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Abstract Genomic profiling often fails to predict therapeutic outcomes in cancer. This failure is, in part, due to a myriad of genetic alterations and the plasticity of cancer signaling networks. Functional profiling, which ascertains signaling dynamics, is an alternative method to anticipate drug responses. It is unclear whether integrating genomic and functional features of solid tumours can provide unique insight into therapeutic vulnerabilities. We perform combined molecular and functional characterization, via BH3 profiling of the intrinsic apoptotic machinery, in glioma patient samples and derivative models. We identify that standard-of-care therapy rapidly rewires apoptotic signaling in a genotype-specific manner, revealing targetable apoptotic vulnerabilities in gliomas containing specific molecular features (e.g., TP53 WT). However, integration of BH3 profiling reveals high mitochondrial priming is also required to induce glioma apoptosis. Accordingly, a machine-learning approach identifies a composite molecular and functional signature that best predicts responses of diverse intracranial glioma models to standard-of-care therapies combined with ABBV-155, a clinical drug targeting intrinsic apoptosis. This work demonstrates how complementary functional and molecular data can robustly predict therapy-induced cell death.

Abstract Two correspondences raised concerns or comments about our analyses regarding exaggerated false positives found by differential expression (DE) methods. Here, we discuss the points they raise and explain why we agree or disagree with these points. We add new analysis to confirm that the Wilcoxon rank-sum test remains the most robust method compared to the other five DE methods (DESeq2, edgeR, limma-voom, dearseq, and NOISeq) in two-condition DE analyses after considering normalization and winsorization, the data preprocessing steps discussed in the two correspondences.

Abstract In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

Abstract Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP’s 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell’s 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

Abstract Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are neurodegenerative diseases that exist on a clinico-pathogenetic spectrum, designated ALS/FTD. The most common genetic cause of ALS/FTD is expansion of the intronic hexanucleotide repeat (GGGGCC) n in C9orf72. Here, we investigate the formation of nucleic acid secondary structures in these expansion repeats, and their role in generating condensates characteristic of ALS/FTD. We observe significant aggregation of the hexanucleotide sequence (GGGGCC) n , which we associate to the formation of multimolecular G-quadruplexes (mG4s) by using a range of biophysical techniques. Exposing the condensates to G4-unfolding conditions leads to prompt disassembly, highlighting the key role of mG4-formation in the condensation process. We further validate the biological relevance of our findings by detecting an increased prevalence of G4-structures in C9orf72 mutant human motor neurons when compared to healthy motor neurons by staining with a G4-selective fluorescent probe, revealing signal in putative condensates. Our findings strongly suggest that RNA G-rich repetitive sequences can form protein-free condensates sustained by multimolecular G-quadruplexes, highlighting their potential relevance as therapeutic targets for C9orf72 mutation-related ALS/FTD.

Abstract Benchmarking single-cell RNA-seq (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) computational tools demands simulators to generate realistic sequencing reads. However, none of the few read simulators aim to mimic real data. To fill this gap, we introduce scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that allows user-specified ground truths and generates synthetic sequencing reads (in a FASTQ or BAM file) by mimicking real data. At both read-sequence and read-count levels, scReadSim mimics real scRNA-seq and scATAC-seq data. Moreover, scReadSim provides ground truths, including unique molecular identifier (UMI) counts for scRNA-seq and open chromatin regions for scATAC-seq. In particular, scReadSim allows users to design cell-type-specific ground-truth open chromatin regions for scATAC-seq data generation. In benchmark applications of scReadSim, we show that UMI-tools achieves the top accuracy in scRNA-seq UMI deduplication, and HMMRATAC and MACS3 achieve the top performance in scATAC-seq peak calling.

