Preharvest UV-C Hormesis Induces Key Genes Associated With Homeostasis, Growth and Defense in Lettuce Inoculated With Xanthomonas campestris pv. vitians

Preharvest application of hormetic doses of ultraviolet-C (UV-C) generates beneficial effects in plants. In this study, within 1 week, four UV-C treatments of 0.4 kJ/m2 were applied to 3-week-old lettuce seedlings. The leaves were inoculated with a virulent strain of Xanthomonas campestris pv. vitians (Xcv) 48 h after the last UV-C application. The extent of the disease was tracked over time and a transcriptomic analysis was performed on lettuce leaf samples. Samples of lettuce leaves, from both control and treated groups, were taken at two different times corresponding to T2, 48 h after the last UV-C treatment and T3, 24 h after inoculation (i.e., 72 h after the last UV-C treatment). A significant decrease in disease severity between the UV-C treated lettuce and the control was observed on days 4, 8, and 14 after pathogen inoculation. Data from the transcriptomic study revealed, that in response to the effect of UV-C alone and/or UV-C + Xcv, a total of 3828 genes were differentially regulated with fold change (|log2-FC|) > 1.5 and false discovery rate (FDR) < 0.05. Among these, of the 2270 genes of known function 1556 were upregulated and 714 were downregulated. A total of 10 candidate genes were verified by qPCR and were generally consistent with the transcriptomic results. The differentially expressed genes observed in lettuce under the conditions of the present study were associated with 14 different biological processes in the plant. These genes are involved in a series of metabolic pathways associated with the ability of lettuce treated with hormetic doses of UV-C to resume normal growth and to defend themselves against potential stressors. The results indicate that the hormetic dose of UV-C applied preharvest on lettuce in this study, can be considered as an eustress that does not interfere with the ability of the treated plants to carry on a set of key physiological processes namely: homeostasis, growth and defense.

Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

We have developed a scalable system that leverages protein nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle protein interface, driven by the relationship between protein-NP affinity and protein abundance. Here we demonstrate the importance of tuning the protein to NP surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 to 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.

False Discovery in A/B Testing

We investigate what fraction of all significant results in website A/B testing is actually null effects (i.e., the false discovery rate (FDR)). Our data consist of 4,964 effects from 2,766 experiments conducted on a commercial A/B testing platform. Using three different methods, we find that the FDR ranges between 28% and 37% for tests conducted at 10% significance and between 18% and 25% for tests at 5% significance (two sided). These high FDRs stem mostly from the high fraction of true null effects, about 70%, rather than from low power. Using our estimates, we also assess the potential of various A/B test designs to reduce the FDR. The two main implications are that decision makers should expect one in five interventions achieving significance at 5% confidence to be ineffective when deployed in the field and that analysts should consider using two-stage designs with multiple variations rather than basic A/B tests. This paper was accepted by Eric Anderson, marketing.

Testing Mediation Effects in High-Dimensional Microbiome Data with False Discovery Rate Control

Background: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null (no exposure-microbe association, no microbe-outcome association given the exposure, or neither), most existing methods for the global test such as MedTest and MODIMA treat the microbes as if they are all under the same type of null. Results: We propose a new approach based on inverse regression that regresses the (possibly transformed) relative abundance of each taxon on the exposure and the exposure-adjusted outcome to assess the exposure-taxon and taxon-outcome associations simultaneously. Then the association p-values are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method is implemented in the LDM and enjoys all the features of the LDM, i.e., allowing an arbitrary number of taxa to be tested, supporting continuous, discrete, or multivariate exposures and outcomes as well as adjustment of confounding covariates, accommodating clustered data, and offering analysis at the relative abundance or presence-absence scale. We refer to this new method as LDM-med. Using extensive simulations, we showed that LDM-med always controlled the type I error of the global test and had compelling power over existing methods; LDM-med always preserved the FDR of testing individual taxa and had much better sensitivity than alternative approaches. In contrast, MedTest and MODIMA had severely inflated type I error when different taxa were under different types of null. The flexibility of LDM-med for a variety of mediation analyses is illustrated by the application to a murine microbiome dataset, which identified a plausible mediator.Conclusions: Inverse regression coupled with the LDM is a strategy that performs well and is capable of handling mediation analysis in a wide variety of microbiome studies.

Dual UMIs and Dual Barcodes With Minimal PCR Amplification Removes Artifacts and Acquires Accurate Antibody Repertoire

Antibody repertoire sequencing (Rep-seq) has been widely used to reveal repertoire dynamics and to interrogate antibodies of interest at single nucleotide-level resolution. However, polymerase chain reaction (PCR) amplification introduces extensive artifacts including chimeras and nucleotide errors, leading to false discovery of antibodies and incorrect assessment of somatic hypermutations (SHMs) which subsequently mislead downstream investigations. Here, a novel approach named DUMPArts, which improves the accuracy of antibody repertoires by labeling each sample with dual barcodes and each molecule with dual unique molecular identifiers (UMIs) via minimal PCR amplification to remove artifacts, is developed. Tested by ultra-deep Rep-seq data, DUMPArts removed inter-sample chimeras, which cause artifactual shared clones and constitute approximately 15% of reads in the library, as well as intra-sample chimeras with erroneous SHMs and constituting approximately 20% of the reads, and corrected base errors and amplification biases by consensus building. The removal of these artifacts will provide an accurate assessment of antibody repertoires and benefit related studies, especially mAb discovery and antibody-guided vaccine design.

