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Abstract Introduction Patients with alopecia areata (AA) may have received several therapies for management of AA during their lives. In the ALLEGRO phase 2b/3 (NCT03732807) study, the oral JAK3/TEC family kinase inhibitor ritlecitinib demonstrated efficacy and an acceptable safety profile in patients aged ≥ 12 years with AA and ≥ 50% scalp hair loss. This post hoc analysis investigated associations between prior use of AA therapies and Severity of Alopecia Tool (SALT) responses in patients receiving ritlecitinib for AA. Methods Patients receiving ritlecitinib 30 mg or 50 mg once daily with or without an initial 4-week 200-mg daily loading dose were grouped by previous exposure to AA treatments, including topicals, intralesional corticosteroids (ILCS), topical immunotherapy, and systemic immunosuppressants or any prior AA treatment. Multivariable logistic regression analyses evaluated the association between response based on a SALT score of ≤ 20 and any prior treatment for AA at weeks 24 and 48. Results Of 522 patients, 360 (69.0%) had previous exposure to any AA treatment. At Week 24, SALT ≤ 20 response was positively associated with prior use of ILCS (odds ratio [OR], 2.12; 95% confidence interval [CI], 1.23–3.65; P 0.05). Previous exposure to any AA therapy was not associated with SALT ≤ 20 response at weeks 24 or 48 (all P > 0.05). Conclusions Prior AA treatment history had no effect on longer-term treatment response to ritlecitinib. Trial Registration Number NCT03732807. Graphical Abstract

Abstract Background The Lentivirus human immunodeficiency virus (HIV) causes chronic inflammation and AIDS in humans, with variable rates of disease progression between individuals driven by both host and viral factors. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host species and the virus strain, yet the immune underpinnings that drive differential Lentivirus pathogenicity remain incompletely understood. Methods We profile immune responses in a unique model of differential lentiviral pathogenicity where pig-tailed macaques are infected with highly genetically similar variants of SIV that differ in virulence. We apply longitudinal single-cell transcriptomics to this cohort, along with single-cell resolution cell-cell communication techniques, to understand the immune mechanisms underlying lentiviral pathogenicity. Results Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic Lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic Lentivirus has evolved to partially escape from interferon responses. Further, we identify CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic Lentivirus infection. Immune responses to highly pathogenic Lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity. Conclusions Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which in turn drives disease progression.

Abstract Serratia marcescens is a gram‐negative bacterium found commonly in water and soil. Initially thought to be non‐pathogenic, it is now recognised as an important cause of nosocomial and opportunistic infections. Skin infections are rare, but cases of S. marcescens causing ulcers, abscesses and necrotizing fasciitis have been reported. We report an unusual cutaneous presentation of S. marcescens in an immunosuppressed patient. A 77‐year‐old man under review for non‐melanoma skin cancer in the context of a previous cardiac transplant, presented with an asymptomatic scalp eruption. Immunosuppressive medications included ciclosporin 90 mg twice daily (2.5 mg/kg/day) and mycophenolate mofetil 1 g twice daily. Physical examination revealed well‐defined annular and polycyclic patches with brownish crusting across his scalp. Bacterial culture demonstrated a heavy growth of Staphylococcus aureus sensitive to flucloxacillin. The patient was treated with 7 days of flucloxacillin 500 mg four times daily. Despite this, the eruption extended. Skin biopsy demonstrated epidermal spongiosis, florid dermal inflammatory cell infiltrate and abundant bacteria and neutrophils in the parakeratotic crust. Fungal stains were negative as was direct immunofluorescence. Repeat culture demonstrated heavy growth of S. marcescens sensitive to ciprofloxacin. The patient was treated with 10 days of oral ciprofloxacin 500 mg twice daily along with 1% hydrogen peroxide cream topically with significant clinical improvement. Microbiological review indicated that a gram‐negative organism was present in the initial scalp swab. In addition, S. marcescens had been detected previously on a skin swab from a recent transient eruption on the torso. Further, a heavy growth of a coliform bacillus was demonstrated in a similar eruption on the chest in 2013. It was concluded that the patient was likely colonised with S. marcescens which appeared to have caused recurrent superficial skin infections over several years. We report this case to highlight an unusual clinical presentation of cutaneous S. marcescens infection. This should be considered in the differential diagnosis of skin eruptions in immunocompromised patients. Clinical information detailing a patient’s immunosuppressed state must be supplied on microbiology requests to allow accurate interpretation of results, and consideration of organisms which may otherwise be overlooked or considered contaminants.