Health Canada approved pembrolizumab in the first-line setting for advanced non-small-cell lung cancer with PD-L1 ≥ 50% and no EGFR/ALK aberration. The keynote 024 trial showed 55% of such patients progress with pembrolizumab monotherapy. We propose that the combination of baseline CT and clinical factors can help identify those patients who may progress. In 138 eligible patients from our institution, we retrospectively collected their baseline variables, including baseline CT findings (primary lung tumor size and metastatic site), smoking pack years, performance status, tumor pathology, and demographics. The treatment response was assessed via RECIST 1.1 using the baseline and first follow-up CT. Associations between the baseline variables and progressive disease (PD) were tested by logistic regression analyses. The results showed 46/138 patients had PD. The baseline CT “number of involved organs” by metastasis and smoking pack years were independently associated with PD (p < 0.05), and the ROC analysis showed a good performance of the model that integrated these variables in predicting PD (AUC: 0.79). This pilot study suggests that the combination of baseline CT disease and smoking PY can identify who may progress on pembrolizumab monotherapy and can potentially facilitate decision-making for the optimal first-line treatment in the high PD-L1 cohort.

Abstract Background Methylation of the p16 promoter resulting in epigenetic gene silencing—known as p16 epimutation—is frequently found in human colorectal cancer and is also common in normal-appearing colonic mucosa of aging individuals. Thus, to improve clinical care of colorectal cancer (CRC) patients, we explored the role of age-related p16 epimutation in intestinal tumorigenesis. Methods We established a mouse model that replicates two common genetic and epigenetic events observed in human CRCs: Apc mutation and p16 epimutation. We conducted long-term survival and histological analysis of tumor development and progression. Colonic epithelial cells and tumors were collected from mice and analyzed by RNA sequencing (RNA-seq), quantitative PCR, and flow cytometry. We performed single-cell RNA sequencing (scRNA-seq) to characterize tumor-infiltrating immune cells throughout tumor progression. We tested whether anti-PD-L1 immunotherapy affects overall survival of tumor-bearing mice and whether inhibition of both epigenetic regulation and immune checkpoint is more efficacious. Results Mice carrying combined Apc mutation and p16 epimutation had significantly shortened survival and increased tumor growth compared to those with Apc mutation only. Intriguingly, colon tumors with p16 epimutation exhibited an activated interferon pathway, increased expression of programmed death-ligand 1 (Pdl1), and enhanced infiltration of immune cells. scRNA-seq further revealed the presence of Foxp3 + Tregs and γδT17 cells, which contribute to an immunosuppressive tumor microenvironment (TME). Furthermore, we showed that a combined therapy using an inhibitor of DNA methylation and a PD-L1 immune checkpoint inhibitor is more effective for improving survival in tumor-bearing mice than blockade of either pathway alone. Conclusions Our study demonstrated that age-dependent p16 epimutation creates a permissive microenvironment for malignant transformation of polyps to colon cancer. Our findings provide a mechanistic rationale for future targeted therapy in patients with p16 epimutation.

Antibodies directed against DNA in systemic lupus erythematosus are functionally diverse. This study demonstrates that DNAse1L3 is the primary target of a subset of autoantibodies previously considered specific for double-stranded DNA.

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

Abstract Background Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. Results Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use. Conclusions To help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.

Macroscelidid afrotherians and canid carnivorans possess four premolar loci, the first of which is not replaced. Previous work suggests that the first premolar in macroscelidids is a retained deciduous tooth, but in Canis it is a successional tooth with no milk precursor. We tested this contrasting interpretation of first premolar homology with data from ontogenetic anatomy and with area predictions from the inhibitory cascade (IC) model. Our results based on anatomy support previous interpretations that the functional first premolar is a retained deciduous tooth (dp1) with no successor in macroscelidids, and a successional tooth (p1) with no precursor in Canis. Hyracoids are among the few placental mammals that show replacement at the first premolar locus and show less deviation than other taxa of actual from predicted areas across the deciduous and molar toothrow. However, predicted vs. actual tooth areas can depart substantially from one another. At least without a better means of representing tooth size, the inhibitory cascade does not help to distinguish the deciduous from successional first premolar. This observation does not rule out the possibility that factors such as a size-shift within the toothrow (e.g., carnivoran carnassials) help to explain deviations from the inhibitory cascade model.

