SARS-CoV-2 Vaccine-Elicited Immunity after B Cell Depletion in Multiple Sclerosis.

The impact of B cell deficiency on the humoral and cellular responses to SARS-CoV2 mRNA vaccination remains a challenging and significant clinical management question. We evaluated vaccine-elicited serological and cellular responses in 1) healthy individuals who were pre-exposed to SARS-CoV-2 (n = 21), 2) healthy individuals who received a homologous booster (mRNA, n = 19; or Novavax, n = 19), and 3) persons with multiple sclerosis on B cell depletion therapy (MS-αCD20) receiving mRNA homologous boosting (n = 36). Pre-exposure increased humoral and CD4 T cellular responses in immunocompetent individuals. Novavax homologous boosting induced a significantly more robust serological response than mRNA boosting. MS-α CD20 had an intact IgA mucosal response and an enhanced CD8 T cell response to mRNA boosting compared with immunocompetent individuals. This enhanced cellular response was characterized by the expansion of only effector, not memory, T cells. The enhancement of CD8 T cells in the setting of B cell depletion suggests a regulatory mechanism between B and CD8 T cell vaccine responses.

Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis.

Lupus nephritis (LN) is a frequent manifestation of systemic lupus erythematosus, and fewer than half of patients achieve complete renal response with standard immunosuppressants. Identifying non-invasive, blood-based pathologic immune alterations associated with renal injury could aid therapeutic decisions. Here, we used mass cytometry immunophenotyping of peripheral blood mononuclear cells in 145 patients with biopsy-proven LN and 40 healthy controls to evaluate the heterogeneity of immune activation in patients with LN and to identify correlates of renal parameters and treatment response. Unbiased analysis identified 3 immunologically distinct groups of patients with LN that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at one year. Patients with enriched circulating granzyme B+ T cells at baseline showed more severe disease and increased numbers of activated CD8 T cells in the kidney, yet they had the highest likelihood of treatment response. A second group characterized primarily by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive by immunophenotyping at enrollment but with chronic renal injuries. Main immune profiles could be distilled down to 5 simple cytometric parameters that recapitulate several of the associations, highlighting the potential for blood immune profiling to translate to clinically useful non-invasive metrics to assess immune-mediated disease in LN.

Expansion of extrafollicular B and T cell subsets in childhood-onset systemic lupus erythematosus.

Introduction: Most childhood-onset SLE patients (cSLE) develop lupus nephritis (cLN), but only a small proportion achieve complete response to current therapies. The prognosis of children with LN and end-stage renal disease is particularly dire. Mortality rates within the first five years of renal replacement therapy may reach 22%. Thus, there is urgent need to decipher and target immune mechanisms that drive cLN. Despite the clear role of autoantibody production in SLE, targeted B cell therapies such as rituximab (anti-CD20) and belimumab (anti-BAFF) have shown only modest efficacy in cLN. While many studies have linked dysregulation of germinal center formation to SLE pathogenesis, other work supports a role for extrafollicular B cell activation in generation of pathogenic antibody secreting cells. However, whether extrafollicular B cell subsets and their T cell collaborators play a role in specific organ involvement in cLN and/or track with disease activity remains unknown. Methods: We analyzed high-dimensional mass cytometry and gene expression data from 24 treatment naïve cSLE patients at the time of diagnosis and longitudinally, applying novel computational tools to identify abnormalities associated with clinical manifestations (cLN) and disease activity (SLEDAI). Results: cSLE patients have an extrafollicular B cell expansion signature, with increased frequency of i) DN2, ii) Bnd2, iii) plasmablasts, and iv) peripheral T helper cells. Most importantly, we discovered that this extrafollicular signature correlates with disease activity in cLN, supporting extrafollicular T/B interactions as a mechanism underlying pediatric renal pathogenesis. Discussion: This study integrates established and emerging themes of extrafollicular B cell involvement in SLE by providing evidence for extrafollicular B and peripheral T helper cell expansion, along with elevated type 1 IFN activation, in a homogeneous cohort of treatment-naïve cSLE patients, a point at which they should display the most extreme state of their immune dysregulation.

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures.

Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in 'At-Risk' populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5 low naïve B cell population was expanded in ACPA-positive FDRs. Further, we developed an "RA immunophenotype score" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets.

Dysregulated Lymphocyte Antigen Receptor Signaling in Common Variable Immunodeficiency with Granulomatous Lymphocytic Interstitial Lung Disease.