Abstract Background Flow and mass cytometry are important modern immunology tools for measuring expression levels of multiple proteins on single cells. The goal is to better understand the mechanisms of responses on a single cell basis by studying differential expression of proteins. Most current data analysis tools compare expressions across many computationally discovered cell types. Our goal is to focus on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. Results Differential analysis of marker expressions can be difficult due to marker correlations and inter-subject heterogeneity, particularly for studies of human immunology. We address these challenges with two multiple regression strategies: a bootstrapped generalized linear model and a generalized linear mixed model. On simulated datasets, we compare the robustness towards marker correlations and heterogeneity of both strategies. For paired experiments, we find that both strategies maintain the target false discovery rate under medium correlations and that mixed models are statistically more powerful under the correct model specification. For unpaired experiments, our results indicate that much larger patient sample sizes are required to detect differences. We illustrate the CytoGLMM R package and workflow for both strategies on a pregnancy dataset. Conclusion Our approach to finding differential proteins in flow and mass cytometry data reduces biases arising from marker correlations and safeguards against false discoveries induced by patient heterogeneity.

The latent HIV-1 reservoir represents a major barrier to achieving a long-term antiretroviral therapy (ART)-free remission or cure for HIV-1. Natural Killer (NK) cells are innate immune cells that play a critical role in controlling viral infections and have been shown to be involved in preventing HIV-1 infection and, in those who are infected, delaying time to progression to AIDS. However, their role in limiting HIV-1 persistence on long term ART is still uncharacterized. To identify associations between markers of HIV-1 persistence and the NK cell receptor-ligand repertoire, we used twin mass cytometry panels to characterize the peripheral blood NK receptor-ligand repertoire in individuals with long-term antiretroviral suppression enrolled in the AIDS Clinical Trial Group A5321 study. At the time of testing, participants had been on ART for a median of 7 years, with virological suppression

Introduction Alopecia areata (AA) is a common cause of immune-mediated non-scarring hair loss. Links between AA and common mental health, autoimmune and atopic conditions, and common infections have previously been described but remain incompletely elucidated and contemporary descriptions of the epidemiology of AA in the UK are lacking.Methods and analysis Retrospective study series using a large population-based cohort (5.2 million) from the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database, exploring four themes: AA epidemiology, mental health comorbidities, autoimmune/atopic associations and common infections.In the epidemiology theme, we will describe the incidence and point prevalence of AA overall and by age, sex and sociodemographic factors. Healthcare utilisation (primary care visits and secondary care referrals) and treatments for AA will also be assessed. In the mental health theme, we will explore the prevalence and incidence of mental health conditions (anxiety, depressive episodes, recurrent depressive disorder, adjustment disorder, agoraphobia, self-harm and parasuicide) in people with AA compared with matched controls. We will also explore the mental health treatment patterns (medication and psychological interventions), time off work and unemployment rates. Within the autoimmune/atopic associations theme, we will examine the prevalence of atopic (atopic dermatitis, allergic rhinitis, asthma) and autoimmune conditions (Crohn’s disease, ulcerative colitis, coeliac disease, type 1 diabetes, Hashimoto’s thyroiditis, Graves’ disease, rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, systemic lupus erythematosus (SLE), polymyalgia rheumatica, Sjögren’s syndrome, psoriasis, vitiligo, multiple sclerosis, pernicious anaemia) in people with AA compared with matched controls. We will also estimate the incidence of new-onset atopic and autoimmune conditions after AA diagnosis. Within the common infections theme, we will examine the incidence of common infections (respiratory tract infection, pneumonia, acute bronchitis, influenza, skin infection, urinary tract infection, genital infections, gastrointestinal infection, herpes simplex, herpes zoster, meningitis, COVID-19) in people with AA compared with matched controls.Ethics and dissemination The Health Research Authority decision tool classed this a study of usual practice, ethics approval was not required. Study approval was granted by the RCGP RSC Study Approval Committee. Results will be disseminated through peer-reviewed publications.Observational study registration number NCT04239521.