Abstract Background Understanding of the role of children in COVID-19 transmission has significant implications for school and childcare policies, as well as appropriate targeting of vaccine campaigns. The objective of this systematic review was to identify the role of children in SARS-CoV-2 transmission to other children and adults. Methods MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and Web of Science were electronically searched for articles published before March 31, 2021. Studies of child-to-child and child-to-adult transmission and quantified the incidence of index and resulting secondary attack rates of children and adults in schools, households, and other congregate pediatric settings were identified. All articles describing confirmed transmission of SARS-CoV-2 from a child were included. PRISMA guidelines for data abstraction were followed, with each step conducted by two reviewers. Results 40 of 6110 articles identified met inclusion criteria. Overall, there were 0.8 secondary cases per primary index case, with a secondary attack rate of 8.4% among known contacts. The secondary attack rate was 26.4% among adult contacts versus 5.7% amongst child contacts. The pooled estimate of a contact of a pediatric index case being infected as secondary case was 0.10 (95% CI 0.03-0.25). Conclusions Children transmit COVID-19 at a lower rate to children than to adults. Household adults are at highest risk of transmission from an infected child, more so than adults or children in other settings.

Abstract When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

Abstract Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

Abstract High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

The global pattern of the mammalian methylome is formed by changes in methylation and demethylation. Here the authors describe a metric methylation concurrence that measures the ratio of unmethylated CpGs inside the partially methylated reads and show that methylation concurrence is associated with epigenetically regulated tumour suppressor genes.

The origin and mechanisms of autoantigen generation in systemic lupus erythematosus (SLE) are poorly understood. Here, we identified SLE neutrophils activated in vivo by IFN as a prominent source of Ro52, also known as tripartite motif–containing protein 21 (TRIM21), a critical autoantigen historically thought to be primarily generated by keratinocytes in SLE. Different from mononuclear cells and keratinocytes, SLE neutrophils are enriched in several unique Ro52 species containing a core sequence encoded by exon 4 (Ro52Ex4) in TRIM21. Ro52Ex4 is the main target of anti-Ro52 antibodies and is found in 2 Ro52 variants (Ro52α and an isoform termed Ro52γ) upregulated in SLE neutrophils. Further analysis of Ro52γ revealed a subset of autoantibodies against a unique C-terminal domain (Ro52γCT) generated from a frameshift due to the lack of exon 6 in Ro52γ. Antibodies to Ro52Ex4 and Ro52γCT distinguish SLE patient subsets characterized by distinct clinical, laboratory, treatment, and transcriptional profiles that are not discerned by the “classical” anti-Ro52 antibodies. These studies uncover IFN-activated neutrophils as a key source of unique immunogenic forms of Ro52 in SLE. Moreover, the finding of Ro52Ex4 and Ro52γCT as core targets of anti-Ro52 antibodies focus interest on Ro52γ as the potential isoform toward which immunological tolerance is initially lost in SLE.

Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge.

Abstract A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data—mbImpute—to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Background: Emergency departments (EDs) are complex, sociotechnical, high-paced, safety-critical work systems that have been disproportionately affected by the COVID-19 pandemic. Despite training, consistent compliance with recommended PPE use during COVID-19 pandemic has been challenging. Healthcare workers (HCWs) have had adapt to overcome these challenges to ensure their own safety and patient safety. We sought to identify barriers in the work system that impede the recommended COVID-19 PPE use in EDs. Methods: We conducted semistructured, in-depth interviews over ZoomTM from August 2020–May 2021 with 45 HCWs from the ED (ie, physicians, nurses, ancillary support staff, etc) affiliated with a large, tertiary-care, academic medical center. These audio-recorded interviews were transcribed and analyzed using a hybrid (inductive and deductive) qualitative coding approach in NVivo software. The deductive portion was guided by the SEIPS work system model, a well-known human-factors conceptual framework. Results: We identified multiple work-system factors in the ED that impede compliance with the recommended COVID-19 PPE use. In addition, ED HCWs have reported making a variety of adaptations or developing strategies to overcome these barriers. Some of these adaptations were made to the PPE physically (eg, trimming portions of PPE), and others were related to the tasks and/or processes associated with PPE, such as filming their own training video demonstrating PPE donning and doffing techniques, and environment services staff checking a patient’s status with nurses prior to entering the patient’s room when there was no COVID-19 signage on the door. Conclusions: Consistent compliance with COVID-19 PPE use in ED clinical practice is challenging and can be negatively affected by a variety of work system factors. Resilience strategies developed by HCWs can provide critical information with regards to HCW needs and potential directions for innovation. Future efforts should focus on not only changing individual HCW behavior through training but also on improving the PPE and ED work system design.

