient PM2.5 has increased by 102.3% and 67.7% from 1990 to 2019, respectively. However, ASMR and ASDR showed little change. In the 13 level-three diseases, ischemic heart disease, stroke, COPD, diabetes mellitus, and lung cancer were the top five contributors to the increase of global deaths or DALYs, among which diabetes mellitus had the fastest increase of ASMR and ASDR, with AAPC of 1.5 (95% CI: 1.43, 1.58) and 2.21 (95% CI: 2.15, 2.27), respectively. The population attributable fractions (PAF) of causes in ASMR or ASDR varied significantly across regions, of which PAF of COPD, stroke and lung cancer were the top three. Regarding the GBD region, high PAF mainly occurred in North Africa and Middle East, South Asia, and East Asia. The age-specific PAFs of ischemic heart disease and stroke deaths and DALYs due to ambient PM2.5 were negatively correlated with age. ASMR and ASDR of overall PM2.5 related-burden showed an inverted “V/U” relationship with the socio-demographic index (SDI). The AAPC of ASMR and ASDR of the overall causes showed a strong negative correlation with SDI in 2019, especially at the SDI larger than 0.5. The deaths and DALYs attributable to ambient PM2.5 continued to increase under the context of population growth and aging. Decision-makers should consider controlling the PM2.5 emission when developing the economy.

Abstract The recent advances in sequencing technologies enable the assembly of individual genomes to the quality of the reference genome. How to integrate multiple genomes from the same species and make the integrated representation accessible to biologists remains an open challenge. Here, we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implement our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome.

Abstract The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions.

Abstract Background Closing gaps in draft genomes is an important post processing step in genome assembly. It leads to more complete genomes, which benefits downstream genome analysis such as annotation and genotyping. Several tools have been developed for gap closing. However, these tools don’t fully utilize the information contained in the sequence data. For example, while it is known that many gaps are caused by genomic repeats, existing tools often ignore many sequence reads that originate from a repeat-related gap. Results We compare GAPPadder with GapCloser, GapFiller and Sealer on one bacterial genome, human chromosome 14 and the human whole genome with paired-end and mate-paired reads with both short and long insert sizes. Empirical results show that GAPPadder can close more gaps than these existing tools. Besides closing gaps on draft genomes assembled only from short sequence reads, GAPPadder can also be used to close gaps for draft genomes assembled with long reads. We show GAPPadder can close gaps on the bed bug genome and the Asian sea bass genome that are assembled partially and fully with long reads respectively. We also show GAPPadder is efficient in both time and memory usage. Conclusion In this paper, we propose a new approach called GAPPadder for gap closing. The main advantage of GAPPadder is that it uses more information in sequence data for gap closing. In particular, GAPPadder finds and uses reads that originate from repeat-related gaps. We show that these repeat-associated reads are useful for gap closing, even though they are ignored by all existing tools. Other main features of GAPPadder include utilizing the information in sequence reads with different insert sizes and performing two-stage local assembly of gap sequences. The results show that our method can close more gaps than several existing tools. The software tool, GAPPadder, is available for download at https://github.com/Reedwarbler/GAPPadder.

Abstract Background While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping. Method In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection. Results Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets. Conclusions We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools.

Abstract Background Repeat elements are important components of most eukaryotic genomes. Most existing tools for repeat analysis rely either on high quality reference genomes or existing repeat libraries. Thus, it is still challenging to do repeat analysis for species with highly repetitive or complex genomes which often do not have good reference genomes or annotated repeat libraries. Recently we developed a computational method called REPdenovo that constructs consensus repeat sequences directly from short sequence reads, which outperforms an existing tool called RepARK. One major issue with REPdenovo is that it doesn’t perform well for repeats with relatively high divergence rates or low copy numbers. In this paper, we present an improved approach for constructing consensus repeats directly from short reads. Comparing with the original REPdenovo, the improved approach uses more repeat-related k-mers and improves repeat assembly quality using a consensus-based k-mer processing method. Results We compare the performance of the new method with REPdenovo and RepARK on Human, Arabidopsis thaliana and Drosophila melanogaster short sequencing data. And the new method fully constructs more repeats in Repbase than the original REPdenovo and RepARK, especially for repeats of higher divergence rates and lower copy number. We also apply our new method on Hummingbird data which doesn’t have a known repeat library, and it constructs many repeat elements that can be validated using PacBio long reads. Conclusion We propose an improved method for reconstructing repeat elements directly from short sequence reads. The results show that our new method can assemble more complete repeats than REPdenovo (and also RepARK). Our new approach has been implemented as part of the REPdenovo software package, which is available for download at https://github.com/Reedwarbler/REPdenovo.

