Anti-inflammatory Quinoline Alkaloids from the Roots of Waltheria indica.

Sixteen new quinoline alkaloids (1a-7, 8a, 9, 10, 13-15, 17, and 21) and 10 known analogs (8b, 11, 12, 16, 18-20, and 22-24), along with three known cyclopeptide alkaloids (25-27), were isolated from the roots of Waltheria indica. The structures of the new compounds were elucidated by detailed NMR and circular dichroism with computational support and mass spectrometry data interpretation. Anti-inflammatory potential of isolates was evaluated based on inhibition of lipopolysaccharide (LPS)-induced nitric oxide (NO) production and tumor necrosis factor-alpha (TNF-α)-induced nuclear factor kappa B (NF-κB) activity with cell culture models. In the absence of cell growth inhibition, compounds 6, 8a, 9-11, 13, 21, and 24 reduced TNF-α-induced NF-κB activity with IC50 values ranging from 7.1 to 12.1 µM, comparable to the positive control (BAY 11-7082, IC50 = 9.7 µM). Compounds 6, 8a, 8b, and 11 showed significant NO-inhibitory activity with IC50 values ranging from 11.0 to 12.8 µM, being more active than the positive control (l-NMMA, IC50 = 22.7 µM). Structure-activity relationships indicated that NO inhibitory activity was significantly affected by C-8 substitution. Inhibition of LPS-induced nitric oxide synthase (iNOS) by 8b [(5S)-waltherione M, IC50 11.7 ± 0.8 µM] correlated with inhibition of iNOS mRNA expression. The biological potential of W. indica metabolites supports the traditional use of this plant for the treatment of inflammatory-related disorders.

On TCR binding predictors failing to generalize to unseen peptides.

Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training set. In this work, we investigate how state-of-the-art deep learning models for TCR-peptide/-pMHC binding prediction generalize to unseen peptides. We create a dataset including positive samples from IEDB, VDJdb, McPAS-TCR, and the MIRA set, as well as negative samples from both randomization and 10X Genomics assays. We name this collection of samples TChard. We propose the hard split, a simple heuristic for training/test split, which ensures that test samples exclusively present peptides that do not belong to the training set. We investigate the effect of different training/test splitting techniques on the models' test performance, as well as the effect of training and testing the models using mismatched negative samples generated randomly, in addition to the negative samples derived from assays. Our results show that modern deep learning methods fail to generalize to unseen peptides. We provide an explanation why this happens and verify our hypothesis on the TChard dataset. We then conclude that robust prediction of TCR recognition is still far for being solved.

NKG2A and HLA-E define an alternative immune checkpoint axis in bladder cancer.

Programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1)-blockade immunotherapies have limited efficacy in the treatment of bladder cancer. Here, we show that NKG2A associates with improved survival and responsiveness to PD-L1 blockade immunotherapy in bladder tumors that have high abundance of CD8+ T cells. In bladder tumors, NKG2A is acquired on CD8+ T cells later than PD-1 as well as other well-established immune checkpoints. NKG2A+ PD-1+ CD8+ T cells diverge from classically defined exhausted T cells through their ability to react to human leukocyte antigen (HLA) class I-deficient tumors using T cell receptor (TCR)-independent innate-like mechanisms. HLA-ABC expression by bladder tumors is progressively diminished as disease progresses, framing the importance of targeting TCR-independent anti-tumor functions. Notably, NKG2A+ CD8+ T cells are inhibited when HLA-E is expressed by tumors and partly restored upon NKG2A blockade in an HLA-E-dependent manner. Overall, our study provides a framework for subsequent clinical trials combining NKG2A blockade with other T cell-targeted immunotherapies, where tumors express higher levels of HLA-E.

Coumarinolignans with Reactive Oxygen Species (ROS) and NF-κB Inhibitory Activities from the Roots of Waltheria indica.

Seven new coumarinolignans, walthindicins A-F (1a, 1b, 2-5, 7), along with five known analogs (6, 8-11), were isolated from the roots of Waltheria indica. The structures of the new compounds are determined by detailed nuclear magnetic resonance (NMR), circular dichroism (CD) with extensive computational support, and mass spectroscopic data interpretation. Compounds were tested for their antioxidant activity in Human Cervical Cancer cells (HeLa cells). Compounds 1a and 6 showed higher reactive oxygen species (ROS) inhibitory activity at 20 µg/mL when compared with other natural compound-based antioxidants such as ascorbic acid. Considering the role of ROS in nuclear-factor kappa B (NF-κB) activation, compounds 1a and 6 were evaluated for NF-κB inhibitory activity and showed a concentration-dependent inhibition in Human Embryonic Kidney 293 cells (Luc-HEK-293).