Abstract A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

Abstract To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Abstract Background Epidemiologic data suggest that patients with systemic lupus erythematosus (SLE) have a lower risk of breast cancer than women in the general population. In light of mechanistic studies suggesting that anti-DNA antibodies have anti-cancer effects, we sought to examine breast cancer risk in autoantibody strata in a well-characterized SLE cohort. Methods SLE patients without a cancer diagnosis prior to entry in the Hopkins Lupus Cohort were studied (N = 2431). Overall and site-specific cancer incidence was calculated in racial strata and compared with the US Surveillance, Epidemiology and End Results (SEER) registry. Breast cancer incidence was further examined in autoantibody subsets. Patients were considered positive for an autoantibody if they were ever positive for a specificity during their disease course. Results Patients with SLE had a 37% lower risk of breast cancer (SIR 0.63, 95% CI 0.39–0.95). The risk of HPV-associated cancers (SIR 4.39, 95% CI 2.87–6.44) and thyroid cancer (SIR 2.27, 95% CI 1.04–4.30) was increased. Cancer risk varied by race, with breast cancer protection occurring in non-African Americans (SIR 0.29, 95% CI 0.11–0.63) and the increased risk of HPV-associated cancers occurring in African Americans (SIR 7.23, 95% CI 4.35–11.3). Breast cancer risk was decreased in patients ever positive for anti-dsDNA (SIR 0.55, 95% CI 0.29–0.96), anti-La (SIR 0.00, 95% CI 0.00–0.78), and lupus anticoagulant (SIR 0.37, 95% CI 0.10–0.94). Patients who were positive for fewer (0–2) SLE autoantibodies did not have a lower risk of breast cancer (SIR 0.84, 95% CI 0.47–1.39), but patients with 3+ autoantibodies had a 59% decreased risk (SIR 0.41, 95% CI 0.16–0.84). Conclusions Positivity for multiple SLE autoantibodies was associated with a lower risk of breast cancer, supporting the hypothesis that a highly diversified immune response may exert an anti-cancer effect against some cancers. Validation of racial differences in cancer risk in SLE is required to determine whether cancer screening strategies should be targeted to racial subgroups.

Here, the authors introduce Cell Heterogeneity–Adjusted cLonal Methylation (CHALM) as a methylation quantification method that considers the heterogeneity of sequenced bulk cells. They apply CHALM to methylation datasets to detect differentially methylated genes that exhibit distinct biological functions supporting underlying mechanisms.

