Novel Circulating and Tissue Monocytes as Well as Macrophages in Pancreatitis and Recovery.

BACKGROUND AND AIMS: Acute pancreatitis (AP) is an inflammatory disease with mild to severe course that is associated with local and systemic complications and significant mortality. Uncovering inflammatory pathways that lead to progression and recovery will inform ways to monitor and/or develop effective therapies. METHODS: We performed single-cell mass Cytometry by Time Of Flight (CyTOF) analysis to identify pancreatic and systemic inflammatory signals during mild AP (referred to as AP), severe AP (SAP), and recovery using 2 independent experimental models and blood from patients with AP and recurrent AP. Flow cytometric validation of monocytes subsets identified using CyTOF analysis was performed independently. RESULTS: Ly6C+ inflammatory monocytes were the most altered cells in the pancreas during experimental AP, recovery, and SAP. Deep profiling uncovered heterogeneity among pancreatic and blood monocytes and identified 7 novel subsets during AP and recovery, and 6 monocyte subsets during SAP. Notably, a dynamic shift in pancreatic CD206+ macrophage population was observed during AP and recovery. Deeper profiling of the CD206+ macrophage identified 7 novel subsets during AP, recovery, and SAP. Differential expression analysis of these novel monocyte and CD206+ macrophage subsets revealed significantly altered surface (CD44, CD54, CD115, CD140a, CD196, podoplanin) and functional markers (interferon-γ, interleukin 4, interleukin 22, latency associated peptide-transforming growth factor-ß, tumor necrosis factor-α, T-bet, RoRγt) that were associated with recovery and SAP. Moreover, a targeted functional analysis further revealed distinct expression of pro- and anti-inflammatory cytokines by pancreatic CD206+ macrophage subsets as the disease either progressed or resolved. Similarly, we identified heterogeneity among circulating classical inflammatory monocytes (CD14+CD16-) and novel subsets in patients with AP and recurrent AP. CONCLUSIONS: We identified several novel monocyte/macrophage subsets with unique phenotype and functional characteristics that are associated with AP, recovery, and SAP. Our findings highlight differential innate immune responses during AP progression and recovery that can be leveraged for future disease monitoring and targeting.

HIV Infection Is Associated with Loss of Anti-Inflammatory Alveolar Macrophages.

HIV type 1 is associated with pulmonary dysfunction that is exacerbated by cigarette smoke. Alveolar macrophages (AM) are the most prominent immune cell in the alveolar space. These cells play an important role in clearing inhaled pathogens and regulating the inflammatory environment; however, how HIV infection impacts AM phenotype and function is not well understood, in part because of their autofluorescence and the absence of well-defined surface markers. The main aim of this study was to evaluate the impact of HIV infection on human AM and to compare the effect of smoking on their phenotype and function. Time-of-flight mass cytometry and RNA sequencing were used to characterize macrophages from human bronchoalveolar lavage of HIV-infected and -uninfected smokers and nonsmokers. We found that the frequency of CD163+ anti-inflammatory AM was decreased, whereas CD163-CCR7+ proinflammatory AM were increased in HIV infection. HIV-mediated proinflammatory polarization was associated with increased levels of inflammatory cytokines and macrophage activation. Conversely, smoking heightened the inflammatory response evident by change in the expression of CXCR4 and TLR4. Altogether, these findings suggest that HIV infection, along with cigarette smoke, favors a proinflammatory macrophage phenotype associated with enhanced expression of inflammatory molecules. Further, this study highlights time-of-flight mass cytometry as a reliable method for immunophenotyping the highly autofluorescent cells present in the bronchoalveolar lavage of cigarette smokers.

CANTARE: finding and visualizing network-based multi-omic predictive models.

BACKGROUND: One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be "ome aware." Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. METHODS: We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting "top table" of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). RESULTS: We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10-5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User's Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/ . CONCLUSION: CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

The Application of Cytokine Expression Assays to Differentiate Active From Previously Treated Syphilis.