Abstract Background Increasing evidence reveals the importance of the microbiome in health and disease and inseparable host-microbial dependencies. Host-microbe interactions are highly relevant in patients receiving allogeneic hematopoietic stem cell transplantation (HSCT), i.e., a replacement of the cellular components of the patients’ immune system with that of a foreign donor. HSCT is employed as curative immunotherapy for a number of non-malignant and malignant hematologic conditions, including cancers such as acute lymphoblastic leukemia. The procedure can be accompanied by severe side effects such as infections, acute graft-versus-host disease (aGvHD), and death. Here, we performed a longitudinal analysis of immunological markers, immune reconstitution and gut microbiota composition in relation to clinical outcomes in children undergoing HSCT. Such an analysis could reveal biomarkers, e.g., at the time point prior to HSCT, that in the future could be used to predict which patients are of high risk in relation to side effects and clinical outcomes and guide treatment strategies accordingly. Results In two multivariate analyses (sparse partial least squares regression and canonical correspondence analysis), we identified three consistent clusters: (1) high concentrations of the antimicrobial peptide human beta-defensin 2 (hBD2) prior to the transplantation in patients with high abundances of Lactobacillaceae, who later developed moderate or severe aGvHD and exhibited high mortality. (2) Rapid reconstitution of NK and B cells in patients with high abundances of obligate anaerobes such as Ruminococcaceae, who developed no or mild aGvHD and exhibited low mortality. (3) High inflammation, indicated by high levels of C-reactive protein, in patients with high abundances of facultative anaerobic bacteria such as Enterobacteriaceae. Furthermore, we observed that antibiotic treatment influenced the bacterial community state. Conclusions We identify multivariate associations between specific microbial taxa, host immune markers, immune cell reconstitution, and clinical outcomes in relation to HSCT. Our findings encourage further investigations into establishing longitudinal surveillance of the intestinal microbiome and relevant immune markers, such as hBD2, in HSCT patients. Profiling of the microbiome may prove useful as a prognostic tool that could help identify patients at risk of poor immune reconstitution and adverse outcomes, such as aGvHD and death, upon HSCT, providing actionable information in guiding precision medicine.

Frontal fibrosing alopecia (FFA) features lichenoid cutaneous inflammation and scarring hair loss. Here, Tziotzios et al. identify four genetic loci associated with FFA by GWAS followed by Bayesian fine-mapping, co-localisation and HLA imputation which highlights HLA-B*07:02 as a risk factor.

Natural killer (NK) cells are the predominant antiviral cells of the innate immune system, and may play an important role in acquisition and disease progression of HIV. While untreated HIV infection is associated with distinct alterations in the peripheral blood NK cell repertoire, less is known about how NK phenotype is altered in the setting of long-term viral suppression with antiretroviral therapy (ART), as well as how NK memory can impact functional responses. As such, we sought to identify changes in NK cell phenotype and function using high-dimensional mass cytometry to simultaneously analyze both surface and functional marker expression of peripheral blood NK cells in a cohort of ART-suppressed, HIV+ patients and HIV- healthy controls. We found that the NK cell repertoire following IL-2 treatment was altered in individuals with treated HIV infection compared to healthy controls, with increased expression of markers including NKG2C and CD2, and decreased expression of CD244 and NKp30. Using co-culture assays with autologous, in vitro HIV-infected CD4 T cells, we identified a subset of NK cells with enhanced responsiveness to HIV-1-infected cells, but no differences in the magnitude of anti-HIV NK cell responses between the HIV+ and HIV− groups. In addition, by profiling of NK cell receptors on responding cells, we found similar phenotypes of HIV-responsive NK cell subsets in both groups. Lastly, we identified clusters of NK cells that are altered in individuals with treated HIV infection compared to healthy controls, but found that these clusters are distinct from those that respond to HIV in vitro. As such, we conclude that while chronic, treated HIV infection induces a reshaping of the IL-2-stimulated peripheral blood NK cell repertoire, it does so in a way that does not make the repertoire more HIV-specific.