TGM4: an immunogenic prostate-restricted antigen.

BACKGROUND: Prostate cancer is the second leading cause of cancer-related death in men in the USA; death occurs when patients progress to metastatic castration-resistant prostate cancer (CRPC). Although immunotherapy with the Food and Drug Administration-approved vaccine sipuleucel-T, which targets prostatic acid phosphatase (PAP), extends survival for 2-4 months, the identification of new immunogenic tumor-associated antigens (TAAs) continues to be an unmet need. METHODS: We evaluated the differential expression profile of castration-resistant prostate epithelial cells that give rise to CRPC from mice following an androgen deprivation/repletion cycle. The expression levels of a set of androgen-responsive genes were further evaluated in prostate, brain, colon, liver, lung, skin, kidney, and salivary gland from murine and human databases. The expression of a novel prostate-restricted TAA was then validated by immunostaining of mouse tissues and analyzed in primary tumors across all human cancer types in The Cancer Genome Atlas. Finally, the immunogenicity of this TAA was evaluated in vitro and in vivo using autologous coculture assays with cells from healthy donors as well as by measuring antigen-specific antibodies in sera from patients with prostate cancer (PCa) from a neoadjuvant clinical trial. RESULTS: We identified a set of androgen-responsive genes that could serve as potential TAAs for PCa. In particular, we found transglutaminase 4 (Tgm4) to be highly expressed in prostate tumors that originate from luminal epithelial cells and only expressed at low levels in most extraprostatic tissues evaluated. Furthermore, elevated levels of TGM4 expression in primary PCa tumors correlated with unfavorable prognosis in patients. In vitro and in vivo assays confirmed the immunogenicity of TGM4. We found that activated proinflammatory effector memory CD8 and CD4 T cells were expanded by monocyte-derived dendritic cell (moDCs) pulsed with TGM4 to a greater extent than moDCs pulsed with either PAP or prostate-specific antigen (PSA), and T cells primed with TGM4-pulsed moDCs produce functional cytokines following a prime/boost regiment or in vitro stimulation. An IgG antibody response to TGM4 was detected in 30% of vaccinated patients, while fewer than 8% of vaccinated patients developed antibody responses to PSA or prostate-specific membrane antigen (PSMA). CONCLUSIONS: These results suggest that TGM4 is an immunogenic, prostate-restricted antigen with the potential for further development as an immunotherapy target.

Landscape and selection of vaccine epitopes in SARS-CoV-2.

BACKGROUND: Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE). METHODS: We explored the set of computationally predicted SARS-CoV-2 HLA-I and HLA-II ligands, examining protein source, concurrent human/murine coverage, and population coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, sequence conservation, source protein abundance, and coverage of high frequency HLA alleles. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering for surface accessibility, sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. RESULTS: From 58 initial candidates, three B cell epitope regions were identified. From 3730 (MHC-I) and 5045 (MHC-II) candidate ligands, 292 CD8+ and 284 CD4+ T cell epitopes were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we proposed a set of 22 SARS-CoV-2 vaccine peptides for use in subsequent murine studies. We curated a dataset of ~ 1000 observed T cell epitopes from convalescent COVID-19 patients across eight studies, showing 8/15 recurrent epitope regions to overlap with at least one of our candidate peptides. Of the 22 candidate vaccine peptides, 16 (n = 10 T cell epitope optimized; n = 6 B cell epitope optimized) were manually selected to decrease their degree of sequence overlap and then synthesized. The immunogenicity of the synthesized vaccine peptides was validated using ELISpot and ELISA following murine vaccination. Strong T cell responses were observed in 7/10 T cell epitope optimized peptides following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides. CONCLUSIONS: Overall, we find our selection process and vaccine formulation to be appropriate for identifying T cell epitopes and eliciting T cell responses against those epitopes. Further studies are needed to optimize prediction and induction of B cell responses, as well as study the protective capacity of predicted T and B cell epitopes.

