From structure to clinic: Design of a muscarinic M1 receptor agonist with potential to treatment of Alzheimer's disease.

Current therapies for Alzheimer's disease seek to correct for defective cholinergic transmission by preventing the breakdown of acetylcholine through inhibition of acetylcholinesterase, these however have limited clinical efficacy. An alternative approach is to directly activate cholinergic receptors responsible for learning and memory. The M1-muscarinic acetylcholine (M1) receptor is the target of choice but has been hampered by adverse effects. Here we aimed to design the drug properties needed for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissue-based assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans. Through this approach, we rationally designed the optimal properties, including selectivity and partial agonism, into HTL9936-a potential candidate for the treatment of memory loss in Alzheimer's disease. More broadly, this demonstrates a strategy for targeting difficult GPCR targets from structure to clinic.

Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia.

N6-methyladenosine (m6A) is an abundant internal RNA modification1,2 that is catalysed predominantly by the METTL3-METTL14 methyltransferase complex3,4. The m6A methyltransferase METTL3 has been linked to the initiation and maintenance of acute myeloid leukaemia (AML), but the potential of therapeutic applications targeting this enzyme remains unknown5-7. Here we present the identification and characterization of STM2457, a highly potent and selective first-in-class catalytic inhibitor of METTL3, and a crystal structure of STM2457 in complex with METTL3-METTL14. Treatment of tumours with STM2457 leads to reduced AML growth and an increase in differentiation and apoptosis. These cellular effects are accompanied by selective reduction of m6A levels on known leukaemogenic mRNAs and a decrease in their expression consistent with a translational defect. We demonstrate that pharmacological inhibition of METTL3 in vivo leads to impaired engraftment and prolonged survival in various mouse models of AML, specifically targeting key stem cell subpopulations of AML. Collectively, these results reveal the inhibition of METTL3 as a potential therapeutic strategy against AML, and provide proof of concept that the targeting of RNA-modifying enzymes represents a promising avenue for anticancer therapy.

Characterisation and long-term follow-up of children with Brugada syndrome: experience from a tertiary paediatric referral centre.

AIMS: Brugada syndrome is an inherited condition, which typically presents in young adults. It can also be diagnosed in children, but data in this group remain scarce. This study aims to describe the clinical features, management, and follow-up of children with personal or family history of Brugada syndrome. METHODS: Retrospective study of consecutive patients with Brugada history followed up in a tertiary paediatric referral centre between 2009 and 2021. Patients were assessed according to the phenotype: positive (with variable genotype) or negative (with positive genotype). RESULTS: Thirty patients were included (mean age at diagnosis 7 ± 6 years, 53% male). Within the positive phenotype (n = 16), 81% were male, and 88% had spontaneous type 1 ECG pattern. A genetic test was performed in 88% and was positive in 57%. Fourteen patients had a negative phenotype-positive genotype, 79% female, all diagnosed during family screening; 43% mentioned family history of sudden cardiac death. Although most of the patients were asymptomatic, the prevalence of rhythm/conduction disturbances was not negligible, particularly if a positive phenotype. No clinically significant events were reported in the negative phenotype patients. Three patients were hospitalised due to an arrhythmic cause, all in patients with a positive phenotype. CONCLUSION: In our study, the documentation of rhythm and conduction disturbances was not infrequent, especially in patients with a positive phenotype. Despite the significant family history, phenotype negative patients had no relevant events during follow-up. Nevertheless, the management of these patients is not clear cut, and a personalised therapeutic strategy with close follow-up is essential.

Analysis of T cell receptor clonotypes in tumor microenvironment identifies shared cancer-type-specific signatures.

Despite the conventional view that a truly random V(D)J recombination process should generate a highly diverse immune repertoire, emerging reports suggest that there is a certain bias toward the generation of shared/public immune receptor chains. These studies were performed in viral diseases where public T cell receptors (TCR) appear to confer better protective responses. Selective pressures generating common TCR clonotypes are currently not well understood, but it is believed that they confer a growth advantage. As very little is known about public TCR clonotypes in cancer, here we set out to determine the extent of shared TCR clonotypes in the intra-tumor microenvironments of virus- and non-virus-driven head and neck cancers using TCR sequencing. We report that tumor-infiltrating T cell clonotypes were indeed shared across individuals with the same cancer type, where the majority of shared sequences were specific to the cancer type (i.e., viral versus non-viral). These shared clonotypes were not particularly enriched in EBV-associated nasopharynx cancer but, in both cancers, exhibited distinct characteristics, namely shorter CDR3 lengths, restricted V- and J-gene usages, and also demonstrated convergent V(D)J recombination. Many of these shared TCRs were expressed in patients with a shared HLA background. Pattern recognition of CDR3 amino acid sequences revealed strong convergence to specific pattern motifs, and these motifs were uniquely found to each cancer type. This suggests that they may be enriched for specificity to common antigens found in the tumor microenvironment of different cancers. The identification of shared TCRs in infiltrating tumor T cells not only adds to our understanding of the tumor-adaptive immune recognition but could also serve as disease-specific biomarkers and guide the development of future immunotherapies.

Applications of Cryo-EM in small molecule and biologics drug design.