Background: Nucleic acid binding proteins are frequently targeted as autoantigens in systemic lupus erythematosus (SLE) and other interferon (IFN)-linked rheumatic diseases. The AIM-like receptors (ALRs) are IFN-inducible innate sensors that form supramolecular assemblies along double-stranded (ds)DNA of various origins. Here, we investigate the ALR absent in melanoma 2 (AIM2) as a novel autoantigen in SLE, with similar properties to the established ALR autoantigen interferon-inducible protein 16 (IFI16). We examined neutrophil extracellular traps (NETs) as DNA scaffolds on which these antigens might interact in a pro-immune context. Methods: AIM2 autoantibodies were measured by immunoprecipitation in SLE and control subjects. Neutrophil extracellular traps were induced in control neutrophils and combined with purified ALR proteins in immunofluorescence and DNase protection assays. SLE renal tissues were examined for ALR-containing NETs by confocal microscopy. Results: AIM2 autoantibodies were detected in 41/131 (31.3%) SLE patients and 2/49 (4.1%) controls. Our SLE cohort revealed a frequent co-occurrence of anti-AIM2, anti-IFI16, and anti-DNA antibodies, and higher clinical measures of disease activity in patients positive for antibodies against these ALRs. We found that both ALRs bind NETs in vitro and in SLE renal tissues. We demonstrate that ALR binding causes NETs to resist degradation by DNase I, suggesting a mechanism whereby extracellular ALR-NET interactions may promote sustained IFN signaling. Conclusions: Our work suggests that extracellular ALRs bind NETs, leading to DNase resistant nucleoprotein fibers that are targeted as autoantigens in SLE. Funding: These studies were funded by NIH R01 DE12354 (AR), P30 AR070254, R01 GM 129342 (JS), K23AR075898 (CM), K08AR077100 (BA), the Jerome L. Greene Foundation and the Rheumatology Research Foundation. Dr. Antiochos and Dr. Mecoli are Jerome L. Greene Scholars. The Hopkins Lupus Cohort is supported by NIH grant R01 AR069572. Confocal imaging performed at the Johns Hopkins Microscopy Facility was supported by NIH Grant S10 OD016374.

Heterogeneous helper T (Th) cell responses contribute to differential susceptibility to immunological disorders. Here the authors perform haplotype-based computational screens of 16 inbred mouse strains to identify a transcription factor, p73, as an important negative regulator of Th1 differentiation, with p73 deficient mice manifesting alterations in two inflammatory disease models.

For many people seeking abortion during the continuing COVID-19 pandemic, telemedicine abortion is the safest and most acceptable method, posing lower risk of exposure to the virus. In addition, by reducing in-person visits with health care providers, increased use of telemedicine for abortion can reduce pressure on overburdened health systems. Given the benefits of telemedicine during the pandemic, government agencies in several countries took measures to temporarily allow telemedicine abortion. We conducted key-word English-language searches to identify examples of government action to remove regulatory barriers to the practice of telemedicine abortion in response to the pandemic. We found instances of government agencies in eight countries taking steps to ease regulatory barriers to telemedicine abortion. Telemedicine abortion is safe, cost-effective, and may be the preferred method of abortion during acute periods of COVID-19 transmission, as well as after the pandemic has abated. As one step to expanding access to abortion with medicine where abortion is legal, health agencies and other regulatory bodies can take steps to remove barriers specific to telemedicine abortion.

Objectives In lupus nephritis the pathological diagnosis from tissue retrieved during kidney biopsy drives treatment and management. Despite recent approval of new drugs, complete remission rates remain well under aspirational levels, necessitating identification of new therapeutic targets by greater dissection of the pathways to tissue inflammation and injury. This study assessed the safety of kidney biopsies in patients with SLE enrolled in the Accelerating Medicines Partnership, a consortium formed to molecularly deconstruct nephritis.Methods 475 patients with SLE across 15 clinical sites in the USA consented to obtain tissue for research purposes during a clinically indicated kidney biopsy. Adverse events (AEs) were documented for 30 days following the procedure and were determined to be related or unrelated by all site investigators. Serious AEs were defined according to the National Institutes of Health reporting guidelines.Results 34 patients (7.2%) experienced a procedure-related AE: 30 with haematoma, 2 with jets, 1 with pain and 1 with an arteriovenous fistula. Eighteen (3.8%) experienced a serious AE requiring hospitalisation; four patients (0.8%) required a blood transfusion related to the kidney biopsy. At one site where the number of cores retrieved during the biopsy was recorded, the mean was 3.4 for those who experienced a related AE (n=9) and 3.07 for those who did not experience any AE (n=140). All related AEs resolved.Conclusions Procurement of research tissue should be considered feasible, accompanied by a complication risk likely no greater than that incurred for standard clinical purposes. In the quest for targeted treatments personalised based on molecular findings, enhanced diagnostics beyond histology will likely be required.