Zinc limitation triggers anticipatory adaptations in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) has complex and dynamic interactions with the human host, and subpopulations of Mtb that emerge during infection can influence disease outcomes. This study implicates zinc ion (Zn2+) availability as a likely driver of bacterial phenotypic heterogeneity in vivo. Zn2+ sequestration is part of "nutritional immunity", where the immune system limits micronutrients to control pathogen growth, but this defense mechanism seems to be ineffective in controlling Mtb infection. Nonetheless, Zn2+-limitation is an environmental cue sensed by Mtb, as calprotectin triggers the zinc uptake regulator (Zur) regulon response in vitro and co-localizes with Zn2+-limited Mtb in vivo. Prolonged Zn2+ limitation leads to numerous physiological changes in vitro, including differential expression of certain antigens, alterations in lipid metabolism and distinct cell surface morphology. Furthermore, Mtb enduring limited Zn2+ employ defensive measures to fight oxidative stress, by increasing expression of proteins involved in DNA repair and antioxidant activity, including well described virulence factors KatG and AhpC, along with altered utilization of redox cofactors. Here, we propose a model in which prolonged Zn2+ limitation defines a population of Mtb with anticipatory adaptations against impending immune attack, based on the evidence that Zn2+-limited Mtb are more resistant to oxidative stress and exhibit increased survival and induce more severe pulmonary granulomas in mice. Considering that extracellular Mtb may transit through the Zn2+-limited caseum before infecting naïve immune cells or upon host-to-host transmission, the resulting phenotypic heterogeneity driven by varied Zn2+ availability likely plays a key role during early interactions with host cells.

Shared Immunogenic Poly-Epitope Frameshift Mutations in Microsatellite Unstable Tumors.

Microsatellite instability-high (MSI-H) tumors are characterized by high tumor mutation burden and responsiveness to checkpoint blockade. We identified tumor-specific frameshifts encoding multiple epitopes that originated from indel mutations shared among patients with MSI-H endometrial, colorectal, and stomach cancers. Epitopes derived from these shared frameshifts have high population occurrence rates, wide presence in many tumor subclones, and are predicted to bind to the most frequent MHC alleles in MSI-H patient cohorts. Neoantigens arising from these mutations are distinctly unlike self and viral antigens, signifying novel groups of potentially highly immunogenic tumor antigens. We further confirmed the immunogenicity of frameshift peptides in T cell stimulation experiments using blood mononuclear cells isolated from both healthy donors and MSI-H cancer patients. Our study uncovers the widespread occurrence and strong immunogenicity of tumor-specific antigens derived from shared frameshift mutations in MSI-H cancer and Lynch syndrome patients, suitable for the design of common "off-the-shelf" cancer vaccines.

A Comprehensive Phenotypic and Functional Immune Analysis Unravels Circulating Anti-Phospholipase A2 Receptor Antibody Secreting Cells in Membranous Nephropathy Patients.

INTRODUCTION: Primary membranous nephropathy (MN) is characterized by the presence of antipodocyte antibodies, but studies describing phenotypic and functional abnormalities in circulating lymphocytes are limited. METHODS: We analyzed 68 different B- and T-cell subsets using flow cytometry in 30 MN patients (before initiating immunosuppression) compared with 31 patients with non-immune-mediated chronic kidney disease (CKD) and 12 healthy individuals. We also measured 19 serum cytokines in MN patients and in healthy controls. Lastly, we quantified the ex vivo production of phospholipase A2 receptor (PLA2R)-specific IgG by plasmablasts (measuring antibodies in culture supernatants and by the newly developed FluoroSpot assay [AutoImmun Diagnostika, Strasberg, Germany]) and assessed the circulating antibody repertoire by phage immunoprecipitation sequencing (PhIP-Seq). RESULTS: After adjusting for multiple testing, plasma cells and regulatory B cells (BREG) were significantly higher (P < 0.05) in MN patients compared with both control groups. The percentages of circulating plasma cells correlated with serum anti-PLA2R antibody levels (P = 0.042) and were associated with disease activity. Ex vivo-expanded PLA2R-specific IgG-producing plasmablasts generated from circulating PLA2R-specific memory B cells (mBCs) correlated with serum anti-PLA2R IgG antibodies (P < 0.001) in MN patients. Tumor necrosis factor-α (TNF-α) was the only significantly increased cytokine in MN patients (P < 0.05), whereas there was no significant difference across study groups in the autoantibody and antiviral antibody repertoire. CONCLUSION: This extensive phenotypic and functional immune characterization shows that autoreactive plasma cells are present in the circulation of MN patients, providing a new therapeutic target and a candidate biomarker of disease activity.

