Analysis of early intermediate states of the nitrogenase reaction by regularization of EPR spectra

Due to the complexity of the catalytic FeMo cofactor site in nitrogenases that mediates the reduction of molecular nitrogen to ammonium, mechanistic details of this reaction remain under debate. In this study, selenium- and sulfur-incorporated FeMo cofactors of the catalytic MoFe protein component from Azotobacter vinelandii are prepared under turnover conditions and investigated by using different EPR methods. Complex signal patterns are observed in the continuous wave EPR spectra of selenium-incorporated samples, which are analyzed by Tikhonov regularization, a method that has not yet been applied to high spin systems of transition metal cofactors, and by an already established grid-of-error approach. Both methods yield similar probability distributions that reveal the presence of at least four other species with different electronic structures in addition to the ground state E0. Two of these species were preliminary assigned to hydrogenated E2 states. In addition, advanced pulsed-EPR experiments are utilized to verify the incorporation of sulfur and selenium into the FeMo cofactor, and to assign hyperfine couplings of 33S and 77Se that directly couple to the FeMo cluster. With this analysis, we report selenium incorporation under turnover conditions as a straightforward approach to stabilize and analyze early intermediate states of the FeMo cofactor.

Engineered compact pan-neuronal promoter from Alphaherpesvirus LAP2 enhances target gene expression in the mouse brain and reduces tropism in the liver

Small promoters capable of driving potent neuron-restricted gene expression are required to support successful brain circuitry and clinical gene therapy studies. However, converting large promoters into functional MiniPromoters, which can be used in vectors with limited capacity, remains challenging. In this study, we describe the generation of a novel version of alphaherpesvirus latency-associated promoter 2 (LAP2), which facilitates precise transgene expression exclusively in the neurons of the mouse brain while minimizing undesired targeting in peripheral tissues. Additionally, we aimed to create a compact neural promoter to facilitate packaging of larger transgenes. Our results revealed that MiniLAP2 (278 bp) drives potent transgene expression in all neurons in the mouse brain, with little to no expression in glial cells. In contrast to the native promoter, MiniLAP2 reduced tropism in the spinal cord and liver. No expression was detected in the kidney or skeletal muscle. In summary, we developed a minimal pan-neuronal promoter that drives specific and robust transgene expression in the mouse brain when delivered intravenously via AAV-PHP.eB vector. The use of this novel MiniPromoter may broaden the range of deliverable therapeutics and improve their safety and efficacy by minimizing the potential for off-target effects.

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

MCL1 inhibition targets Myeloid Derived Suppressors Cells, promotes antitumor immunity and enhances the efficacy of immune checkpoint blockade

Immune checkpoint inhibitors (ICIs) are now the first-line treatment for patients with advanced melanoma. Despite promising clinical results, many patients fail to respond to these therapies. BH3 mimetics, a novel class of small molecule inhibitors that bind and inhibit anti-apoptotic members of the BCL2 family proteins such as BCL2 or MCL1, have been very successful in treating hematologic malignancies. However, there are limited studies on the immunomodulatory role of the BH3 mimetics. Several factors contribute to ICI resistance including myeloid-derived suppressor cells (MDSCs) that exert immunosuppressive effects through direct and indirect inhibition of antitumor immunity. Thus, targeting MDSCs to enhance antitumor immunity has the potential to enhance the efficacy of ICIs. In this study, we show that the MCL1 inhibitor S64315 reduces melanoma tumor growth in an immune cell-dependent manner in mice. Specifically, S64315 enhances antitumor immunity by reducing MDSC frequency and by promoting the activity of CD8+T cells. Additionally, human MDSCs are 10 times more sensitive to S64315 than cutaneous melanoma lines. Further, we found that a higher expression of MCL1 is associated with poor survival for patients treated with anti-PD-1. Finally, combining S64315 and anti-PD-1 significantly slowed tumor growth compared to either agent alone. Together, this proof-of-concept study demonstrates the potential of combining an MCL1 inhibitor with anti-PD-1 in the treatment of melanoma. It justifies the further development of next generation MCL1 inhibitors to improve efficacy of ICIs in treating malignant melanoma.

Human iN neuronal model of schizophrenia displays dysregulation of chromogranin B and related neuropeptide transmitter signatures

Schizophrenia (SZ) is a serious mental illness and neuropsychiatric brain disorder with behavioral symptoms that include hallucinations, delusions, disorganized behavior, and cognitive impairment. Regulation of such behaviors requires utilization of neurotransmitters released to mediate cell-cell communication which are essential to brain functions in health and disease. We hypothesized that SZ may involve dysregulation of neurotransmitters secreted from neurons. To gain an understanding of human SZ, induced neurons (iNs) were derived from SZ patients and healthy control subjects to investigate peptide neurotransmitters, known as neuropeptides, which represent the major class of transmitters. The iNs were subjected to depolarization by high KCl in the culture medium and the secreted neuropeptides were identified and quantitated by nano-LC-MS/MS tandem mass spectrometry. Several neuropeptides were identified from schizophrenia patient-derived neurons, including chromogranin B (CHGB), neurotensin, and natriuretic peptide. Focusing on the main secreted CHGB neuropeptides, results revealed differences in SZ iNs compared to control iN neurons. Lower numbers of distinct CHGB peptides were found in the SZ secretion media compared to controls. Mapping of the peptides to the CHGB precursor revealed peptides unique to either SZ or control, and peptides common to both conditions. Also, the iNs secreted neuropeptides under both KCl and basal (no KCl) conditions. These findings are consistent with reports that chromogranin B levels are reduced in the cerebrospinal fluid and specific brain regions of SZ patients. These findings suggest that iNs derived from SZ patients can model the decreased CHGB neuropeptides observed in human SZ.

