Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study.

This study explores the bidirectional association between multimorbidity and falls in Chinese middle-aged and elderly adults. Participants aged 45 and above from the China Health and Retirement Longitudinal Study were included. Binary logistic regression assessed the impact of chronic conditions on fall incidence (stage I), while multinomial logistic regression examined the relationship between baseline falls and multimorbidity (stage II). The fully adjusted odds ratios (ORs) for one, two, or three or more chronic conditions were 1.34, 1.65, and 2.02, respectively. Among participants without baseline falls, 28.61% developed two or more chronic conditions during follow-up, compared to 37.4% of those with a history of falls. Fully adjusted ORs for one, two, or three or more chronic conditions in those with a history of falls were 1.21, 1.38 and 1.70, respectively. The bidirectional relationship held in sensitivity and subgroup analyses. A bidirectional relationship exists between multimorbidity and falls in Chinese middle-aged and elderly adults. Strengthening chronic condition screening and treatment in primary healthcare may reduce falls risk, and prioritizing fall prevention and intervention in daily life is recommended.

Single-Cell Analysis Identifies NOTCH3-Mediated Interactions between Stromal Cells That Promote Microenvironment Remodeling and Invasion in Lung Adenocarcinoma.

Cancer immunotherapy has revolutionized the treatment of lung adenocarcinoma (LUAD); however, a significant proportion of patients do not respond. Recent transcriptomic studies to understand determinants of immunotherapy response have pinpointed stromal-mediated resistance mechanisms. To gain a better understanding of stromal biology at the cellular and molecular level in LUAD, we performed single-cell RNA sequencing of 256,379 cells, including 13,857 mesenchymal cells, from 9 treatment-naïve patients. Among the mesenchymal cell subsets, FAP+PDPN+ cancer-associated fibroblasts (CAF) and ACTA2+MCAM+ pericytes were enriched in tumors and differentiated from lung-resident fibroblasts. Imaging mass cytometry revealed that both subsets were topographically adjacent to the perivascular niche and had close spatial interactions with endothelial cells (EC). Modeling of ligand and receptor interactomes between mesenchymal and ECs identified that NOTCH signaling drives these cell-to-cell interactions in tumors, with pericytes and CAFs as the signal receivers and arterial and PLVAPhigh immature neovascular ECs as the signal senders. Either pharmacologically blocking NOTCH signaling or genetically depleting NOTCH3 levels in mesenchymal cells significantly reduced collagen production and suppressed cell invasion. Bulk RNA sequencing data demonstrated that NOTCH3 expression correlated with poor survival in stroma-rich patients and that a T cell-inflamed gene signature only predicted survival in patients with low NOTCH3. Collectively, this study provides valuable insights into the role of NOTCH3 in regulating tumor stroma biology, warranting further studies to elucidate the clinical implications of targeting NOTCH3 signaling. SIGNIFICANCE: NOTCH3 signaling activates tumor-associated mesenchymal cells, increases collagen production, and augments cell invasion in lung adenocarcinoma, suggesting its critical role in remodeling tumor stroma.

Nomogram incorporating log odds of positive lymph nodes improves prognostic prediction for ovarian serous carcinoma: a real-world retrospective cohort study.

OBJECTIVES: Ovarian serous carcinoma (OSC) is a major cause of gynaecological cancer death, yet there is a lack of reliable prognostic models. To address this, we developed and validated a nomogram based on conventional clinical characteristics and log odds of positive lymph nodes (LODDS) to predict the prognosis of OSC patients. SETTING: A Real-World Retrospective Cohort Study from the Surveillance, Epidemiology and End Results programme. PARTICIPANTS: We obtained data on 4192 patients diagnosed with OSC between 2010 and 2015. Eligibility criteria included specific diagnostic codes, OSC being the primary malignant tumour and age at diagnosis over 18 years. Exclusion criteria were missing information on various factors and unknown cause of death or survival time. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome were overall survival (OS) and ovarian cancer-specific survival (OCSS). RESULTS: For OS and OCSS outcomes, we selected 7 and 5 variables, respectively, to establish the nomogram. In the training and validation cohorts, the C index for OS or OCSS was 0.716 or 0.718 and 0.731 or 0.733, respectively, with a 3-year time-dependent area under the curve (AUC) of 0.745 or 0.751 and a 5-year time-dependent AUC of 0.742 or 0.751. Calibration curves demonstrated excellent consistency between predicted and observed outcomes. The Net Reclassification Index, integrated discrimination improvement and decision curve analysis curves indicated that our nomogram performed better than the International Federation of Gynaecology and Obstetrics (FIGO) staging system in predicting OS and OCSS for OSC patients in both the training and validation cohorts. CONCLUSION: Our nomogram, which includes LODDS, offers higher accuracy and reliability than the FIGO staging system and can predict overall and OCSS in OSC patients.