Daclizumab beta is a humanized monoclonal antibody that binds to CD25 and selectively inhibits high-affinity IL-2 receptor signaling. As a former treatment for relapsing forms of multiple sclerosis (RMS), daclizumab beta induces robust expansion of the CD56bright subpopulation of NK cells that is correlated with the drug’s therapeutic effects. As NK cells represent a heterogeneous population of lymphocytes with a range of phenotypes and functions, the goal of this study was to better understand how daclizumab beta altered the NK cell repertoire to provide further insight into the possible mechanism(s) of action in RMS. We used mass cytometry to evaluate expression patterns of NK cell markers and provide a comprehensive assessment of the NK cell repertoire in individuals with RMS treated with daclizumab beta or placebo over the course of 1 year. Treatment with daclizumab beta significantly altered the NK cell repertoire compared to placebo treatment. As previously reported, daclizumab beta significantly increased expression of CD56 on total NK cells. Within the CD56bright NK cells, treatment was associated with multiple phenotypic changes, including increased expression of NKG2A and NKp44, and diminished expression of CD244, CD57, and NKp46. These alterations occurred broadly across the CD56bright population, and were not associated with a specific subset of CD56bright NK cells. While the changes were less dramatic, CD56dim NK cells responded distinctly to daclizumab beta treatment, with higher expression of CD2 and NKG2A, and lower expression of FAS-L, HLA-DR, NTB-A, NKp30, and Perforin. Together, these data indicate that the expanded CD56bright NK cells share features of both immature and mature NK cells. These findings show that daclizumab beta treatment is associated with unique changes in NK cells that may enhance their ability to kill autoreactive T cells or to exert immunomodulatory functions.

Specific causes of preterm birth remain unclear. Several recent studies have suggested that immune changes during pregnancy are associated with the timing of delivery, yet few studies have been performed in low-income country settings where the rates of preterm birth are the highest. We conducted a retrospective nested case-control evaluation within a longitudinal study among HIV-uninfected pregnant Kenyan women. To characterize immune function in these women, we evaluated unstimulated and stimulated peripheral blood mononuclear cells in vitro with the A/California/2009 strain of influenza to understand the influenza-induced immune response. We then evaluated transcript expression profiles using the Affymetrix Human GeneChip Transcriptome Array 2.0. Transcriptional profiles of sufficient quality for analysis were obtained from 54 women; 19 of these women delivered 37 weeks. The median time to birth from sample collection was 13 weeks. No transcripts were significantly associated with preterm birth in a case-control study of matched term and preterm birth (n = 42 women). In the influenza-stimulated samples, expression of IFNL1 was associated with longer time to delivery—the amount of time between sample collection and delivery (n = 54 women). A qPCR analysis confirmed that influenza-induced IFNL expression was associated with longer time to delivery. These data indicate that during pregnancy, ex vivo influenza stimulation results in altered transcriptional response and is associated with time to delivery in cohort of women residing in an area with high preterm birth prevalence.

Abstract Background Detecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Estimating data points’ ‘natural ordering’ and their corresponding uncertainties can help researchers draw insights about the mechanisms involved. Results We introduce a Bayesian Unidimensional Scaling (BUDS) technique which extracts dominant sources of variation in high dimensional datasets and produces their visual data summaries, facilitating the exploration of a hidden continuum. The method maps multivariate data points to latent one-dimensional coordinates along their underlying trajectory, and provides estimated uncertainty bounds. By statistically modeling dissimilarities and applying a DiSTATIS registration method to their posterior samples, we are able to incorporate visualizations of uncertainties in the estimated data trajectory across different regions using confidence contours for individual data points. We also illustrate the estimated overall data density across different areas by including density clouds. One-dimensional coordinates recovered by BUDS help researchers discover sample attributes or covariates that are factors driving the main variability in a dataset. We demonstrated usefulness and accuracy of BUDS on a set of published microbiome 16S and RNA-seq and roll call data. Conclusions Our method effectively recovers and visualizes natural orderings present in datasets. Automatic visualization tools for data exploration and analysis are available at: https://nlhuong.shinyapps.io/visTrajectory/ .