To investigate the role of serum cytokine assays to distinguish between active from treated syphilis among serofast patients, we recruited individuals into a prospective cohort study. Participants underwent routine syphilis screening. We selected specimens from a majority cohort of serofast participants with treated and active syphilis. We analyzed specimens with a 62-cytokine multiplex bead-based enzyme-linked immunosorbent assay. Cytokines, brain-derived neurotrophic factor and tumor necrosis factor ß, were most predictive. We built a decision tree that was 82.4% accurate, 100% (95% confidence interval, 82%-100%) sensitive, and 45% (18%-75%) specific. Our decision tree differentiated between serum specimens from serofast participants with treated syphilis versus active syphilis.

Simultaneous Recognition of Allogeneic MHC and Cognate Autoantigen by Autoreactive T Cells in Transplant Rejection.

The autoimmune condition is a primary obstacle to inducing tolerance in type 1 diabetes patients receiving allogeneic pancreas transplants. It is unknown how autoreactive T cells that recognize self-MHC molecules contribute to MHC-disparate allograft rejection. In this report, we show the presence and accumulation of dual-reactive, that is autoreactive and alloreactive, T cells in C3H islet allografts that were transplanted into autoimmune diabetic NOD mice. Using high-throughput sequencing, we discovered that T cells prevalent in allografts share identical TCRs with autoreactive T cells present in pancreatic islets. T cells expressing TCRs that are enriched in allograft lesions recognized C3H MHC molecules, and five of six cell lines expressing these TCRs were also reactive to NOD islet cells. These results reveal the presence of autoreactive T cells that mediate cross-reactive alloreactivity, and indicate a requirement for regulating such dual-reactive T cells in tissue replacement therapies given to autoimmune individuals.

VOLARE: visual analysis of disease-associated microbiome-immune system interplay.

BACKGROUND: Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. "Omic" methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between "omes", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments. RESULTS: To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies-microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results. CONCLUSIONS: Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Evaluation of a minimal array of Treponema pallidum antigens as biomarkers for syphilis diagnosis, infection staging, and response to treatment.

IMPORTANCE: This manuscript explores the host humoral response to selected antigens of the syphilis agent during infection to evaluate their potential use as diagnostic tests and markers for treatment.

Differential effects of antiretroviral treatment on immunity and gut microbiome composition in people living with HIV in rural versus urban Zimbabwe.

BACKGROUND: The widespread availability of antiretroviral therapy (ART) has dramatically reduced mortality and improved life expectancy for people living with HIV (PLWH). However, even with HIV-1 suppression, chronic immune activation and elevated inflammation persist and have been linked to a pro-inflammatory gut microbiome composition and compromised intestinal barrier integrity. PLWH in urban versus rural areas of sub-Saharan Africa experience differences in environmental factors that may impact the gut microbiome and immune system, in response to ART, yet this has not previously been investigated in these groups. To address this, we measured T cell activation/exhaustion/trafficking markers, plasma inflammatory markers, and fecal microbiome composition in PLWH and healthy participants recruited from an urban clinic in the city of Harare, Zimbabwe, and a district hospital that services surrounding rural villages. PLWH were either ART naïve at baseline and sampled again after 24 weeks of first-line ART and the antibiotic cotrimoxazole or were ART-experienced at both timepoints. RESULTS: Although expected reductions in the inflammatory marker IL-6, T-cell activation, and exhaustion were observed with ART-induced viral suppression, these changes were much more pronounced in the urban versus the rural area. Gut microbiome composition was the most highly altered from healthy controls in ART experienced PLWH, and characterized by both reduced alpha diversity and altered composition. However, gut microbiome composition showed a pronounced relationship with T cell activation and exhaustion in ART-naïve PLWH, suggesting a particularly significant role for the gut microbiome in disease progression in uncontrolled infection. Elevated immune exhaustion after 24 weeks of ART did correlate with both living in the rural location and a more Prevotella-rich/Bacteroides-poor microbiome type, suggesting a potential role for rural-associated microbiome differences or their co-variates in the muted improvements in immune exhaustion in the rural area. CONCLUSION: Successful ART was less effective at reducing gut microbiome-associated inflammation and T cell activation in PLWH in rural versus urban Zimbabwe, suggesting that individuals on ART in rural areas of Zimbabwe may be more vulnerable to co-morbidity related to sustained immune dysfunction in treated infection. Video Abstract.