PURPOSE: A subset of common variable immunodeficiency (CVID) patients either presents with or develops autoimmune and lymphoproliferative complications, such as granulomatous lymphocytic interstitial lung disease (GLILD), a major cause of morbidity and mortality in CVID. While a myriad of phenotypic lymphocyte derangements has been associated with and described in GLILD, defects in T and B cell antigen receptor (TCR/BCR) signaling in CVID and CVID with GLILD (CVID/GLILD) remain undefined, hindering discovery of biomarkers for disease monitoring, prognostic prediction, and personalized medicine approaches. METHODS: To identify perturbations of immune cell subsets and TCR/BCR signal transduction, we applied mass cytometry analysis to peripheral blood mononuclear cells (PBMCs) from healthy control participants (HC), CVID, and CVID/GLILD patients. RESULTS: Patients with CVID, regardless of GLILD status, had increased frequency of HLADR+CD4+ T cells, CD57+CD8+ T cells, and CD21lo B cells when compared to healthy controls. Within these cellular populations in CVID/GLILD patients only, engagement of T or B cell antigen receptors resulted in discordant downstream signaling responses compared to CVID. In CVID/GLILD patients, CD21lo B cells showed perturbed BCR-mediated phospholipase C gamma and extracellular signal-regulated kinase activation, while HLADR+CD4+ T cells and CD57+CD8+ T cells displayed disrupted TCR-mediated activation of kinases most proximal to the receptor. CONCLUSION: Both CVID and CVID/GLILD patients demonstrate an activated T and B cell phenotype compared to HC. However, only CVID/GLILD patients exhibit altered TCR/BCR signaling in the activated lymphocyte subsets. These findings contribute to our understanding of the mechanisms of immune dysregulation in CVID with GLILD.

Impaired functionality of antigen presenting cells in HIV- exposed uninfected infants in the first six months of life.

HIV-exposed uninfected infants (HEU) have increased morbidity and mortality due to infections in the first 6 months of life that tapers down to 2 years of life. The underlying immunologic defects remain undefined. We investigated antigen-presenting cells (APC) by comparing the phenotype of unstimulated APC, responses to toll-like receptor (TLR) stimulation, and ability to activate natural killer (NK) cells in 24 HEU and 64 HIV-unexposed infants (HUU) at 1-2 days of life (birth) and 28 HEU and 45 HUU at 6 months of life. At birth, unstimulated APC showed higher levels of activation and cytokine production in HEU than HUU and stimulation with TLR agonists revealed lower expression of inflammatory cytokines and activation markers, but similar expression of IL10 regulatory cytokine, in APC from HEU compared to HUU. Differences were still present at 6 months of life. From birth to 6 months, APC underwent extensive phenotypic and functional changes in HUU and minimal changes in HEU. TLR stimulation also generated lower NK cell expression of CD69 and/or IFNγ in HEU compared with HUU at birth and 6 months. In vitro experiments showed that NK IFNγ expression depended on APC cytokine secretion in response to TLR stimulation. Ex vivo IL10 supplementation decreased APC-mediated NK cell activation measured by IFNγ expression. We conclude that APC maturation was stunted or delayed in the first 6 months of life in HEU compared with HUU. Deficient inflammatory APC responses and/or the imbalance between inflammatory and regulatory responses in HEU may play an important role in their increased susceptibility to severe infections.

Specialized interferon action in COVID-19.

The impacts of interferon (IFN) signaling on COVID-19 pathology are multiple, with both protective and harmful effects being documented. We report here a multiomics investigation of systemic IFN signaling in hospitalized COVID-19 patients, defining the multiomics biosignatures associated with varying levels of 12 different type I, II, and III IFNs. The antiviral transcriptional response in circulating immune cells is strongly associated with a specific subset of IFNs, most prominently IFNA2 and IFNG. In contrast, proteomics signatures indicative of endothelial damage and platelet activation associate with high levels of IFNB1 and IFNA6. Seroconversion and time since hospitalization associate with a significant decrease in a specific subset of IFNs. Additionally, differential IFN subtype production is linked to distinct constellations of circulating myeloid and lymphoid immune cell types. Each IFN has a unique metabolic signature, with IFNG being the most associated with activation of the kynurenine pathway. IFNs also show differential relationships with clinical markers of poor prognosis and disease severity. For example, whereas IFNG has the strongest association with C-reactive protein and other immune markers of poor prognosis, IFNB1 associates with increased neutrophil to lymphocyte ratio, a marker of late severe disease. Altogether, these results reveal specialized IFN action in COVID-19, with potential diagnostic and therapeutic implications.

DenVar: density-based variation analysis of multiplex imaging data.

Summary: Multiplex imaging platforms have become popular for studying complex single-cell biology in the tumor microenvironment (TME) of cancer subjects. Studying the intensity of the proteins that regulate important cell-functions becomes extremely crucial for subject-specific assessment of risks. The conventional approach requires selection of two thresholds, one to define the cells of the TME as positive or negative for a particular protein, and the other to classify the subjects based on the proportion of the positive cells. We present a threshold-free approach in which distance between a pair of subjects is computed based on the probability density of the protein in their TMEs. The distance matrix can either be used to classify the subjects into meaningful groups or can directly be used in a kernel machine regression framework for testing association with clinical outcomes. The method gets rid of the subjectivity bias of the thresholding-based approach, enabling easier but interpretable analysis. We analyze a lung cancer dataset, finding the difference in the density of protein HLA-DR to be significantly associated with the overall survival and a triple-negative breast cancer dataset, analyzing the effects of multiple proteins on survival and recurrence. The reliability of our method is demonstrated through extensive simulation studies. Availability and implementation: The associated R package can be found here, https://github.com/sealx017/DenVar. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.