The Ras pathway patterns the poles of the Drosophila embryo by downregulating the levels and activity of a DNA-binding transcriptional repressor Capicua (Cic). We demonstrate that the spatiotemporal pattern of Cic during this signaling event can be harnessed for functional studies of mutations in the Ras pathway in human diseases. Our approach relies on a new microfluidic device that enables parallel imaging of Cic dynamics in dozens of live embryos. We found that although the pattern of Cic in early embryos is complex, it can be accurately approximated by a product of one spatial profile and one time-dependent amplitude. Analysis of these functions of space and time alone reveals the differential effects of mutations within the Ras pathway. Given the highly conserved nature of Ras-dependent control of Cic, our approach provides new opportunities for functional analysis of multiple sequence variants from developmental abnormalities and cancers.

Highly exposed seronegative (HESN) individuals present a unique setting to study mechanisms of protection against HIV acquisition. As natural killer (NK) cell activation and function have been implicated as a correlate of protection in HESN individuals, we sought to better understand the features of NK cells that may confer protection. We used mass cytometry to phenotypically profile NK cells from a cohort of Beninese sex workers and healthy controls. We found that NK cells from HESN women had increased expression of NKG2A, NKp30 and LILRB1, as well as the Fc receptor CD16, and decreased expression of DNAM-1, CD94, Siglec-7, and NKp44. Using functional assessments of NK cells from healthy donors against autologous HIV-infected CD4+ T cells, we observed that NKp30+ and Siglec-7+ cells had improved functional activity. Further, we found that NK cells from HESN women trended towards increased antibody-dependent cellular cytotoxicity (ADCC) activity; this activity correlated with increased CD16 expression. Overall, we identify features of NK cells in HESN women that may contribute to protection from HIV infection. Follow up studies with larger cohorts are warranted to confirm these findings.

Pregnant women are particularly susceptible to complications of influenza A virus infection, which may result from pregnancy-induced changes in the function of immune cells, including natural killer (NK) cells. To better understand NK cell function during pregnancy, we assessed the ability of the two main subsets of NK cells, CD56dim, and CD56bright NK cells, to respond to influenza-virus infected cells and tumor cells. During pregnancy, CD56dim and CD56bright NK cells displayed enhanced functional responses to both infected and tumor cells, with increased expression of degranulation markers and elevated frequency of NK cells producing IFN-γ. To better understand the mechanisms driving this enhanced function, we profiled CD56dim and CD56bright NK cells from pregnant and non-pregnant women using mass cytometry. NK cells from pregnant women displayed significantly increased expression of several functional and activation markers such as CD38 on both subsets and NKp46 on CD56dim NK cells. NK cells also displayed diminished expression of the chemokine receptor CXCR3 during pregnancy. Overall, these data demonstrate that functional and phenotypic shifts occur in NK cells during pregnancy that can influence the magnitude of the immune response to both infections and tumors.

The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle. Whether shifts away from the foraging lifestyle that characterize much of humanity's past influence the gut microbiome, and to what degree, remains unclear. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in traditional human lifestyles. These groups led seminomadic hunting-gathering lifestyles until transitioning to varying levels of agricultural dependence upon farming. The Tharu began farming 250-300 years ago, the Raute and Raji transitioned 30-40 years ago, and the Chepang retain many aspects of a foraging lifestyle. We assess the contributions of dietary and environmental factors on their gut-associated microbes and find that differences in the lifestyles of Himalayan foragers and farmers are strongly correlated with microbial community variation. Furthermore, the gut microbiomes of all four traditional Himalayan populations are distinct from that of the Americans, indicating that industrialization may further exacerbate differences in the gut community. The Chepang foragers harbor an elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of the populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. The gut microbiomes of Raute and Raji reveal an intermediate state between the Chepang and Tharu, indicating that divergence from a stereotypical foraging microbiome can occur within a single generation. Our results also show that environmental factors such as drinking water source and solid cooking fuel are significantly associated with the gut microbiome. Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across lifestyles. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of determining how large-scale gut microbiome reconfiguration impacts human biology.