Antiretroviral treatment is less effective at reducing gut microbiome-associated inflammation and T cell activation in people living with HIV in rural versus urban Zimbabwe.

The widespread availability of antiretroviral therapy (ART) for people living with HIV (PLWH) has dramatically reduced mortality and improved life expectancy. However, even with suppression of HIV-1 replication, chronic immune activation and elevated inflammation persist. Chronic immune activation has been linked to a pro-inflammatory gut microbiome composition, exacerbated by compromised intestinal barrier integrity that occurs after HIV infection. Individuals living in urban versus rural areas of sub-Saharan Africa have differences in environmental factors such as water source or diet that may impact gut microbiome composition, yet immune phenotype and gut microbiome composition response to ART in PLWH living in rural versus urban areas of sub-Saharan Africa have not been compared. Here, we measured immune phenotypes and fecal microbiome composition in PLWH and healthy participants recruited from the urban Mabvuku polyclinic in the city of Harare, Zimbabwe and the Mutoko District hospital located in a district 146 km from Harare that services surrounding rural villages. PLWH were either ART naïve at baseline and sampled again after 24 weeks of treatment with efavirenz/lamivudine/tenofovir disoproxil fumarate (EFV/3TC/TDF) and the prophylactic antibiotic cotrimoxazole or were ART experienced at both timepoints. Although expected reductions in the inflammatory marker IL-6, T-cell activation, and exhaustion were observed in individuals who had suppressed HIV-1 with treatment, these changes were significant only when considering individuals in the urban and not the rural area. Gut microbiome composition showed more marked differences from healthy controls in the ART experienced compared to ART naïve cohort, and consistent longitudinal changes were also observed in ART naïve PLWH after 24 weeks of treatment, including a reduction in alpha diversity and altered composition. However, gut microbiome composition showed a more pronounced relationship with chronic immune activation and exhaustion phenotypes in the ART naïve compared to ART experienced PLWH, suggesting a particularly significant role for the gut microbiome in disease progression in uncontrolled infection.

The Stanford Data Miner: a novel approach for integrating and exploring heterogeneous immunological data.

BACKGROUND: Systems-level approaches are increasingly common in both murine and human translational studies. These approaches employ multiple high information content assays. As a result, there is a need for tools to integrate heterogeneous types of laboratory and clinical/demographic data, and to allow the exploration of that data by aggregating and/or segregating results based on particular variables (e.g., mean cytokine levels by age and gender). METHODS: Here we describe the application of standard data warehousing tools to create a novel environment for user-driven upload, integration, and exploration of heterogeneous data. The system presented here currently supports flow cytometry and immunoassays performed in the Stanford Human Immune Monitoring Center, but could be applied more generally. RESULTS: Users upload assay results contained in platform-specific spreadsheets of a defined format, and clinical and demographic data in spreadsheets of flexible format. Users then map sample IDs to connect the assay results with the metadata. An OLAP (on-line analytical processing) data exploration interface allows filtering and display of various dimensions (e.g., Luminex analytes in rows, treatment group in columns, filtered on a particular study). Statistics such as mean, median, and N can be displayed. The views can be expanded or contracted to aggregate or segregate data at various levels. Individual-level data is accessible with a single click. The result is a user-driven system that permits data integration and exploration in a variety of settings. We show how the system can be used to find gender-specific differences in serum cytokine levels, and compare them across experiments and assay types. CONCLUSIONS: We have used the tools and techniques of data warehousing, including open-source business intelligence software, to support investigator-driven data integration and mining of diverse immunological data.

