The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.

BACKGROUND: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS: Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. RESULTS: Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). CONCLUSIONS: Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.

High-density lipoprotein cholesterol subfraction HDL2 is associated with improved endothelial function in systemic lupus erythematosus.

OBJECTIVE: Patients with systemic lupus erythematosus (SLE) have increased risk of premature atherosclerosis but the exact mechanisms remains unclear. Flow-mediated dilatation (FMD) is an established non-invasive assessment of vascular endothelial function. Lipoprotein subfractions may be better predictors of FMD than conventional cholesterol measurements. We tested the hypothesis that lipoprotein subfractions are independently associated with FMD. METHODS: Forty-one consecutive adult patients with SLE without known cardiovascular risk factors or disease were recruited in this cross-sectional study. Endothelial function and early atherosclerosis were assessed by brachial FMD and common carotid artery (CCA) intima-media thickness (IMT). High-density lipoprotein (HDL)/low-density lipoprotein (LDL) subfractions were measured. Machine learning models were also constructed to predict FMD and CCA IMT. RESULTS: Median FMD was 4.48% (IQR 5.00%) while median IMT was 0.54 mm (IQR 0.12 mm). Univariate analysis showed lower LDL1 (r=-0.313, p<0.05) and higher HDL2 subfractions (r=0.313, p<0.05) were significantly associated with higher log-transformed FMD. In a multiple linear regression model, HDL2 (ß=0.024, SE=0.012, p<0.05) remained an independent predictor of higher FMD after adjusting for age, body mass index, LDL1 and systolic blood pressure. The machine learning model included parameters such as HDL2 (positive association), prednisolone dose, LDL cholesterol and LDL1 for prediction of FMD (r=0.433, p<0.01). Age, LDL cholesterol and systolic blood pressure were independently associated with higher CCA IMT after adjusting for body mass index and HDL2. CONCLUSIONS: HDL 2, a large HDL particle, was independently associated with greater FMD and may be a biomarker of vascular health in SLE.

Combining multimodal magnetic resonance brain imaging and machine learning to unravel neurocognitive function in non-neuropsychiatric systemic lupus erythematosus.

OBJECTIVE: To study whether multimodal brain MRI comprising permeability and perfusion measures coupled with machine learning can predict neurocognitive function in young patients with SLE without neuropsychiatric manifestations. METHODS: SLE patients and healthy controls (HCs) (≤40 years of age) underwent multimodal structural brain MRI that comprised voxel-based morphometry (VBM), magnetization transfer ratio (MTR) and dynamic contrast-enhanced (DCE) MRI in this cross-sectional study. Neurocognitive function assessed by Automated Neuropsychological Assessment Metrics was reported as the total throughput score (TTS). Olfactory function was assessed. A machine learning-based model (i.e. glmnet) was constructed to predict TTS. RESULTS: Thirty SLE patients and 10 HCs were studied. Both groups had comparable VBM, MTR, olfactory bulb volume (OBV), olfactory function and TTS. While after correction for multiple comparisons the uncorrected increase in the blood-brain barrier (BBB) permeability parameters compared with HCs did not remain evident in SLE patients, DCE-MRI perfusion parameters, notably an increase in right amygdala perfusion, was positively correlated with TTS in SLE patients (r = 0.636, false discovery rate P < 0.05). A machine learning-trained multimodal MRI model comprising alterations of VBM, MTR, OBV and DCE-MRI parameters mainly in the limbic system regions predicted TTS in SLE patients (r = 0.644, P < 0.0005). CONCLUSION: Multimodal brain MRI demonstrated increased right amygdala perfusion that was associated with better neurocognitive performance in young SLE patients without statistically significant BBB leakage and microstructural abnormalities. A machine learning-constructed multimodal model comprising microstructural, perfusion and permeability parameters accurately predicted neurocognitive performance in SLE patients.

Platelet TLR7 is essential for the formation of platelet-neutrophil complexes and low-density neutrophils in lupus nephritis.

OBJECTIVES: Platelets and low-density neutrophils (LDNs) are major players in the immunopathogenesis of SLE. Despite evidence showing the importance of platelet-neutrophil complexes (PNCs) in inflammation, little is known about the relationship between LDNs and platelets in SLE. We sought to characterize the role of LDNs and Toll-like receptor 7 (TLR7) in clinical disease. METHODS: Flow cytometry was used to immunophenotype LDNs from SLE patients and controls. The association of LDNs with organ damage was investigated in a cohort of 290 SLE patients. TLR7 mRNA expression was assessed in LDNs and high-density neutrophils (HDNs) using publicly available mRNA sequencing datasets and our own cohort using RT-PCR. The role of TLR7 in platelet binding was evaluated in platelet-HDN mixing studies using TLR7-deficient mice and Klinefelter syndrome patients. RESULTS: SLE patients with active disease have more LDNs, which are heterogeneous and more immature in patients with evidence of kidney dysfunction. LDNs are platelet bound, in contrast to HDNs. LDNs settle in the peripheral blood mononuclear cell (PBMC) layer due to the increased buoyancy and neutrophil degranulation from platelet binding. Mixing studies demonstrated that this PNC formation was dependent on platelet-TLR7 and that the association results in increased NETosis. The neutrophil:platelet ratio is a useful clinical correlate for LDNs, and a higher NPR is associated with past and current flares of LN. CONCLUSIONS: LDNs sediment in the upper PBMC fraction due to PNC formation, which is dependent on the expression of TLR7 in platelets. Collectively, our results reveal a novel TLR7-dependent crosstalk between platelets and neutrophils that may be an important therapeutic opportunity for LN.

Dynamic altruistic cooperation within breast tumors.

BACKGROUND: Social behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism's survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance. METHODS: MicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells. RESULTS: Opposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKß. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit. CONCLUSIONS: Our data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

LipidA-IDER to Explore the Global Lipid A Repertoire of Drug-Resistant Gram-Negative Bacteria.

With the global emergence of drug-resistant bacteria causing difficult-to-treat infections, there is an urgent need for a tool to facilitate studies on key virulence and antimicrobial resistant factors. Mass spectrometry (MS) has contributed substantially to the elucidation of the structure-function relationships of lipid A, the endotoxic component of lipopolysaccharide which also serves as an important protective barrier against antimicrobials. Here, we present LipidA-IDER, an automated structure annotation tool for system-level scale identification of lipid A from high-resolution tandem mass spectrometry (MS2) data. LipidA-IDER was validated against previously reported structures of lipid A in the reference bacteria, Escherichia coli and Pseudomonas aeruginosa. Using MS2 data of variable quality, we demonstrated LipidA-IDER annotated lipid A with a performance of 71.2% specificity and 70.9% sensitivity, offering greater accuracy than existing lipidomics software. The organism-independent workflow was further applied to a panel of six bacterial species: E. coli and Gram-negative members of ESKAPE pathogens. A comprehensive atlas comprising 188 distinct lipid A species, including remodeling intermediates, was generated and can be integrated with software including MS-DIAL and Metabokit for identification and semiquantitation. Systematic comparison of a pair of polymyxin-sensitive and polymyxin-resistant Acinetobacter baumannii isolated from a human patient unraveled multiple key lipid A structural features of polymyxin resistance within a single analysis. Probing the lipid A landscape of bacteria using LipidA-IDER thus holds immense potential for advancing our understanding of the vast diversity and structural complexity of a key lipid virulence and antimicrobial-resistant factor. LipidA-IDER is freely available at https://github.com/Systems-Biology-Of-Lipid-Metabolism-Lab/LipidA-IDER.

Distinct transcriptomic and metabolomic profiles characterize NSAID-induced urticaria/angioedema patients undergoing aspirin desensitization.

BACKGROUND: There is limited data on the mechanisms of aspirin desensitization in patients with nonsteroidal anti-inflammatory drug (NSAID)-induced urticaria/angioedema (NIUA). OBJECTIVES: We sought to characterize the transcriptomic and metabolomic profiles of patients with NIUA undergoing aspirin desensitization. METHODS: PBMCs and plasma were separated from the blood of patients with NIUA undergoing aspirin desensitization for coronary artery disease and NSAID-tolerant controls. RNA was isolated from PBMCs and subjected to messenger RNA (mRNA)- and long noncoding RNA (lncRNA)-sequencing. Plasma samples were analyzed using LC-MS/MS for metabolite shifts using a semitargeted metabolomics panel. RESULTS: Eleven patients with NIUA and 10 healthy controls were recruited. The mRNA gene profiles of predesensitization versus postdesensitization and healthy control versus postdesensitization did not differ significantly. However, we identified 739 mRNAs and 888 lncRNAs as differentially expressed from preaspirin desensitization patients and controls. A 12-mRNA gene signature was trained using a machine learning algorithm to distinguish between controls, postdose, and predose samples. Ingenuity Pathway Analysis identified 5 canonical pathways that were significantly enriched in preaspirin desensitization samples. IL-22 was the most upregulated pathway. To investigate the potential regulatory roles of the differentially expressed lncRNA on the mRNAs, 9 lncRNAs and 12 mRNAs showed significantly correlated expression patterns in the IL-22 pathway. To validate the transcriptomics data, IL-22 was measured in the plasma samples of the subjects using ELISA. IL-22 was significantly higher in preaspirin desensitization patients compared with controls. In parallel, metabolomic analysis revealed stark differences in plasma profiles of preaspirin desensitization patients and healthy controls. In particular, 2-hydroxybenzoic acid (salicylic acid) was significantly lower in preaspirin desensitization patients compared with healthy controls. CONCLUSIONS: This is the first study to combine both transcriptomic and metabolomic approaches in patients with NIUA, which contributes to a deeper understanding about the pathogenesis of NIUA and may potentially pave the way toward a molecular diagnosis of NSAID hypersensitivity.

Mapping of γ/δ T cells reveals Vδ2+ T cells resistance to senescence.

BACKGROUND: Immune adaptation with aging is a major of health outcomes. Studies in humans have mainly focus on αß T cells while γδ T cells have been neglected despite their role in immunosurveillance. We investigated the impact of aging on γδ T cell subsets phenotypes, functions, senescence and their molecular response to stress. METHODS: Peripheral blood of young and old donors in Singapore have been used to assess the phenotype, functional capacity, proliferation capacity and gene expression of the various γδ T cell subsets. Peripheral blood mononuclear cells from apheresis cones and young donors have been used to characterize the telomere length, epigenetics profile and DNA damage response of the various γδ T cell subsets phenotype. FINDINGS: Our data shows that peripheral Vδ2+ phenotype, functional capacity (cytokines, cytotoxicity, proliferation) and gene expression profile are specific when compared against all other αß and γδ T cells in aging. Hallmarks of senescence including telomere length, epigenetic profile and DNA damage response of Vδ2+ also differs against all other αß and γδ T cells. INTERPRETATION: Our results highlight the differential impact of lifelong stress on γδ T cells subsets, and highlight possible mechanisms that enable Vδ2+ to be resistant to cellular aging. The new findings reinforce the concept that Vδ2+ have an "innate-like" behavior and are more resilient to the environment as compared to "adaptive-like" Vδ1+ T cells.

Streamlining volumetric multi-channel image cytometry using hue-saturation-brightness-based surface creation.

Image cytometry is the process of converting image data to flow cytometry-style plots, and it usually requires computer-aided surface creation to extract out statistics for cells or structures. One way of dealing with structures stained with multiple markers in three-dimensional images, is carrying out multiple rounds of channel co-localization and image masking before surface creation, which is cumbersome and laborious. We propose the application of the hue-saturation-brightness color space to streamline this process, which produces complete surfaces, and allows the user to have a global view of the data before flexibly defining cell subsets. Spectral compensation can also be performed after surface creation to accurately resolve different signals. We demonstrate the utility of this workflow in static and dynamic imaging datasets of a needlestick injury on the mouse ear, and we believe this scalable and intuitive approach will improve the ease of performing histocytometry on biological samples.

Vδ2+ and α/ß T cells show divergent trajectories during human aging.

Chronological aging and a variety of stressors are driving forces towards immunosenescence. While much attention was paid to the main T cell component, α/ß T cells, few studies concentrate on the impact of age on γ/δ T cells' characteristics. The latter are important players of adaptive immunity but also have features associated with innate immunity. Vδ2+ are the main component of γ/δ while Vδ1+ T cells expand upon Cytomegalovirus (CMV) infection and with age. The Vδ2+ T cells are not influenced by persistent infections but do contribute to immunosurveillance against bacterial pathogens. Here, we focus on Vδ2+ T cells and report that their composition and functionality is not altered in older adults. We have performed a side-by-side comparison of α/ß and Vδ2 cells by using two robust markers of T cell replicative history and cell differentiation (CD28 and CD27), and cytokine secretion (IFN-γ and TNF-α). Significant differences in Vδ2 versus α/ß homeostasis, as well as phenotypic and functional changes emerged. However, the data strongly suggest a sustained functionality of the Vδ2 population with age, independently of the challenge. This suggests differential trajectories towards immunosenescence in α/ß and Vδ2+ T cells, most likely explained by their intrinsic functions.

SDPMOD: an automated comparative modeling server for small disulfide-bonded proteins.

Small disulfide-bonded proteins (SDPs) are rich sources for therapeutic drugs. Designing drugs from these proteins requires three-dimensional structural information, which is only available for a subset of these proteins. SDPMOD addresses this deficit in structural information by providing a freely available automated comparative modeling service to the research community. For expert users, SDPMOD offers a manual mode that permits the selection of a desired template as well as a semi-automated mode that allows users to select the template from a suggested list. Besides the selection of templates, expert users can edit the target-template alignment, thus allowing further customization of the modeling process. Furthermore, the web service provides model stereochemical quality evaluation using PROCHECK. SDPMOD is freely accessible to academic users via the web interface at http://proline.bic.nus.edu.sg/sdpmod.