Comprehensive analysis and accurate quantification of unintended large gene modifications induced by CRISPR-Cas9 gene editing.

Most genome editing analyses to date are based on quantifying small insertions and deletions. Here, we show that CRISPR-Cas9 genome editing can induce large gene modifications, such as deletions, insertions, and complex local rearrangements in different primary cells and cell lines. We analyzed large deletion events in hematopoietic stem and progenitor cells (HSPCs) using different methods, including clonal genotyping, droplet digital polymerase chain reaction, single-molecule real-time sequencing with unique molecular identifier, and long-amplicon sequencing assay. Our results show that large deletions of up to several thousand bases occur with high frequencies at the Cas9 on-target cut sites on the HBB (11.7 to 35.4%), HBG (14.3%), and BCL11A (13.2%) genes in HSPCs and the PD-1 (15.2%) gene in T cells. Our findings have important implications to advancing genome editing technologies for treating human diseases, because unintended large gene modifications may persist, thus altering the biological functions and reducing the available therapeutic alleles.

The TRACE-Seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells.

Targeted DNA correction of disease-causing mutations in hematopoietic stem and progenitor cells (HSPCs) may enable the treatment of genetic diseases of the blood and immune system. It is now possible to correct mutations at high frequencies in HSPCs by combining CRISPR/Cas9 with homologous DNA donors. Because of the precision of gene correction, these approaches preclude clonal tracking of gene-targeted HSPCs. Here, we describe Tracking Recombination Alleles in Clonal Engraftment using sequencing (TRACE-Seq), a methodology that utilizes barcoded AAV6 donor template libraries, carrying in-frame silent mutations or semi-randomized nucleotides outside the coding region, to track the in vivo lineage contribution of gene-targeted HSPC clones. By targeting the HBB gene with an AAV6 donor template library consisting of ~20,000 possible unique exon 1 in-frame silent mutations, we track the hematopoietic reconstitution of HBB targeted myeloid-skewed, lymphoid-skewed, and balanced multi-lineage repopulating human HSPC clones in mice. We anticipate this methodology could potentially be used for HSPC clonal tracking of Cas9 RNP and AAV6-mediated gene targeting outcomes in translational and basic research settings.

Tools for experimental and computational analyses of off-target editing by programmable nucleases.

Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.

The prevalence of thyroid autoantibodies in autoimmune connective tissue diseases: a systematic review and meta-analysis.

AIM: Patients with autoimmune connective tissue disease (ACTD) may have anti-thyroid peroxidase antibody (TPOAb) and anti-thyroglobulin antibody (TgAb). This study aimed to compare the prevalence of thyroid autoantibodies in ACTD patients and controls. METHODS: All case-control studies published between 1980 and 2019 in English were searched from Medline, Embase, Web of Science, PubMed databases for meta-analysis and subgroup analyses. RESULTS: Total 10,321 ACTD cases and 12,949 healthy controls were included, and the prevalence of thyroid autoantibody positivity was higher in ACTD patients than in controls. Subgroup analysis revealed positive association between TgAb and ACTD in populations from all continents including European, Asian, African, and American. In addition, we found positive association between TgAb positivity and most ACTD cases including RA, SLE, pSS, and UCTD, positive association between TPOAb positivity and all ACTD cases including RA, SLE, pSS, SSc, and UCTD, and positive association between TPOAb positivity and ACTD in European, Asian, and African but not in American populations. CONCLUSION: Thyroid autoantibodies are more prevalent in ACTD patients than in healthy controls. It is important to screen patients with ACTD for the presence of thyroid autoimmunity, and perform thyroid function tests in clinical evaluation of ACTD patients.