Reproducibility of mass spectrometry based metabolomics data.

BACKGROUND: Assessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets. RESULTS: We apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well. CONCLUSIONS: This research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

The COVIDome Explorer researcher portal.

COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. To expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients, including matched analysis of the whole-blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate herein the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.

The COVIDome Explorer Researcher Portal.

COVID-19 pathology involves dysregulation of diverse molecular, cellular, and physiological processes. In order to expedite integrated and collaborative COVID-19 research, we completed multi-omics analysis of hospitalized COVID-19 patients including matched analysis of the whole blood transcriptome, plasma proteomics with two complementary platforms, cytokine profiling, plasma and red blood cell metabolomics, deep immune cell phenotyping by mass cytometry, and clinical data annotation. We refer to this multidimensional dataset as the COVIDome. We then created the COVIDome Explorer, an online researcher portal where the data can be analyzed and visualized in real time. We illustrate here the use of the COVIDome dataset through a multi-omics analysis of biosignatures associated with C-reactive protein (CRP), an established marker of poor prognosis in COVID-19, revealing associations between CRP levels and damage-associated molecular patterns, depletion of protective serpins, and mitochondrial metabolism dysregulation. We expect that the COVIDome Explorer will rapidly accelerate data sharing, hypothesis testing, and discoveries worldwide.

Seroconversion stages COVID19 into distinct pathophysiological states.

COVID19 is a heterogeneous medical condition involving diverse underlying pathophysiological processes including hyperinflammation, endothelial damage, thrombotic microangiopathy, and end-organ damage. Limited knowledge about the molecular mechanisms driving these processes and lack of staging biomarkers hamper the ability to stratify patients for targeted therapeutics. We report here the results of a cross-sectional multi-omics analysis of hospitalized COVID19 patients revealing that seroconversion status associates with distinct underlying pathophysiological states. Low antibody titers associate with hyperactive T cells and NK cells, high levels of IFN alpha, gamma and lambda ligands, markers of systemic complement activation, and depletion of lymphocytes, neutrophils, and platelets. Upon seroconversion, all of these processes are attenuated, observing instead increases in B cell subsets, emergency hematopoiesis, increased D-dimer, and hypoalbuminemia. We propose that seroconversion status could potentially be used as a biosignature to stratify patients for therapeutic intervention and to inform analysis of clinical trial results in heterogenous patient populations.

Subjects at-risk for future development of rheumatoid arthritis demonstrate a PAD4-and TLR-dependent enhanced histone H3 citrullination and proinflammatory cytokine production in CD14hi monocytes.

The presence of anti-citrullinated protein/peptide antibodies (ACPA) and epitope spreading across the target autoantigens is a unique feature of rheumatoid arthritis (RA). ACPA are present in the peripheral blood for several years prior to the onset of arthritis and clinical classification of RA. ACPA recognize multiple citrullinated proteins, including histone H3 (H3). Intracellular citrullination of H3 in neutrophils and T cells is known to regulate immune cell function by promoting neutrophil extracellular trap formation and citrullinated autoantigen release as well as regulating the Th2/Th17 T cell phenotypic balance. However, the roles of H3 citrullination in other immune cells are not fully elucidated. We aimed to explore H3 citrullination and cytokine/metabolomic signatures in peripheral blood immune cells from subjects prior to and after the onset of RA, at baseline and in response to ex vivo toll-like receptor (TLR) stimulation. Here, we analyzed 13 ACPA (+) subjects without arthritis but at-risk for future development of RA, 14 early RA patients, and 13 healthy controls. We found significantly elevated H3 citrullination in CD14hi monocytes, as well as CD1c+ dendritic cells and CD66+ granulocytes. Unsupervised analysis identified two distinct subsets in CD14hi monocytes characterized by H3 modification and unique cytokine/metabolomic signatures. CD14hi monocytes with elevated TLR-stimulated H3 citrullination were significantly increased in ACPA (+) at-risk subjects. These cells were skewed to produce TNFα, MIP1ß, IFNα, and partially IL-12. Additionally, they demonstrate peptidyl arginine deiminase 4 (PAD4) mediated upregulation of the glycolytic enzyme PFKFB3. These CD14hi monocytes with elevated H3 citrullination morphologically formed monocyte extracellular traps (METs). Taken together, dysregulated PAD4-driven cytokine production as well as MET formation in CD14hi monocytes in ACPA (+) at-risk subjects likely plays an important role in the development of RA via promoting and perpetuating inflammation and generation of citrullinated autoantigens.

