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High-dimensional cytometry is a powerful technique for deciphering the immunopathological factors common to multiple individuals. However, rational comparisons of multiple batches of experiments performed on different occasions or at different sites are challenging because of batch effects. In this study, we describe the integration of multibatch cytometry datasets (iMUBAC), a flexible, scalable, and robust computational framework for unsupervised cell-type identification across multiple batches of highdimensional cytometry datasets, even without technical replicates. After overlaying cells from multiple healthy controls across batches, iMUBAC learns batch-specific cell-type classification boundaries and identifies aberrant immunophenotypes in patient samples from multiple batches in a unified manner. We illustrate unbiased and streamlined immunophenotyping using both public and in-house mass cytometry and spectral flow cytometry datasets. The method is available as the R package iMUBAC (https:// github.com/casanova-lab/iMUBAC).

The similarities and differences between trained immunity and other immune processes are the subject of intense interrogation. Therefore, a consensus on the definition of trained immunity in both in vitro and in vivo settings, as well as in experimental models and human subjects, is necessary for advancing this field of research. Here we aim to establish a common framework that describes the experimental standards for defining trained immunity.

The Fourth Annual Symposium on Hidradenitis Suppurativa (SHSA) took place on November 1-3, 2019, at the Westin Book Cadillac Hotel in Detroit, Michigan. This symposium was a joint meeting of the US Hidradenitis Suppurativa Foundation and the Canadian Hidradenitis Suppurativa Foundation. This cross-disciplinary meeting with experts from around the world was an opportunity to discuss the most recent advances in the study of hidradenitis suppurativa (HS) pathogenesis, clinical trials, classification, scoring systems, complementary/alternative medical treatments, diet, pain management, surgical and laser treatment, and ultrasonographic assessment. A special preconference workshop was held on the use of neodymium-doped yttrium-aluminum-garnet laser hair reduction, sinus tract deroofing, and carbon dioxide laser excision with ultrasonographic mapping and tumescent anesthesia for the treatment of HS. The focused workshops on establishing an HS clinic, setting up an HS support group, the Hidradenitis Suppurativa Prospective Observational Registry and Biospecimen Repository, and wound care were held during the meeting. A special program called HS Ambassadors was established for patients who may have questions about the conference presentations, and in addition, a meet and greet for patients and HS Ambassadors was arranged. To facilitate networking between those early in their careers and clinical and research experts, a mentoring reception was held.

Background: Dupilumab, the first biological drug to be approved for the treatment of moderate to severe atopic dermatitis in adolescents and adults, has shown good efficacy and safety in clinical trials. Objective: To evaluate real-world data on the efficacy and safety of dupilumab in atopic dermatitis. Methods: PubMed and EMBASE were searched for observational studies with data on efficacy, drug survival, and safety of dupilumab for the treatment of atopic dermatitis. Primary outcomes were mean percentage change in Eczema Area and Severity Index (EASI) score and proportion of atopic dermatitis patients achieving 50%, 75%, and 90% improvement in EASI score after dupilumab therapy. Results: Twenty-two unique studies encompassing 3303 atopic dermatitis patients were included. After 16 weeks of dupilumab therapy, the pooled proportion of patients achieving 50%, 75%, and 90% EASI score improvement was 85.1%, 59.8%, and 26.8%, respectively, and the weighted mean reduction in EASI score was 69.6%. Conjunctivitis was the most common adverse event, reported in a pooled proportion of 26.1%. Limitations: Limited data in terms of size and follow-up time were available. Conclusion: Real-world data show that dupilumab is a successful and well-tolerated therapy for atopic dermatitis, but ocular adverse events commonly occur. Registries are needed to monitor for adverse events.

Purpose of review The coronavirus disease 2019 (COVID-19) pandemic has caught the world unprepared, with no prevention or treatment strategies in place. In addition to the efforts to develop an effective vaccine, alternative approaches are essential to control this pandemic, which will most likely require multiple readily available solutions. Among them, monoclonal anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies have been isolated by multiple laboratories in record time facilitated by techniques that were first pioneered for HIV-1 antibody discovery. Here, we summarize how lessons learned from anti-HIV-1 antibody discovery have provided fundamental knowledge for the rapid development of anti-SARS-CoV-2 antibodies. Recent findings Research laboratories that successfully identified potent broadly neutralizing antibodies against HIV-1 have harnessed their antibody discovery techniques to isolate novel potent anti-SARS-CoV-2 antibodies, which have efficacy in animal models. These antibodies represent promising clinical candidates for treatment or prevention of COVID-19. Passive transfer of antibodies is a promising approach when the elicitation of protective immune responses is difficult, as in the case of HIV-1 infection. Antibodies can also play a significant role in post-exposure prophylaxis, in high-risk populations that may not mount robust immune responses after vaccination, and in therapy. We provide a review of the recent approaches used for anti-SARS-CoV-2 antibody discovery and upcoming challenges in the field.