Fine-mapping within eQTL credible intervals by expression CROP-seq.

The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes.

Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-map Regulatory Intervals.

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.

Prediction of influenza B vaccine effectiveness from sequence data.

Influenza is a contagious respiratory illness that causes significant human morbidity and mortality, affecting 5-15% of the population in a typical epidemic season. Human influenza epidemics are caused by types A and B, with roughly 25% of human cases due to influenza B. Influenza B is a single-stranded RNA virus with a high mutation rate, and both prior immune history and vaccination put significant pressure on the virus to evolve. Due to the high rate of viral evolution, the influenza B vaccine component of the annual influenza vaccine is updated, roughly every other year in recent years. To predict when an update to the vaccine is needed, an estimate of expected vaccine effectiveness against a range of viral strains is required. We here introduce a method to measure antigenic distance between the influenza B vaccine and circulating viral strains. The measure correlates well with effectiveness of the influenza B component of the annual vaccine in humans between 1979 and 2014. We discuss how this measure of antigenic distance may be used in the context of annual influenza vaccine design and prediction of vaccine effectiveness.

PGWD: Integrating Personal Genome for Warfarin Dosing.

Warfarin is a drug normally used in the prevention of thrombosis and the formation of blood clots. The dosage of warfarin is strongly affected by genetic variants of CYP2C9 and VKORC1 genes. Current technologies for detecting the variants of these genes are mainly based on real-time PCR. In recent years, due to the rapidly dropping cost of whole genome sequencing and genotyping, more and more people get their whole genome sequenced or genotyped. However, current software for warfarin dosing prediction is based on low-throughput genetic information from either real-time PCR or melting curve methods. There is no bioinformatics tool available that can take the high-throughput genome sequencing data as input and determine the accurate dosage of warfarin. Here, we present PGWD, a web tool that analyzes personal genome sequencing data and integrates with clinical information for warfarin dosing.

Virtual Pharmacist: A Platform for Pharmacogenomics.

We present Virtual Pharmacist, a web-based platform that takes common types of high-throughput data, namely microarray SNP genotyping data, FASTQ and Variant Call Format (VCF) files as inputs, and reports potential drug responses in terms of efficacy, dosage and toxicity at one glance. Batch submission facilitates multivariate analysis or data mining of targeted groups. Individual analysis consists of a report that is readily comprehensible to patients and practioners who have basic knowledge in pharmacology, a table that summarizes variants and potential affected drug response according to the US Food and Drug Administration pharmacogenomic biomarker labeled drug list and PharmGKB, and visualization of a gene-drug-target network. Group analysis provides the distribution of the variants and potential affected drug response of a target group, a sample-gene variant count table, and a sample-drug count table. Our analysis of genomes from the 1000 Genome Project underlines the potentially differential drug responses among different human populations. Even within the same population, the findings from Watson's genome highlight the importance of personalized medicine. Virtual Pharmacist can be accessed freely at http://www.sustc-genome.org.cn/vp or installed as a local web server. The codes and documentation are available at the GitHub repository (https://github.com/VirtualPharmacist/vp). Administrators can download the source codes to customize access settings for further development.

PGWD: integrating personal genome for warfarin dosing.

Warfarin is a drug normally used in the prevention of thrombosis and the formation of blood clots. The dosage of warfarin is strongly affected by genetic variants of CYP2C9 and VKORC1 genes. Current technologies for detecting the variants of these genes are mainly based on real-time PCR. In recent years, due to the rapidly dropping cost of whole genome sequencing and genotyping, more and more people get their whole genome sequenced or genotyped. However, current software for warfarin dosing prediction is based on low-throughput genetic information either from real-time PCR, or melting curve methods. There is no bioinformatics tool available that can take the high-throughput genome sequencing data as input and determine the accurate dosage of warfarin. Here, we present PGWD, a web tool that analyzes personal genome sequencing data, and integrates with clinical information for warfarin dosing.