Production and excretion of astaxanthin by engineered Yarrowia lipolytica using plant oil as both the carbon source and the biocompatible extractant.

This study aimed to develop a bioprocess using plant oil as the carbon source for lipid-assimilating yeast to produce high-value astaxanthin. Using high-oleic safflower oil as a model, efficient cell growth and astaxanthin production by the engineered Yarrowia lipolytica strain ST7403 was demonstrated, and a considerable portion of astaxanthin was found excreted into the spent oil. Astaxanthin was the predominant carotenoid in the extracellular oil phase that allowed facile in situ recovery of astaxanthin without cell lysis. Autoclaving the safflower oil medium elevated the peroxide level but it declined quickly during fermentation (reduced by 84% by day 3) and did not inhibit cell growth or astaxanthin production. In a 1.5-L fed-batch bioreactor culture with a YnB-based medium containing 20% safflower oil, and with the feeding of casamino acids, astaxanthin production reached 54 mg/L (53% excreted) in 28 days. Further improvement in astaxanthin titer and productivity was achieved by restoring leucine biosynthesis in the host, and running fed-batch fermentation using a high carbon-to-nitrogen ratio yeast extract/peptone medium containing 70% safflower oil, with feeding of additional yeast extract/peptone, to attain 167 mg/L astaxanthin (48% excreted) in 9.5 days of culture. These findings facilitate industrial microbial biorefinery development that utilizes renewable lipids as feedstocks to not only produce high-value products but also effectively extract and recover the products, including non-native ones.Key Points• Yarrowia lipolytica can use plant oil as a C-source for astaxanthin production.• Astaxanthin is excreted and accumulated in the extracellular oil phase.• Astaxanthin is the predominant carotenoid in the extracellular oil phase.• Plant oil serves as a biocompatible solvent for in situ astaxanthin extraction. Graphical abstract.

MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing.

Computational prediction of the peptides presented on major histocompatibility complex (MHC) class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors have expanded with the use of mass spectrometry to identify naturally presented MHC ligands. In addition to elucidating binding motifs, the identified ligands also reflect the antigen processing steps that occur prior to MHC binding. Here, we developed an integrated predictor of MHC class I presentation that combines new models for MHC class I binding and antigen processing. Considering only peptides first predicted by the binding model to bind strongly to MHC, the antigen processing model is trained to discriminate published mass spectrometry-identified MHC class I ligands from unobserved peptides. The integrated model outperformed the two individual components as well as NetMHCpan 4.0 and MixMHCpred 2.0.2 on held-out mass spectrometry experiments. Our predictors are implemented in the open source MHCflurry package, version 2.0 (github.com/openvax/mhcflurry).

High-Throughput MHC I Ligand Prediction Using MHCflurry.

MHCflurry is an open source package for peptide/MHC I binding affinity prediction. Its command-line and programmatic interfaces make it well-suited for integration into high-throughput bioinformatic pipelines. Users can download models fit to publicly available data or train predictors on their own affinity measurements or mass spec datasets. This chapter gives a tutorial on essential MHCflurry functionality, including generating predictions, training new models, and using the MHCflurry Python interface. MHCflurry is available at https://github.com/openvax/mhcflurry .

Bioinformatic methods for cancer neoantigen prediction.

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

MHCflurry: Open-Source Class I MHC Binding Affinity Prediction.

Predicting the binding affinity of major histocompatibility complex I (MHC I) proteins and their peptide ligands is important for vaccine design. We introduce an open-source package for MHC I binding prediction, MHCflurry. The software implements allele-specific neural networks that use a novel architecture and peptide encoding scheme. When trained on affinity measurements, MHCflurry outperformed the standard predictors NetMHC 4.0 and NetMHCpan 3.0 overall and particularly on non-9-mer peptides in a benchmark of ligands identified by mass spectrometry. The released predictor, MHCflurry 1.2.0, uses mass spectrometry datasets for model selection and showed competitive accuracy with standard tools, including the recently released NetMHCpan 4.0, on a small benchmark of affinity measurements. MHCflurry's prediction speed exceeded 7,000 predictions per second, 396 times faster than NetMHCpan 4.0. MHCflurry is freely available to use, retrain, or extend, includes Python library and command line interfaces, may be installed using package managers, and applies software development best practices.