Functional brain connectivity is the co-occurrence of brain activity in different areas during resting and while doing tasks. The data of interest are multivariate timeseries measured simultaneously across brain parcels using resting-state fMRI (rfMRI). We analyze functional connectivity using two heteroscedasticity models. Our first model is low-dimensional and scales linearly in the number of brain parcels. Our second model scales quadratically. We apply both models to data from the Human Connectome Project (HCP) comparing connectivity between short and conventional sleepers. We find stronger functional connectivity in short than conventional sleepers in brain areas consistent with previous findings. This might be due to subjects falling asleep in the scanner. Consequently, we recommend the inclusion of average sleep duration as a covariate to remove unwanted variation in rfMRI studies. A power analysis using the HCP data shows that a sample size of 40 detects 50% of the connectivity at a false discovery rate of 20%. We provide implementations using R and the probabilistic programming language Stan.

The R package structSSI provides an accessible implementation of two recently developed simultaneous and selective inference techniques: the group Benjamini-Hochberg and hierarchical false discovery rate procedures. Unlike many multiple testing schemes, these methods specifically incorporate existing information about the grouped or hierarchical dependence between hypotheses under consideration while controlling the false discovery rate. Doing so increases statistical power and interpretability. Furthermore, these procedures provide novel approaches to the central problem of encoding complex dependency between hypotheses. We briefly describe the group Benjamini-Hochberg and hierarchical false discovery rate procedures and then illustrate them using two examples, one a measure of ecological microbial abundances and the other a global temperature time series. For both procedures, we detail the steps associated with the analysis of these particular data sets, including establishing the dependence structures, performing the test, and interpreting the results. These steps are encapsulated by R functions, and we explain their applicability to general data sets.

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.

the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.

To date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs).We present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs.Our integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcomes.

Vinyl chloride (VC) is a human carcinogen and widespread priority pollutant. Here we report the first, to our knowledge, complete genome sequences of microorganisms able to respire VC, Dehalococcoides sp. strains VS and BAV1. Notably, the respective VC reductase encoding genes, vcrAB and bvcAB, were found embedded in distinct genomic islands (GEIs) with different predicted integration sites, suggesting that these genes were acquired horizontally and independently by distinct mechanisms. A comparative analysis that included two previously sequenced Dehalococcoides genomes revealed a contextually conserved core that is interrupted by two high plasticity regions (HPRs) near the Ori. These HPRs contain the majority of GEIs and strain-specific genes identified in the four Dehalococcoides genomes, an elevated number of repeated elements including insertion sequences (IS), as well as 91 of 96 rdhAB, genes that putatively encode terminal reductases in organohalide respiration. Only three core rdhA orthologous groups were identified, and only one of these groups is supported by synteny. The low number of core rdhAB, contrasted with the high rdhAB numbers per genome (up to 36 in strain VS), as well as their colocalization with GEIs and other signatures for horizontal transfer, suggests that niche adaptation via organohalide respiration is a fundamental ecological strategy in Dehalococccoides. This adaptation has been exacted through multiple mechanisms of recombination that are mainly confined within HPRs of an otherwise remarkably stable, syntenic, streamlined genome among the smallest of any free-living microorganism.