Summary: The existence of doublets is a key confounder in single-cell RNA sequencing (scRNA-seq) data analysis. Computational techniques have been developed for detecting doublets from scRNA-seq data. We developed an R package DoubletCollection to integrate the installation and execution of eight doublet detection methods. DoubletCollection provides a unified interface to perform and visualize downstream analysis after doublet detection. Here, we present a protocol of using DoubletCollection to benchmark doublet-detection methods. This protocol can accommodate new doublet-detection methods in the fast-growing scRNA-seq field.For details on the use and execution of this protocol, please refer to Xi and Li (2020).

Background and aims We hypothesised that intracellular homocysteine and homocysteine metabolite levels in patients with SLE are disproportionately elevated compared with the levels seen in healthy subjects and that they are independently associated with coronary plaque in SLE.Methods A liquid chromatography–tandem mass spectrometry absolute quantification assay was used for the determination of six analytes in both plasma and peripheral blood mononuclear cells (PBMCs): homocysteine (Hcy), S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), methionine (Met), cystathionine (Cysta) and 5-methyltetrahydrofolate (5m-THF). We then compared intracellular (PBMC) and extracellular (plasma) Hcy and Hcy metabolite (SAM, SAH, Met, Cysta and 5m-THF) concentrations in 10 patients with SLE and in 10 age, sex and ethnicity matched controls. Subjects with a history of diabetes mellitus, cardiovascular disease, hypertension, alcohol consumption in excess of 3 units per day, anaemia, renal insufficiency (serum creatinine >1.5 mg/dL) and pregnancy were excluded. All patients with SLE had two coronary CT angiography studies as screening for occult coronary atherosclerotic disease.Results Plasma from patients with SLE had higher levels of Hcy (p

The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.

Abstract Background Dysfunctional autophagy is implicated in Alzheimer’s Disease (AD) pathogenesis. The alterations in the expression of many autophagy related genes (ATGs) have been reported in AD brains; however, the disparity of the changes confounds the role of autophagy in AD. Methods To further understand the autophagy alteration in AD brains, we analyzed transcriptomic (RNAseq) datasets of several brain regions (BA10, BA22, BA36 and BA44 in 223 patients compared to 59 healthy controls) and measured the expression of 130 ATGs. We used autophagy-deficient mouse models to assess the impact of the identified ATGs depletion on memory, autophagic activity and amyloid-β (Aβ) production. Results We observed significant downregulation of multiple components of two autophagy kinase complexes BECN1-PIK3C3 and ULK1/2-FIP200 specifically in the parahippocampal gyrus (BA36). Most importantly, we demonstrated that deletion of NRBF2, a component of the BECN1-PIK3C3 complex, which also associates with ULK1/2-FIP200 complex, impairs memory in mice, alters long-term potentiation (LTP), reduces autophagy in mouse hippocampus, and promotes Aβ accumulation. Furthermore, AAV-mediated NRBF2 overexpression in the hippocampus not only rescues the impaired autophagy and memory deficits in NRBF2-depleted mice, but also reduces β-amyloid levels and improves memory in an AD mouse model. Conclusions Our data not only implicates NRBF2 deficiency as a risk factor for cognitive impairment associated with AD, but also support the idea of NRBF2 as a potential therapeutic target for AD.

Abstract Background General translational cis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements include mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate to control translation rates are not well understood. Results Here, we show that these sequence features specify 42–81% of the variance in translation rates in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Mus musculus, and Homo sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25–60 nucleotide segments within mRNA 5′ regions, that changes in tri-nucleotide frequencies between highly and poorly translated 5′ regions are correlated between all species, and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA. Conclusions Our work shows that general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that directs translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and within cis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell.