Cell fate determination requires faithful execution of gene expression programs, which are increasingly recognized to respond to metabolic inputs. In particular, the family of alpha-ketoglutarate (alpha KG)-dependent dioxygenases, which include several chromatin-modifying enzymes, are emerging as key mediators of metabolic control of cell fate. alpha KG-dependent dioxygenases consume the metabolite alpha KG (also known as 2-oxoglutarate) as an obligate cosubstrate and are inhibited by succinate, fumarate, and 2-hydroxyglutarate. Here, we review the role of these metabolites in the control of dioxygenase activity and cell fate programs. We discuss the biochemical and transcriptional mechanisms enabling these metabolites to control cell fate and review evidence that nutrient availability shapes tissue-specific fate programs via alpha KG-dependent dioxygenases.

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment(1). The origins of specific compounds are known, including metabolites that are highly heritable(2,3), or those that are influenced by the gut microbiome(4), by lifestyle choices such as smoking(5), or by diet(6). However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites-in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts(7,8) that were not available to us when we trained the algorithms. We used feature attribution analysis(9) to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites. The levels of 1,251 metabolites are measured in 475 phenotyped individuals, and machine-learning algorithms reveal that diet and the microbiome are the determinants with the strongest predictive power for the levels of these metabolites.

Pillai & Meng (Pillai & Meng 2016 Ann. Stat.44, 2089-2097; p. 2091) speculated that 'the dependence among [random variables, rvs] can be overwhelmed by the heaviness of their marginal tails ..'. We give examples of statistical models that support this speculation. While under natural conditions the sample correlation of regularly varying (RV) rvs converges to a generally random limit, this limit is zero when the rvs are the reciprocals of powers greater than one of arbitrarily (but imperfectly) positively or negatively correlated normals. Surprisingly, the sample correlation of these RV rvs multiplied by the sample size has a limiting distribution on the negative half-line. We show that the asymptotic scaling of Taylor's Law (a power-law variance function) for RV rvs is, up to a constant, the same for independent and identically distributed observations as for reciprocals of powers greater than one of arbitrarily (but imperfectly) positively correlated normals, whether those powers are the same or different. The correlations and heterogeneity do not affect the asymptotic scaling. We analyse the sample kurtosis of heavy-tailed data similarly. We show that the least-squares estimator of the slope in a linear model with heavy-tailed predictor and noise unexpectedly converges much faster than when they have finite variances.

The development of neutralizing antibodies (NAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following infection or vaccination is likely to be critical for the development of sufficient population immunity to drive cessation of the coronavirus disease of 2019 (COVID-19) pandemic. A large number of serologic tests, platforms, and methodologies are being employed to determine seroprevalence in populations to select convalescent plasma samples for therapeutic trials and to guide policies about reopening. However, the tests have substantial variations in sensitivity and specificity, and their ability to quantitatively predict levels of NAbs is unknown. We collected 370 unique donors enrolled in the New York Blood Center Convalescent Plasma Program between April and May of 2020. We measured levels of antibodies in convalescent plasma samples using commercially available SARS-CoV-2 detection tests and in-house enzyme-linked immunosorbent assays (ELISAs) and correlated serological measurements with NAb activity measured using pseudotyped virus particles, which offer the most informative assessment of antiviral activity of patient sera against viral infection. Our data show that a large proportion of convalescent plasma samples have modest antibody levels and that commercially available tests have various degrees of accuracy in predicting NAb activity. We found that the Ortho anti-SARS-CoV-2 total Ig and IgG high-throughput serological assays (HTSAs) and the Abbott SARS-CoV-2 IgG assay quantify levels of antibodies that strongly correlate with the results of NAb assays and are consistent with gold standard ELISA results. These findings provide immediate clinical relevance to serology results that can be equated to NAb activity and could serve as a valuable roadmap to guide the choice and interpretation of serological tests for SARSCoV-2.