Immune-Proteome Profiling in Classical Hodgkin Lymphoma Tumor Diagnostic Tissue

In classical Hodgkin Lymphoma (cHL), immunoediting via protein signaling is key to evading tumor surveillance. We aimed to identify immune-related proteins that distinguish diagnostic cHL tissues (=diagnostic tumor lysates, n = 27) from control tissues (reactive lymph node lysates, n = 30). Further, we correlated our findings with the proteome plasma profile between cHL patients (n = 26) and healthy controls (n = 27). We used the proximity extension assay (PEA) with the OlinkTM multiplex Immuno-Oncology panel, consisting of 92 proteins. Univariate, multivariate-adjusted analysis and Benjamini–Hochberg’s false discovery testing (=Padj) were performed to detect significant discrepancies. Proteins distinguishing cHL cases from controls were more numerous in plasma (30 proteins) than tissue (17 proteins), all Padj < 0.05. Eight of the identified proteins in cHL tissue (PD-L1, IL-6, CCL17, CCL3, IL-13, MMP12, TNFRS4, and LAG3) were elevated in both cHL tissues and cHL plasma compared with control samples. Six proteins distinguishing cHL tissues from controls tissues were significantly correlated to PD-L1 expression in cHL tissue (IL-6, MCP-2, CCL3, CCL4, GZMB, and IFN-gamma, all p ≤0.05). In conclusion, this study introduces a distinguishing proteomic profile in cHL tissue and potential immune-related markers of pathophysiological relevance.

Códigos da classificação internacional de doenças como rastreadores de eventos adversos a medicamentos

Introdução: Os Eventos Adversos a Medicamentos-EAM representam riscos à saúde e sua subnotificação representa um desafio para a saúde pública. A busca ativa de casos suspeitos de EAM nos bancos de dados de saúde utilizando a Classificação Internacional de Doenças-CID é uma das estratégias que pode reduzir as subnotificações desses eventos. Objetivo:O objetivo desse estudo é identificar os códigos da CID mais usados como rastreadores de EAM e avaliar a sua concordância entre os pesquisadores. Métodos: Foi realizada uma revisão sistemática da literatura utilizando as bases de dados PubMed, Scopus, Web of Science, MEDLINE e LILACS com os descritores “Classificação Internacional de Doenças”, “CID-10”, “Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos”, “Envenenamento”, “Erros de Medicação”. Os artigos incluídos tiveram seus códigos CID identificados, comparados e sua qualidade avaliada. A análise de concordância dos códigos foi feita usando o modelo de ensaios de Bernoulli, testes de proporções binomial exata e a técnica de false discovery rate para analisar as hipóteses postas. A análise estatística foi feita com o software R. O estudo está registrado no PROSPERO sob n.º CRD42019120694. Resultados: Foram identificados 5.167 artigos e após os critérios de seleção, 33 foram incluídos nessa revisão. Foram identificados 1.105 códigos da CID. O coeficiente de prevalência dos EAM variou entre 0,18% e 18,4% em internações hospitalares e a taxa de mortalidade variou entre 0,12 a 45,9 óbitos por 100 mil óbitos. Somente 195 (17,7%) códigos tiveram alta concordância entre os pesquisadores. Muitos códigos CID utilizados para detectar EAM possuem baixa concordância entre os pesquisadores e produziram diferentes taxas do evento. Conclusão: Os códigos rastreadores de EAM identificados representam um método simples e eficiente para captação de eventos adversos em grandes bancos de dados em saúde, contribuindo na redução da subnotificação nos tradicionais sistemas de notificações de EAM.

Differential expression of miRNAs involved in biological processes responsible for inflammation and immune response in lichen sclerosus urethral stricture disease

Purpose To better understand the pathophysiology of lichen sclerosus (LS) urethral stricture disease (USD), we aimed to investigate expression profiles of microRNAs (miRNAs) in tissue samples from men undergoing urethroplasty. Methods Urethral stricture tissue was collected from 2005–2020. Histologic features diagnostic of LS were the basis of pathologic evaluation. Foci of areas diagnostic for LS or non-LS strictures were chosen for RNA evaluation. In an initial screening analysis, 13 LS urethral strictures and 13 non-LS strictures were profiled via miRNA RT-qPCR arrays for 752 unique miRNA. A validation analysis of 23 additional samples (9 LS and 14 non-LS) was performed for 15 miRNAs. Statistical analyses were performed using SPSS v25. Gene Ontology (GO) analysis was performed using DIANA-mirPath v. 3.0. Results In the screening analysis 143 miRNAs were detected for all samples. 27 were differentially expressed between the groups (false discovery p-value <0.01). 15 of these miRNAs individually demonstrated an area under the curve (AUC)>0.90 for distinguishing between between LS and non-LS strictures. 11-fold upregulation of MiR-155-5p specifically was found in LS vs. non-LS strictures (p<0.001, AUC = 1.0). In the validation analysis, 13 of the 15 miRNAs tested were confirmed to have differential expression (false discovery p-value <0.10). Conclusions To our knowledge this is the first study evaluating miRNA expression profiles in LS and non-LS USD. We identified several miRNAs that are differentially expressed in USD caused by LS vs other etiologies, which could potentially serve as biomarkers of LS USD. The top eight differentially expressed miRNAs have been linked to immune response processes as well as involvement in wound healing, primarily angiogenesis and fibrosis.