Repeat elements are important components of eukaryotic genomes. One limitation in our understanding of repeat elements is that most analyses rely on reference genomes that are incomplete and often contain missing data in highly repetitive regions that are difficult to assemble. To overcome this problem we develop a new method, REPdenovo, which assembles repeat sequences directly from raw shotgun sequencing data. REPdenovo can construct various types of repeats that are highly repetitive and have low sequence divergence within copies. We show that REPdenovo is substantially better than existing methods both in terms of the number and the completeness of the repeat sequences that it recovers. The key advantage of REPdenovo is that it can reconstruct long repeats from sequence reads. We apply the method to human data and discover a number of potentially new repeats sequences that have been missed by previous repeat annotations. Many of these sequences are incorporated into various parasite genomes, possibly because the filtering process for host DNA involved in the sequencing of the parasite genomes failed to exclude the host derived repeat sequences. REPdenovo is a new powerful computational tool for annotating genomes and for addressing questions regarding the evolution of repeat families. The software tool, REPdenovo, is available for download at https://github.com/Reedwarbler/REPdenovo.

The endocytosis of transferrin receptor (TfR) has served as a model to study the receptor-targeted cargo delivery system for cancer therapy for many years. To accurately evaluate and optically measure this TfR targeting delivery in vitro, a CHO cell line with enhanced green fluorescent protein (EGFP)-tagged human TfR was established. A chimera of the hTfR and EGFP was engineered by fusing EGFP to the amino terminus of hTfR. Data were provided to demonstrate that hTfR-EGFP chimera was predominantly localized on the plasma membrane with some intracellular fluorescent structures on CHO cells and the EGFP moiety did not affect the endocytosis property of hTfR. Receptor internalization occurred similarly to that of HepG2 cells expressing wild-type hTfR. The internalization percentage of this chimeric receptor was about 81 ± 3% of wild type. Time-dependent co-localization of hTfR-EGFP and PE-conjugated anti-hTfR mAb in living cells demonstrated the trafficking of mAb-receptor complexes through the endosomes followed by segregation of part of the mAb and receptor at the late stages of endocytosis. The CHO-hTfR cells preferentially took up anti-hTfR mAb conjugated nanoparticles. This CHO-hTfR cell line makes it feasible for accurate evaluation and visualization of intracellular trafficking of therapeutic agents conjugated with transferrin or Abs targeting the hTfRs.

Insertions and deletions (indels) are important types of structural variations. Obtaining accurate genotypes of indels may facilitate further genetic study. There are a few existing methods for calling indel genotypes from sequence reads. However, none of these tools can accurately call indel genotypes for indels of all lengths, especially for low coverage sequence data. In this paper, we present GINDEL, an approach for calling genotypes of both insertions and deletions from sequence reads. GINDEL uses a machine learning approach which combines multiple features extracted from next generation sequencing data. We test our approach on both simulated and real data and compare with existing tools, including Genome STRiP, Pindel and Clever-sv. Results show that GINDEL works well for deletions larger than 50 bp on both high and low coverage data. Also, GINDEL performs well for insertion genotyping on both simulated and real data. For comparison, Genome STRiP performs less well for shorter deletions (50-200 bp) on both simulated and real sequence data from the 1000 Genomes Project. Clever-sv performs well for intermediate deletions (200-1500 bp) but is less accurate when coverage is low. Pindel only works well for high coverage data, but does not perform well at low coverage. To summarize, we show that GINDEL not only can call genotypes of insertions and deletions (both short and long) for high and low coverage population sequence data, but also is more accurate and efficient than other approaches. The program GINDEL can be downloaded at: http://sourceforge.net/p/gindel.

DWPI is a value-added patent data provided by Clarivate which is well-know content curated from a variety of invention perspectives. DWPI curation system has been maintained through categorized abstracts, unique patent family structure, assignee code, and technical field indexes, etc. while leveraging with data technologies, including AI. In general, the output from AI highly depends on the base model and content for its quality and reliability. Nowadays, while the use of generative AI is being explored in various domains, there are stronger needs for trusted, high-quality AI in the intellectual property field as well. Clarivate is forwarding to the direction to make it happen by integrating AI technologies and DWPI curation mechanisms built in the long history. [ABSTRACT FROM AUTHOR]