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer.

BACKGROUND: Patients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown. METHODS: We sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for high grade serous ovarian carcinoma patients enrolled in the Australian Ovarian Cancer Study. In this series, 35 of 114 samples were collected after exposure to chemotherapy; 14 are matched with an untreated sample from the same patient. Our approach integrates whole genome and RNA sequencing of bulk tumor samples with class I MHC binding prediction and mutational signatures extracted from studies of chemotherapy-exposed Caenorhabditis elegans and Gallus gallus cells. We additionally investigated the relationship between neoantigens, tumor infiltrating immune cells estimated from RNA-seq with CIBERSORT, and patient survival. RESULTS: Greater neoantigen burden and CD8+ T cell infiltration in primary, pre-treatment samples were independently associated with improved survival. Relapse samples collected after chemotherapy harbored a median of 78% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other processes operative during relapse. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0-16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology. CONCLUSION: Relapsed ovarian cancers harbor more predicted neoantigens than primary tumors, but the increase is due to pre-existing mutational processes, not mutagenesis from chemotherapy.

Cause of death in MS: long-term follow-up of a randomised cohort, 21 years after the start of the pivotal IFNß-1b study.

OBJECTIVES: Compared with controls, multiple sclerosis (MS) patients die, on average, 7-14 years prematurely. Previously, we reported that, 21 years after their participation in the pivotal randomised, controlled trial (RCT) of interferon ß-1b, mortality was reduced by 46-47% in the two groups who received active therapy during the RCT. To determine whether the excessive deaths observed in placebo-treated patients was due to MS-related causes, we analysed the causes-of-death (CODs) in these three, randomised, patient cohorts. DESIGN: Long-term follow-up (LTF) of the pivotal RCT of interferon ß-1b. SETTING: Eleven North American MS-centres participated. PARTICIPANTS: In the original RCT, 372 patients participated, of whom 366 (98.4%) were identified after a median of 21.1 years from RCT enrolment. INTERVENTIONS: Using multiple information sources, we attempted to establish COD and its relationship to MS in deceased patients. PRIMARY OUTCOME: An independent adjudication committee, masked to treatment assignment and using prespecified criteria, determined the likely CODs and their MS relationships. RESULTS: Among the 366 MS patients included in this LTF study, 81 deaths were recorded. Mean age-at-death was 51.7 (±8.7) years. COD, MS relationship, or both were determined for 88% of deaths (71/81). Patients were assigned to one of nine COD categories: cardiovascular disease/stroke; cancer; pulmonary infections; sepsis; accidents; suicide; death due to MS; other known CODs; and unknown COD. Of the 69 patients for whom information on the relationship of death to MS was available, 78.3% (54/69) were adjudicated to be MS related. Patients randomised to receive placebo during the RCT (compared with patients receiving active treatment) experienced an excessive number of MS-related deaths. CONCLUSIONS: In this long-term, randomised, cohort study, MS patients receiving placebo during the RCT experienced greater all-cause mortality compared to those on active treatment. The excessive mortality in the original placebo group was largely from MS-related causes, especially, MS-related pulmonary infections.

Effects of actin-myosin kinetics on the calcium sensitivity of regulated thin filaments.

Activation of thin filaments in striated muscle occurs when tropomyosin exposes myosin binding sites on actin either through calcium-troponin (Ca-Tn) binding or by actin-myosin (A-M) strong binding. However, the extent to which these binding events contributes to thin filament activation remains unclear. Here we propose a simple analytical model in which strong A-M binding and Ca-Tn binding independently activates the rate of A-M weak-to-strong binding. The model predicts how the level of activation varies with pCa as well as A-M attachment, N·k(att), and detachment, k(det), kinetics. To test the model, we use an in vitro motility assay to measure the myosin-based sliding velocities of thin filaments at different pCa, N·k(att), and k(det) values. We observe that the combined effects of varying pCa, N·k(att), and k(det) are accurately fit by the analytical model. The model and supporting data imply that changes in attachment and detachment kinetics predictably affect the calcium sensitivity of striated muscle mechanics, providing a novel A-M kinetic-based interpretation for perturbations (e.g. disease-related mutations) that alter calcium sensitivity.