Congruence as a measurement of extended haplotype structure across the genome.

BACKGROUND: Historically, extended haplotypes have been defined using only a few data points, such as alleles for several HLA genes in the MHC. High-density SNP data, and the increasing affordability of whole genome SNP typing, creates the opportunity to define higher resolution extended haplotypes. This drives the need for new tools that support quantification and visualization of extended haplotypes as defined by as many as 2000 SNPs. Confronted with high-density SNP data across the major histocompatibility complex (MHC) for 2,300 complete families, compiled by the Type 1 Diabetes Genetics Consortium (T1DGC), we developed software for studying extended haplotypes. METHODS: The software, called ExHap (Extended Haplotype), uses a similarity measurement we term congruence to identify and quantify long-range allele identity. Using ExHap, we analyzed congruence in both the T1DGC data and family-phased data from the International HapMap Project. RESULTS: Congruent chromosomes from the T1DGC data have between 96.5% and 99.9% allele identity over 1,818 SNPs spanning 2.64 megabases of the MHC (HLA-DRB1 to HLA-A). Thirty-three of 132 DQ-DR-B-A defined haplotype groups have > 50% congruent chromosomes in this region. For example, 92% of chromosomes within the DR3-B8-A1 haplotype are congruent from HLA-DRB1 to HLA-A (99.8% allele identity). We also applied ExHap to all 22 autosomes for both CEU and YRI cohorts from the International HapMap Project, identifying multiple candidate extended haplotypes. CONCLUSIONS: Long-range congruence is not unique to the MHC region. Patterns of allele identity on phased chromosomes provide a simple, straightforward approach to visually and quantitatively inspect complex long-range structural patterns in the genome. Such patterns aid the biologist in appreciating genetic similarities and differences across cohorts, and can lead to hypothesis generation for subsequent studies.

Dynamic Serial Cytokine Measurements During Intravenous Ca-DTPA Chelation in Gadolinium Deposition Disease and Gadolinium Storage Condition: A Pilot Study.

PURPOSE: The aim of this study was to investigate the feasibility of measuring early changes in serum cytokine levels after intravenous diethylenetriaminepentaacetic acid (Ca-DTPA) chelation in patients manifesting either gadolinium deposition disease (GDD) or gadolinium storage condition (GSC) and the possible usefulness of this method in further research. METHODS: Four patients with recent-onset GDD (≤1 year) and 2 patients with long-standing GSC (4 and 9 years) underwent chelation with intravenous bolus administration of Ca-DTPA. Multiple blood draws were performed to measure serum cytokines: at T = 0 (before Ca-DTPA injection) and 1, 5, 10, 30, 60 minutes, and 24 hours after Ca-DTPA injection. Patients rated the severity of GDD symptom flare at 24 hours. The 24-hour urine Gd amounts were measured prechelation and for the 24 hours after chelation. Serum samples were analyzed blind to whether patients had GDD or GSC but with knowledge of the time points characterizing each sample. RESULTS: Urine samples for both GDD and GSC patients showed increases in Gd postchelation. All GDD patients experienced flare reactions postchelation; the 2 GSC patients did not. Two cytokines, EGF and sCD40L, peaked at 30 minutes postchelation in at least 4 of the 6 participants. Three cytokines, ENA78/CXCL5, EOTAXIN/CCL11, and LEPTIN, peaked at 24 hours in at least 4 of the 6 participants. Two participants were high outliers for a large number of cytokines across time points. No clear distinction between GDD and GSC was apparent from the cytokine patterns, although differences were present. CONCLUSIONS: This pilot study describes precise temporal resolution (in the range of minutes) after a cytokine-inciting event. Select cytokines exhibited peak values at different time points. At this preliminary stage of investigation, peak cytokine release seems to reflect the amount of Gd mobilized rather than the severity of the patient symptomatic reaction. Too few subjects were studied to support statistical analysis between GDD and GSC groups, although differences were observed through visual data analysis.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study.