Seroconversion stages COVID19 into distinct pathophysiological states.

COVID19 is a heterogeneous medical condition involving a suite of underlying pathophysiological processes including hyperinflammation, endothelial damage, thrombotic microangiopathy, and end-organ damage. Limited knowledge about the molecular mechanisms driving these processes and lack of staging biomarkers hamper the ability to stratify patients for targeted therapeutics. We report here the results of a cross-sectional multi-omics analysis of hospitalized COVID19 patients revealing that seroconversion status associates with distinct underlying pathophysiological states. Seronegative COVID19 patients harbor hyperactive T cells and NK cells, high levels of IFN alpha, gamma and lambda ligands, markers of systemic complement activation, neutropenia, lymphopenia and thrombocytopenia. In seropositive patients, all of these processes are attenuated, observing instead increases in B cell subsets, emergency hematopoiesis, increased markers of platelet activation, and hypoalbuminemia. We propose that seroconversion status could potentially be used as a biosignature to stratify patients for therapeutic intervention and to inform analysis of clinical trial results in heterogenous patient populations.

Optimization of intracerebroventricular streptozotocin dose for the induction of neuroinflammation and memory impairments in rats.

Intracerebroventricular (ICV) injection of streptozotocin (STZ) is a well established procedure to induce neuroinflammation leading to dementia in experimental animals. However, the optimal dose of STZ has not been determined. In the present study, rats were ICV injected with 1.5, 3 and 6 mg of STZ per kg of body weight. After 21 days, neuroinflammatory markers i.e. TNF-α, IL-1ß, ROS and nitrite were quantified in the hippocampus. Memory function was assessed by the radial arm maze test after 9, 12, 15, 18, 21 days following STZ injection. STZ treatment significantly increased neuroinflammatory markers and decreased memory functions in a dose dependent manner showing optimum effects at the dose of 3 mg/kg.

Serum iron deficiency was associated with lower cognitive development in the children of the Santal tribe of West Bengal.

AIM: Poor cognitive scores and low serum iron have been reported among chronically undernourished children from the Santal tribe, West Bengal. Our aim was to investigate the association between iron status and non-verbal cognitive development. METHODS: We randomly selected 170 children (52.9% boys) aged 5-12 years from the Purulia district of West Bengal during 2007-2008 and assessed their iron status: haemoglobin concentration, serum concentration of iron, ferritin, transferrin, total iron-binding capacity and transferrin saturation. Their non-verbal cognitive development was assessed using the Raven's Coloured Progressive Matrices. RESULTS: The haemoglobin concentration, serum iron, serum ferritin and transferrin saturation levels of the 27 children with an intellectual deficit and the 32 who had a below average intelligence quotient (IQ) were significantly lower (P < .05) than the 65 children with an average IQ. A large number of boys (55.6%) and girls (41.7%) who have an intellectual deficit had stage III iron depletion. The cognitive scores of children with stage II and III iron depletion were significantly lower (P < .05) than those with a normal IQ. CONCLUSION: The iron depletion stage was associated with the severity of non-verbal cognitive impairment and serum ferritin appeared to be a sensitive biomarker for predicting non-verbal cognitive development.

Predictive Modeling for Metabolomics Data.

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.

Vitamin E Reduces Hypobaric Hypoxia-Induced Immune Responses in Male Rats.

In hypobaric hypoxia (HH) at high altitude, the immune responses are changed probably due to oxidative stress-induced production of free radicals and nonradicals. Vitamin E is an antioxidant and protects the cells from oxidative damage. The present study was carried out to study the antioxidant role of vitamin E on the immune changes induced by oxidative stress in HH at high altitude. Select immune responses (phagocytic activity of white blood cell [WBC], cytotoxic activity of splenic mononuclear cells [MNCs], and delayed type of hypersensitivity [DTH]) and hematological changes (total count and differential count [DC] of WBC) were measured in male rats exposed to intermittent HH (at 5486.4 m in a simulated chamber for 8 hours/d for 6 consecutive days) and in normobaric condition with and without p.o. administration of vitamin E in three different doses (20, 40, and 60 mg/kg body weight). The increase of phagocytic activity of blood WBC, and reduction of cytotoxic activity of splenic MNC and DTH response were observed in rats exposed to HH. After the administration of vitamin E at different doses, the immune changes were blocked in a dose-dependent manner. Exposure to HH also led to the elevation of serum corticosterone (CORT), which was arrested after administration of vitamin E. The results indicate that the immune changes in HH at high altitude are probably mediated by the production of free radicals and nonradicals, and vitamin E can block these immune changes by its reactive oxygen species quenching effects.