Predicting transcriptional outcomes of novel multigene perturbations with GEARS

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.

Bioinspired one-pot furan-thiol-amine multicomponent reaction for making heterocycles and its applications

One-pot multicomponent coupling of different units in a chemoselective manner and their late-stage diversification has wide applicability in varying chemistry fields. Here, we report a simple multicomponent reaction inspired by enzymes that combines thiol and amine nucleophiles in one pot via a furan-based electrophile to generate stable pyrrole heterocycles independent of the diverse functionalities on furans, thiols and amines under physiological conditions. The resulting pyrrole provides a reactive handle to introduce diverse payloads. We demonstrate the application of Furan-Thiol-Amine (FuTine) reaction for the selective and irreversible labeling of peptides, synthesis of macrocyclic and stapled peptides, selective modification of twelve different proteins with varying payloads, homogeneous engineering of proteins, homogeneous stapling of proteins, dual modification of proteins with different fluorophores using the same chemistry and labeling of lysine and cysteine in a complex human proteome.

Imaging immunomodulatory treatment responses in a multiple sclerosis mouse model using hyperpolarized 13C metabolic MRI

BACKGROUND

In recent years, the ability of conventional magnetic resonance imaging (MRI), including T₁ contrast-enhanced (CE) MRI, to monitor high-efficacy therapies and predict long-term disability in multiple sclerosis (MS) has been challenged. Therefore, non-invasive methods to improve MS lesions detection and monitor therapy response are needed.

METHODS

We studied the combined cuprizone and experimental autoimmune encephalomyelitis (CPZ-EAE) mouse model of MS, which presents inflammatory-mediated demyelinated lesions in the central nervous system as commonly seen in MS patients. Using hyperpolarized ¹³C MR spectroscopy (MRS) metabolic imaging, we measured cerebral metabolic fluxes in control, CPZ-EAE and CPZ-EAE mice treated with two clinically-relevant therapies, namely fingolimod and dimethyl fumarate. We also acquired conventional T₁ CE MRI to detect active lesions, and performed ex vivo measurements of enzyme activities and immunofluorescence analyses of brain tissue. Last, we evaluated associations between imaging and ex vivo parameters.

RESULTS

We show that hyperpolarized [1-¹³C]pyruvate conversion to lactate is increased in the brain of untreated CPZ-EAE mice when compared to the control, reflecting immune cell activation. We further demonstrate that this metabolic conversion is significantly decreased in response to the two treatments. This reduction can be explained by increased pyruvate dehydrogenase activity and a decrease in immune cells. Importantly, we show that hyperpolarized ¹³C MRS detects dimethyl fumarate therapy, whereas conventional T₁ CE MRI cannot.

CONCLUSIONS

In conclusion, hyperpolarized MRS metabolic imaging of [1-¹³C]pyruvate detects immunological responses to disease-modifying therapies in MS. This technique is complementary to conventional MRI and provides unique information on neuroinflammation and its modulation.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Membrane-induced tau amyloid fibrils

The intrinsically disordered protein tau aggregates into β-sheet amyloid fibrils that spread in human brains afflicted with Alzheimer's disease and other neurodegenerative diseases. Tau interaction with lipid membranes might play a role in the formation and spreading of these pathological aggregates. Here we investigate the conformation and assembly of membrane-induced tau aggregates using solid-state NMR and transmission electron microscopy. A tau construct that encompasses the microtubule-binding repeats and a proline-rich domain is reconstituted into cholesterol-containing phospholipid membranes. 2D ¹³C-¹³C correlation spectra indicate that tau converted from a random coil to a β-sheet conformation over weeks. Small unilamellar vesicles (SUVs) cause different equilibrium conformations from large unilamellar vesicles (LUVs) and multilamellar vesicles (MLVs). Importantly, SUV-bound tau developed long fibrils that exhibit the characteristic β-sheet chemical shifts of Tyr310 in heparin-fibrillized tau. In comparison, LUVs and MLVs do not induce fibrils but cause different β-sheet aggregates. Lipid-protein correlation spectra indicate that these tau aggregates reside at the membrane-water interface, without inserting into the middle of the lipid bilayer. Removal of cholesterol from the SUVs abolished the fibrils, indicating that both membrane curvature and cholesterol are required for tau fibril formation. These results have implications for how lipid membranes might nucleate tau aggregates.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

Experimental strategies to improve drug-target identification in mass spectrometry-based thermal stability assays

Mass spectrometry (MS)-based thermal stability assays have recently emerged as one of the most promising solutions for the identification of protein-ligand interactions. Here, we have investigated eight combinations of several recently introduced MS-based advancements, including the Phase-Constrained Spectral Deconvolution Method, Field Asymmetric Ion Mobility Spectrometry, and the implementation of a carrier sample as improved MS-based acquisition approaches for thermal stability assays (iMAATSA). We used intact Jurkat cells treated with a commercially available MEK inhibitor, followed by heat treatment, to prepare a set of unfractionated isobarically-labeled proof-of-concept samples to compare the performance of eight different iMAATSAs. Finally, the best-performing iMAATSA was compared to a conventional approach and evaluated in a fractionation experiment. Improvements of up to 82% and 86% were demonstrated in protein identifications and high-quality melting curves, respectively, over the conventional approach in the proof-of-concept study, while an approximately 12% improvement in melting curve comparisons was achieved in the fractionation experiment.