OBJECTIVE: Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention. METHODS: This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE). RESULTS: A total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p < 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p < 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648). CONCLUSIONS: Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

Elevated inflammatory fecal immune factors in men who have sex with men with HIV associate with microbiome composition and gut barrier function.

Introduction: People living with HIV infection (PLWH) exhibit elevated levels of gastrointestinal inflammation. Potential causes of this inflammation include HIV infection and associated immune dysfunction, sexual behaviors among men who have sex with men (MSM) and gut microbiome composition. Methods: To better understand the etiology of gastrointestinal inflammation we examined levels of 28 fecal soluble immune factors (sIFs) and the fecal microbiome in well-defined cohorts of HIV seronegative MSM (MSM-SN), MSM with untreated HIV infection (MSM-HIV) and MSM with HIV on anti-retroviral treatment (MSMART). Additionally, fecal solutes from these participants were used to stimulate T-84 colonic epithelial cells to assess barrier function. Results: Both MSM cohorts with HIV had elevated levels of fecal calprotectin, a clinically relevant marker of GI inflammation, and nine inflammatory fecal sIFs (GM-CSF, ICAM-1, IL-1ß, IL-12/23, IL-15, IL-16, TNF-ß, VCAM-1, and VEGF). Interestingly, four sIFs (GM-CSF, ICAM-1, IL-7 and IL-12/23) were significantly elevated in MSM-SN compared to seronegative male non-MSM. Conversely, IL-22 and IL-13, cytokines beneficial to gut health, were decreased in all MSM with HIV and MSM-SN respectively. Importantly, all of these sIFs significantly correlated with calprotectin, suggesting they play a role in GI inflammation. Principal coordinate analysis revealed clustering of fecal sIFs by MSM status and significant associations with microbiome composition. Additionally, fecal solutes from participants in the MSM-HIV cohort significantly decreased colonic transcellular fluid transport in vitro, compared to non-MSM-SN, and this decrease associated with overall sIF composition and increased concentrations of eight inflammatory sIFs in participants with HIV. Lastly, elevated levels of plasma, sCD14 and sCD163, directly correlated with decreased transcellular transport and microbiome composition respectively, indicating that sIFs and the gut microbiome are associated with, and potentially contribute to, bacterial translocation. Conclusion: Taken together, these data demonstrate that inflammatory sIFs are elevated in MSM, regardless of HIV infection status, and are associated with the gut microbiome and intestinal barrier function.

Acute Chelation Therapy-Associated Changes in Urine Gadolinium, Self-reported Flare Severity, and Serum Cytokines in Gadolinium Deposition Disease.