Objective Retinoblastoma (Rb) protein is a nuclear protein with several important functions, including the ability to stabilize heterochromatin. Because antibodies against the nucleosome and chromatin are key in systemic lupus erythematosus (SLE), we sought to determine whether Rb was an autoantigen in SLE and to evaluate any associated clinical phenotypes. Methods Sera from 222 patients with SLE from the Hopkins longitudinal cohort were studied. Additional cohorts tested included sera from 100 patients with primary Sjögren syndrome (pSS) (disease controls) evaluated at the Johns Hopkins Jerome L. Greene Sjögren's Center and sera from 36 healthy individuals. Anti‐Rb antibodies were assayed by immunoprecipitation of 35S‐methionine–labeled Rb, which was generated by in vitro transcription/translation. Fisher's exact test was used for the univariate analysis. Multivariable exact logistic regression was used to model for the presence of proteinuria in patients with SLE. Results Anti‐Rb antibodies were present in 15 of 222 (6.8%) patients with SLE, in 3 of 100 patients with pSS (3%), and in 0 of 36 healthy individuals. Among patients with SLE, Rb antibodies were strongly negatively associated with proteinuria (P = 0.0031), renal involvement (odds ratio [OR] = 0.11; P = 0.01), and anemia (OR = 0.05; P < 0.0001) and were positively associated with stroke (OR = 7.65; P = 0.05). The negative association with lupus nephritis held true in multivariate models (adjusted OR = 0.11; P = 0.01). Conclusion Anti‐Rb antibodies are a novel specificity not previously described in SLE. These new data define a possible SLE subset that is protected against renal involvement, is positively associated with stroke, and is not associated with antiphospholipid antibodies.

Background Positive remodelling index and presence of low-attenuation non-calcified plaque (LANCP) are characteristic vessel changes in unstable coronary plaques. We sought to characterise these high-risk plaque features in patients with systemic lupus erythematosus (SLE) and to compare them with controls.Methods A total of 72 patients who satisfied the SLICC classification criteria for SLE had coronary CT angiography (CCTA) studies, 30 of which had follow-up CCTA, as screening for occult coronary atherosclerotic disease in asymptomatic individuals. A total of 100 consecutive controls with no known history of lupus, heart disease or revascularisation who had two coronary CT angiograms at least 1 year apart were included in the study. These were asymptomatic patients referred by their primary physicians for screening of coronary artery disease and the screening interval was decided by the primary physicians. The methodology for image acquisition was identical.Results LANCP burden at baseline was significantly greater in patients with SLE compared with controls. LANCP volume was significantly greater in patients over 60 years of age (p10 mg/day. LANCP burden remained stable over follow-up. There were no significant differences in remodelling index compared with controls.Conclusion This is the first study describing high-risk CCTA features of coronary plaque in patients with SLE. Both LANCP and positive remodelling are common in SLE. These characteristic vessel changes may identify patients with SLE at increased risk of cardiovascular events and those in need for more frequent cardiac monitoring.

The T cell-dependent bispecific (TDB) antibody, anti-CD79b/CD3, targets CD79b and CD3 cell-surface receptors expressed on B cells and T cells, respectively. Since the anti-CD79b arm of this TDB binds only to human CD79b, a surrogate TDB that binds to cynomolgus monkey CD79b (cyCD79b) was used for preclinical characterization. To evaluate the impact of CD3 binding affinity on the TDB pharmacokinetics (PK), we utilized non-tumor-targeting bispecific anti-gD/CD3 antibodies composed of a low/high CD3 affinity arm along with a monospecific anti-gD arm as controls in monkeys and mice. An integrated PKPD model was developed to characterize PK and pharmacodynamics (PD). This study revealed the impact of CD3 binding affinity on anti-cyCD79b/CD3 PK. The surrogate anti-cyCD79b/CD3 TDB was highly effective in killing CD79b-expressing B cells and exhibited nonlinear PK in monkeys, consistent with target-mediated clearance. A dose-dependent decrease in B cell counts in peripheral blood was observed, as expected. Modeling indicated that anti-cyCD79b/CD3 TDB’s rapid and target-mediated clearance may be attributed to faster internalization of CD79b, in addition to enhanced CD3 binding. The model yielded unbiased and precise curve fits. These findings highlight the complex interaction between TDBs and their targets and may be applicable to the development of other biotherapeutics.