Supervised learning can be used to segment/identify regions of interest in images usingboth color and morphological information. A novel object identication algorithm wasdeveloped in Java to locate immune and cancer cells in images of immunohistochemically-stained lymph node tissue from a recent study published by Kohrt et al. (2005). Thealgorithms are also showing promise in other domains. The success of the method de-pends heavily on the use of color, the relative homogeneity of object appearance and oninteractivity. As is often the case in segmentation, an algorithm specically tailored tothe application works better than using broader methods that work passably well on anyproblem. Our main innovation is the interactive feature extraction from color images. Wealso enable the user to improve the classication with an interactive visualization system.This is then coupled with the statistical learning algorithms and intensive feedback fromthe user over many classication-correction iterations, resulting in a highly accurate anduser-friendly solution. The system ultimately provides the locations of every cell recog-nized in the entire tissue in a text le tailored to be easily imported into R (Ihaka andGentleman 1996; R Development Core Team 2009) for further statistical analyses. Thisdata is invaluable in the study of spatial and multidimensional relationships between cellpopulations and tumor structure. This system is available at http://www.GemIdent.com/together with three demonstration videos and a manual.

BackgroundWhile lymph node metastasis is among the strongest predictors of disease-free and overall survival for patients with breast cancer, the immunological nature of tumor-draining lymph nodes is often ignored, and may provide additional prognostic information on clinical outcome.Methods and findingsWe performed immunohistochemical analysis of 47 sentinel and 104 axillary (nonsentinel) nodes from 77 breast cancer patients with 5 y of follow-up to determine if alterations in CD4, CD8, and CD1a cell populations predict nodal metastasis or disease-free survival. Sentinel and axillary node CD4 and CD8 T cells were decreased in breast cancer patients compared to control nodes. CD1a dendritic cells were also diminished in sentinel and tumor-involved axillary nodes, but increased in tumor-free axillary nodes. Axillary node, but not sentinel node, CD4 T cell and dendritic cell populations were highly correlated with disease-free survival, independent of axillary metastasis. Immune profiling of ALN from a test set of 48 patients, applying CD4 T cell and CD1a dendritic cell population thresholds of CD4 > or = 7.0% and CD1a > or = 0.6%, determined from analysis of a learning set of 29 patients, provided significant risk stratification into favorable and unfavorable prognostic groups superior to clinicopathologic characteristics including tumor size, extent or size of nodal metastasis (CD4, p < 0.001 and CD1a, p < 0.001). Moreover, axillary node CD4 T cell and CD1a dendritic cell populations allowed more significant stratification of disease-free survival of patients with T1 (primary tumor size 2 cm or less) and T2 (5 cm or larger) tumors than all other patient characteristics. Finally, sentinel node immune profiles correlated primarily with the presence of infiltrating tumor cells, while axillary node immune profiles appeared largely independent of nodal metastases, raising the possibility that, within axillary lymph nodes, immune profile changes and nodal metastases represent independent processes.ConclusionThese findings demonstrate that the immune profile of tumor-draining lymph nodes is of novel biologic and clinical importance for patients with early stage breast cancer.

Small-molecule metabolites are principal actors in myriad phenomena across biochemistry and serve as an important source of biomarkers and drug candidates. Given a sample of unknown composition, identifying the metabolites present is difficult given the large number of small molecules both known and yet to be discovered. Even for biofluids such as human blood, building reliable ways of identifying biomarkers is challenging. A workhorse method for characterizing individual molecules in such untargeted metabolomics studies is tandem mass spectrometry (MS/MS). MS/MS spectra provide rich information about chemical composition. However, structural characterization from spectra corresponding to unknown molecules remains a bottleneck in metabolomics. Current methods often rely on matching to pre-existing databases in one form or another. Here we develop a preprocessing scheme and supervised topic modeling approach to identify modular groups of spectrum fragments and neutral losses corresponding to chemical substructures using labeled latent Dirichlet allocation (LLDA) to map spectrum features to known chemical structures. These structures appear in new unknown spectra and can be predicted. We find that LLDA is an interpretable and reliable method for structure prediction from MS/MS spectra. Specifically, the LLDA approach has the following advantages: (a) molecular topics are interpretable; (b) A practitioner can select any set of chemical structure labels relevant to their problem; (c ) LLDA performs well and can exceed the performance of other methods in predicting substructures in novel contexts.