OBJECTIVES: The aim of this study was to determine the following in patients who have undergone magnetic resonance imaging with gadolinium-based contrast agents (GBCAs) and meet the proposed diagnostic criteria for gadolinium deposition disease (GDD): (1) the effectiveness of chelation therapy (CT) with intravenous Ca-diethylenetriaminepentaacetic acid in removing retained gadolinium (Gd) and factors affecting the amount removed; (2) the frequency of CT-induced Flare, that is, GDD diagnostic symptom worsening, and factors affecting Flare intensity; (3) whether, as reported in a separate cohort, GDD patients' serum cytokine levels differ significantly from those in healthy normal controls and change significantly in response to CT; and (4) whether urine Gd, Flare reaction, and serum cytokine findings in GDD patients are mimicked in non-ill patients described as having gadolinium storage condition (GSC). MATERIALS AND METHODS: Twenty-one GDD subjects and 3 GSC subjects underwent CT. Patients provided pre-CT and post-CT 24-hour urine samples for Gd content determination along with pre-CT and 24-hour post-CT serum samples for cytokine analysis. Patients rated potential Flare 24 hours after CT. Pre-CT and post-CT 24-hour urine Gd analyses and Luminex serum cytokine assays were performed blind to patients' GDD and GSC status and all other data except age and sex. Serum cytokine levels in a healthy normal control group of age- and sex-matched subjects drawn from Stanford influenza vaccination studies were measured once, contemporaneously with those of GDD and GSC patients, using the same Luminex assay. RESULTS: Urine Gd amounts increased post-CT by 4 times or more after 87% of the 30 CT sessions. The most important factors appeared to be the time since the last GBCA dose and the cumulative dose received. Urine Gd amounts for GDD and GSC patients fell in the same ranges. All GDD patients, and no GSC patient, reported a Flare 24 hours post-CT. Linear regression found that Flare intensity was significantly predicted by a model including pre- and post-CT Gd amounts and the number of GBCA-enhanced magnetic resonance imaging. Post-CT, multiple cytokines showed strong positive relationships with GDD patients' Flare intensity in multivariable models. The pre-CT serum levels of 12 cytokines were significantly different in GDD patients compared with healthy flu vaccine controls. The small number of GSC patients precluded analogous statistical testing. Post-CT, GDD patients' serum levels of 20 cytokines were significantly decreased, and 2 cytokines significantly increased. These cytokines did not exhibit the same change pattern in the 3 GSC patients. The small number of GSC patients precluded statistical comparisons of GSC to GDD patients' results. CONCLUSIONS: In this preliminary study, 24-hour urine Gd content increased markedly and similarly in GDD and GSC patients after Ca-diethylenetriaminepentaacetic acid CT. Post-CT Flare reaction developed only in GDD patients. The current study is the second finding significantly different serum cytokine levels in GDD patients compared with healthy normal controls. These differences and the difference between GDD and GSC patients' Flare and cytokine responses to CT suggest some inflammatory, immunologic, or other physiological differences in patients with GDD. Further research into the treatment and physiological underpinnings of GDD is warranted.

The Gut Microbiota during a Behavioral Weight Loss Intervention.

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

OBJECTIVE: Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS: Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS: A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. CONCLUSIONS: These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

Exhaustive expansion: A novel technique for analyzing complex data generated by higher-order polychromatic flow cytometry experiments.

BACKGROUND: The complex data sets generated by higher-order polychromatic flow cytometry experiments are a challenge to analyze. Here we describe Exhaustive Expansion, a data analysis approach for deriving hundreds to thousands of cell phenotypes from raw data, and for interrogating these phenotypes to identify populations of biological interest given the experimental context. METHODS: We apply this approach to two studies, illustrating its broad applicability. The first examines the longitudinal changes in circulating human memory T cell populations within individual patients in response to a melanoma peptide (gp100209-2M) cancer vaccine, using 5 monoclonal antibodies (mAbs) to delineate subpopulations of viable, gp100-specific, CD8+ T cells. The second study measures the mobilization of stem cells in porcine bone marrow that may be associated with wound healing, and uses 5 different staining panels consisting of 8 mAbs each. RESULTS: In the first study, our analysis suggests that the cell surface markers CD45RA, CD27 and CD28, commonly used in historical lower order (2-4 color) flow cytometry analysis to distinguish memory from naïve and effector T cells, may not be obligate parameters in defining central memory T cells (TCM). In the second study, we identify novel phenotypes such as CD29+CD31+CD56+CXCR4+CD90+Sca1-CD44+, which may characterize progenitor cells that are significantly increased in wounded animals as compared to controls. CONCLUSIONS: Taken together, these results demonstrate that Exhaustive Expansion supports thorough interrogation of complex higher-order flow cytometry data sets and aids in the identification of potentially clinically relevant findings.

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome.

BACKGROUND: Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. METHODS: We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. RESULTS: Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. CONCLUSIONS: This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention.