Peroxisome-derived lipids are self antigens that stimulate invariant natural killer T cells in the thymus.

The development and maturation of semi-invariant natural killer T cells (iNKT cells) rely on the recognition of self antigens presented by CD1d restriction molecules in thymus. The nature of the stimulatory thymic self lipids remains elusive. We isolated lipids from thymocytes and found that ether-bonded mono-alkyl glycerophosphates and the precursors and degradation products of plasmalogens stimulated iNKT cells. Synthetic analogs showed high potency in activating thymic and peripheral iNKT cells. Mice deficient in the peroxisomal enzyme glyceronephosphate O-acyltransferase (GNPAT), essential for the synthesis of ether lipids, had significant alteration of the thymic maturation of iNKT cells and fewer iNKT cells in both thymus and peripheral organs, which confirmed the role of ether-bonded lipids as iNKT cell antigens. Thus, peroxisome-derived lipids are nonredundant self antigens required for the generation of a full iNKT cell repertoire.

Lysine Deacetylases and Regulated Glycolysis in Macrophages.

Regulated cellular metabolism has emerged as a fundamental process controlling macrophage functions, but there is still much to uncover about the precise signaling mechanisms involved. Lysine acetylation regulates the activity, stability, and/or localization of metabolic enzymes, as well as inflammatory responses, in macrophages. Two protein families, the classical zinc-dependent histone deacetylases (HDACs) and the NAD-dependent HDACs (sirtuins, SIRTs), mediate lysine deacetylation. We describe here mechanisms by which classical HDACs and SIRTs directly regulate specific glycolytic enzymes, as well as evidence that links these protein deacetylases to the regulation of glycolysis-related genes. In these contexts, we discuss HDACs and SIRTs as key control points for regulating immunometabolism and inflammatory outputs from macrophages.

Effects of host-directed therapies on the pathology of tuberculosis.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has co-evolved with the human immune system and utilizes multiple strategies to persist within infected cells, to hijack several immune mechanisms, and to cause severe pathology and tissue damage in the host. This delays the efficacy of current antibiotic therapy and contributes to the evolution of multi-drug-resistant strains. These challenges led to the development of the novel approach in TB treatment that involves therapeutic targeting of host immune response to control disease pathogenesis and pathogen growth, namely, host-directed therapies (HDTs). Such HDT approaches can (1) enhance the effect of antibiotics, (2) shorten treatment duration for any clinical form of TB, (3) promote development of immunological memory that could protect against relapse, and (4) ameliorate the immunopathology including matrix destruction and fibrosis associated with TB. In this review we discuss TB-HDT candidates shown to be of clinical relevance that thus could be developed to reduce pathology, tissue damage, and subsequent impairment of pulmonary function. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Host NAD+ metabolism and infections: therapeutic implications.

Nicotinamide adenine dinucleotide (NAD+) is both a crucial coenzyme and a cosubstrate for various metabolic reactions in all living cells. Maintenance of NAD+ levels is essential for cell energy homeostasis, survival, proliferation and function. Mounting evidence points to NAD+ as one of the major modulators of immuno-metabolic circuits, thus regulating immune responses and functions. Recent studies delineate impaired host NAD+ metabolism during chronic infections and inflammation, suggesting NAD+ replenishment as an avenue to ameliorate deleterious inflammatory responses. Here, we discuss aspects of NAD+ biosynthesis and consumption, NAD+ biology during infections and how NAD+ metabolism can be intervened with pharmacologically to enhance the host's immunological fitness against pathogens.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.

Evaluation of Rh-Hemolytic Disease in Neonates and Management with Early Intensive Phototherapy in the Neonatal Intensive Care Unit.

BACKGROUND OF THE STUDY: In neonates with Rh-hemolytic disease, light emitting diode (LED) phototherapy allows delivery of high spectral irradiance (SI). A linear correlation exists between SI and efficacy of phototherapy with no saturation point. There is scant data on evaluation and early phototherapy using LED units in Rh-hemolytic disease. OBJECTIVE: This study aimed to describe the hemoglobin (Hb), hematocrit (Hct), total serum bilirubin (TSB), phototherapy parameters and short-term outcomes in neonates with Rh-hemolytic disease. METHODOLOGY: Maternal parameters for Rh-isoimmunization were recorded and monitoring of fetal anemia by Doppler ultrasound was done. Early intensive phototherapy within 1 h of birth was initiated for cord blood Hb below 13.6 g/dl and/or TSB greater than 2.8 mg/dl. RESULTS: Fifty Rh positive neonates were enrolled of which 11/50 (22%) received intrauterine transfusions. The maximum TSB remained below 18 mg/dl in 42/50 (84%) of neonates. The mean SI on the trunk was 56.260 ± 8.768 µW/cm2/nm and duration of phototherapy was 7 ± 3.29 days (mean ± SD). There was a positive correlation between strength of indirect antiglobulin test and cord blood Hb: correlation coefficient (r) = 0.295; direct antiglobulin test and duration of phototherapy: r = 0.331. Early packed red blood cell (PRBC) transfusion was required in 8/50 (16%) neonates while 20/50 (40%) required late transfusions. CONCLUSION: With a mean SI of 56.260 ± 8.768 µW/cm2/nm on the trunk, TSB remained below 18 mg/dl in majority thereby avoiding exchange transfusion. Early or late PRBC transfusion requirement was 1 (1-2) (median ± interquartile range).

Metformin Alters Human Host Responses to Mycobacterium tuberculosis in Healthy Subjects.

BACKGROUND: Metformin, the most widely administered diabetes drug, has been proposed as a candidate adjunctive host-directed therapy for tuberculosis, but little is known about its effects on human host responses to Mycobacterium tuberculosis. METHODS: We investigated in vitro and in vivo effects of metformin in humans. RESULTS: Metformin added to peripheral blood mononuclear cells from healthy volunteers enhanced in vitro cellular metabolism while inhibiting the mammalian target of rapamycin targets p70S6K and 4EBP1, with decreased cytokine production and cellular proliferation and increased phagocytosis activity. Metformin administered to healthy human volunteers led to significant downregulation of genes involved in oxidative phosphorylation, mammalian target of rapamycin signaling, and type I interferon response pathways, particularly following stimulation with M. tuberculosis, and upregulation of genes involved in phagocytosis and reactive oxygen species production was increased. These in vivo effects were accompanied by a metformin-induced shift in myeloid cells from classical to nonclassical monocytes. At a functional level, metformin lowered ex vivo production of tumor necrosis factor α, interferon γ, and interleukin 1ß but increased phagocytosis activity and reactive oxygen species production. CONCLUSION: Metformin has a range of potentially beneficial effects on cellular metabolism, immune function, and gene transcription involved in innate host responses to M. tuberculosis.

Does oxidative stress contribute to adverse outcomes in HIV-associated TB?

In HIV infection, oxidative stress is a pronounced phenomenon, with likely links to HIV-related pathologies and the progression of HIV infection per se. TB is an AIDS-defining condition. HIV-associated oxidative stress, like that associated with diabetes mellitus, might adversely impact the outcomes of TB, probably through increased propensity for generation of metabolically dormant mycobacterial persisters, alongside other mechanisms. This hypothesis might help in guiding the exploration of relevant research directions to improve the care of patients.

Multitarget, quantitative nanoplasmonic electrical field-enhanced resonating device (NE2RD) for diagnostics.

Recent advances in biosensing technologies present great potential for medical diagnostics, thus improving clinical decisions. However, creating a label-free general sensing platform capable of detecting multiple biotargets in various clinical specimens over a wide dynamic range, without lengthy sample-processing steps, remains a considerable challenge. In practice, these barriers prevent broad applications in clinics and at patients' homes. Here, we demonstrate the nanoplasmonic electrical field-enhanced resonating device (NE(2)RD), which addresses all these impediments on a single platform. The NE(2)RD employs an immunodetection assay to capture biotargets, and precisely measures spectral color changes by their wavelength and extinction intensity shifts in nanoparticles without prior sample labeling or preprocessing. We present through multiple examples, a label-free, quantitative, portable, multitarget platform by rapidly detecting various protein biomarkers, drugs, protein allergens, bacteria, eukaryotic cells, and distinct viruses. The linear dynamic range of NE(2)RD is five orders of magnitude broader than ELISA, with a sensitivity down to 400 fg/mL This range and sensitivity are achieved by self-assembling gold nanoparticles to generate hot spots on a 3D-oriented substrate for ultrasensitive measurements. We demonstrate that this precise platform handles multiple clinical samples such as whole blood, serum, and saliva without sample preprocessing under diverse conditions of temperature, pH, and ionic strength. The NE(2)RD's broad dynamic range, detection limit, and portability integrated with a disposable fluidic chip have broad applications, potentially enabling the transition toward precision medicine at the point-of-care or primary care settings and at patients' homes.

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

Mutations in genes for the F420 biosynthetic pathway and a nitroreductase enzyme are the primary resistance determinants in spontaneous in vitro-selected PA-824-resistant mutants of Mycobacterium tuberculosis.

Alleviating the burden of tuberculosis (TB) requires an understanding of the genetic basis that determines the emergence of drug-resistant mutants. PA-824 (pretomanid) is a bicyclic nitroimidazole class compound presently undergoing the phase III STAND clinical trial, despite lacking identifiable genetic markers for drug-specific resistant Mycobacterium tuberculosis. In the present study, we aimed to characterize the genetic polymorphisms of spontaneously generated PA-824-resistant mutant strains by surveying drug metabolism genes for potential mutations. Of the 183 independently selected PA-824-resistant M. tuberculosis mutants, 83% harbored a single mutation in one of five nonessential genes associated with either PA-824 prodrug activation (ddn, 29%; fgd1, 7%) or the tangential F420 biosynthetic pathway (fbiA, 19%; fbiB, 2%; fbiC, 26%). Crystal structure analysis indicated that identified mutations were specifically located within the protein catalytic domain that would hinder the activity of the enzymes required for prodrug activation. This systematic analysis conducted of genotypes resistant to PA-824 may contribute to future efforts in monitoring clinical strain susceptibility with this new drug therapy.

ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.

A two-stage transformer based network for motor imagery classification.

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.

Fetal liver CD34+ contain human immune and endothelial progenitors and mediate solid tumor rejection in NOG mice.

BACKGROUND: Transplantation of CD34+ hematopoietic stem and progenitor cells (HSPC) into immunodeficient mice is an established method to generate humanized mice harbouring a human immune system. Different sources and methods for CD34+ isolation have been employed by various research groups, resulting in customized models that are difficult to compare. A more detailed characterization of CD34+ isolates is needed for a better understanding of engraftable hematopoietic and potentially non-hematopoietic cells. Here we have performed a direct comparison of CD34+ isolated from cord blood (CB-CD34+) or fetal liver (FL-CD34+ and FL-CD34+CD14-) and their engraftment into immunocompromised NOD/Shi-scid Il2rgnull (NOG) mice. METHODS: NOG mice were transplanted with either CB-CD34+, FL-CD34+ or FL-CD34+CD14- to generate CB-NOG, FL-NOG and FL-CD14--NOG, respectively. After 15-20 weeks, the mice were sacrificed and human immune cell reconstitution was assessed in blood and several organs. Liver sections were pathologically assessed upon Haematoxylin and Eosin staining. To assess the capability of allogenic tumor rejection in CB- vs. FL-reconstituted mice, animals were subcutaneously engrafted with an HLA-mismatched melanoma cell line. Tumor growth was assessed by calliper measurements and a Luminex-based assay was used to compare the cytokine/chemokine profiles. RESULTS: We show that CB-CD34+ are a uniform population of HSPC that reconstitute NOG mice more rapidly than FL-CD34+ due to faster B cell development. However, upon long-term engraftment, FL-NOG display increased numbers of neutrophils, dendritic cells and macrophages in multiple tissues. In addition to HSPC, FL-CD34+ isolates contain non-hematopoietic CD14+ endothelial cells that enhance the engraftment of the human immune system in FL-NOG mice. We demonstrate that these CD14+CD34+ cells are capable of reconstituting Factor VIII-producing liver sinusoidal endothelial cells (LSEC) in FL-NOG. However, CD14+CD34+ also contribute to hepatic sinusoidal dilatation and immune cell infiltration, which may culminate in a graft-versus-host disease (GVHD) pathology upon long-term engraftment. Finally, using an HLA-A mismatched CDX melanoma model, we show that FL-NOG, but not CB-NOG, can mount a graft-versus-tumor (GVT) response resulting in tumor rejection. CONCLUSION: Our results highlight important phenotypical and functional differences between CB- and FL-NOG and reveal FL-NOG as a potential model to study hepatic sinusoidal dilatation and mechanisms of GVT.

Rv0495c regulates redox homeostasis in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) has evolved sophisticated surveillance mechanisms to neutralize the ROS-induces toxicity which otherwise would degrade a variety of biological molecules including proteins, nucleic acids and lipids. In the present study, we find that Mtb lacking the Rv0495c gene (ΔRv0495c) is presented with a highly oxidized cytosolic environment. The superoxide-induced lipid peroxidation resulted in altered colony morphology and loss of membrane integrity in ΔRv0495c. As a consequence, ΔRv0495c demonstrated enhanced susceptibility when exposed to various host-induced stress conditions. Further, as expected, we observed a mutant-specific increase in the abundance of transcripts that encode proteins involved in antioxidant defence. Surprisingly, despite showing a growth defect phenotype in macrophages, the absence of the Rv0495c enhanced the pathogenicity and augmented the ability of the Mtb to grow inside the host. Additionally, our study revealed that Rv0495c-mediated immunomodulation by the pathogen helps create a favorable niche for long-term survival of Mtb inside the host. In summary, the current study underscores the fact that the truce in the war between the host and the pathogen favours long-term disease persistence in tuberculosis. We believe targeting Rv0495c could potentially be explored as a strategy to potentiate the current anti-TB regimen.

Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity.

Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.

T cell recognition of non-peptidic antigens in infectious diseases.

The immune system has evolved to recognize a wide range of antigenic molecules of self and non-self origin. The stimulatory antigens form complexes with antigen-presenting molecules and directly interact with the T cell receptor (TCR). Peptidic antigens associate with major histocompatibility complex (MHC) molecules and therefore, are indicated as MHC-restricted. Non-peptidic antigens do not bind to MHC molecules and are presented by other classes of antigen-presenting molecules. These non-MHC restricted antigens include glycolipid molecules, phosphorylated metabolites of the mevalonate pathway and vitamin B2 precursors. T cells specific for non-peptidic antigens have important roles in host defense against infections, autoimmunity, allergies and tumour immunosurveillance. Hence, understanding the molecular interactions between the antigen presenting cell (APC) and the T cells with non-peptidic specificity is of great relevance. Here, we review current knowledge of this type of T cells, their TCR repertoire, the structural aspects of recognized antigens, the mode of antigen recognition, and their function with special emphasis on their role in infectious diseases.

Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals.

Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.

Deep temporal networks for EEG-based motor imagery recognition.

The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.