Electron cryo-microscopy (cryo-EM) is a powerful technique for the structural characterization of biological macromolecules, enabling high-resolution analysis of targets once inaccessible to structural interrogation. In recent years, pharmaceutical companies have begun to utilize cryo-EM for structure-based drug design. Structural analysis of integral membrane proteins, which comprise a large proportion of druggable targets and pose particular challenges for X-ray crystallography, by cryo-EM has enabled insights into important drug target families such as G protein-coupled receptors (GPCRs), ion channels, and solute carrier (SLCs) proteins. Structural characterization of biologics, such as vaccines, viral vectors, and gene therapy agents, has also become significantly more tractable. As a result, cryo-EM has begun to make major impacts in bringing critical therapeutics to market. In this review, we discuss recent instructive examples of impacts from cryo-EM in therapeutics design, focusing largely on its implementation at Pfizer. We also discuss the opportunities afforded by emerging technological advances in cryo-EM, and the prospects for future development of the technique.

A knottin scaffold directs the CXC-chemokine-binding specificity of tick evasins.

Tick evasins (EVAs) bind either CC- or CXC-chemokines by a poorly understood promiscuous or "one-to-many" mechanism to neutralize inflammation. Because EVAs potently inhibit inflammation in many preclinical models, highlighting their potential as biological therapeutics for inflammatory diseases, we sought to further unravel the CXC-chemokine-EVA interactions. Using yeast surface display, we identified and characterized 27 novel CXC-chemokine-binding evasins homologous to EVA3 and defined two functional classes. The first, which included EVA3, exclusively bound ELR+ CXC-chemokines, whereas the second class bound both ELR+ and ELR- CXC-chemokines, in several cases including CXC-motif chemokine ligand 10 (CXCL10) but, surprisingly, not CXCL8. The X-ray crystal structure of EVA3 at a resolution of 1.79 Å revealed a single antiparallel ß-sheet with six conserved cysteine residues forming a disulfide-bonded knottin scaffold that creates a contiguous solvent-accessible surface. Swapping analyses identified distinct knottin scaffold segments necessary for different CXC-chemokine-binding activities, implying that differential ligand positioning, at least in part, plays a role in promiscuous binding. Swapping segments also transferred chemokine-binding activity, resulting in a hybrid EVA with dual CXCL10- and CXCL8-binding activities. The solvent-accessible surfaces of the knottin scaffold segments have distinctive shape and charge, which we suggest drives chemokine-binding specificity. These studies provide structural and mechanistic insight into how CXC-chemokine-binding tick EVAs achieve class specificity but also engage in promiscuous binding.

Glutaminolysis is a metabolic dependency in FLT3ITD acute myeloid leukemia unmasked by FLT3 tyrosine kinase inhibition.

FLT3 internal tandem duplication (FLT3ITD) mutations are common in acute myeloid leukemia (AML) associated with poor patient prognosis. Although new-generation FLT3 tyrosine kinase inhibitors (TKI) have shown promising results, the outcome of FLT3ITD AML patients remains poor and demands the identification of novel, specific, and validated therapeutic targets for this highly aggressive AML subtype. Utilizing an unbiased genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screen, we identify GLS, the first enzyme in glutamine metabolism, as synthetically lethal with FLT3-TKI treatment. Using complementary metabolomic and gene-expression analysis, we demonstrate that glutamine metabolism, through its ability to support both mitochondrial function and cellular redox metabolism, becomes a metabolic dependency of FLT3ITD AML, specifically unmasked by FLT3-TKI treatment. We extend these findings to AML subtypes driven by other tyrosine kinase (TK) activating mutations and validate the role of GLS as a clinically actionable therapeutic target in both primary AML and in vivo models. Our work highlights the role of metabolic adaptations as a resistance mechanism to several TKI and suggests glutaminolysis as a therapeutically targetable vulnerability when combined with specific TKI in FLT3ITD and other TK activating mutation-driven leukemias.

Refinement of two-dimensional electrophoresis for vitreous proteome profiling using an artificial neural network.

Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices. Graphical abstract.

Development and validation of a comprehensive genomic diagnostic tool for myeloid malignancies.

The diagnosis of hematologic malignancies relies on multidisciplinary workflows involving morphology, flow cytometry, cytogenetic, and molecular genetic analyses. Advances in cancer genomics have identified numerous recurrent mutations with clear prognostic and/or therapeutic significance to different cancers. In myeloid malignancies, there is a clinical imperative to test for such mutations in mainstream diagnosis; however, progress toward this has been slow and piecemeal. Here we describe Karyogene, an integrated targeted resequencing/analytical platform that detects nucleotide substitutions, insertions/deletions, chromosomal translocations, copy number abnormalities, and zygosity changes in a single assay. We validate the approach against 62 acute myeloid leukemia, 50 myelodysplastic syndrome, and 40 blood DNA samples from individuals without evidence of clonal blood disorders. We demonstrate robust detection of sequence changes in 49 genes, including difficult-to-detect mutations such as FLT3 internal-tandem and mixed-lineage leukemia (MLL) partial-tandem duplications, and clinically significant chromosomal rearrangements including MLL translocations to known and unknown partners, identifying the novel fusion gene MLL-DIAPH2 in the process. Additionally, we identify most significant chromosomal gains and losses, and several copy neutral loss-of-heterozygosity mutations at a genome-wide level, including previously unreported changes such as homozygosity for DNMT3A R882 mutations. Karyogene represents a dependable genomic diagnosis platform for translational research and for the clinical management of myeloid malignancies, which can be readily adapted for use in other cancers.

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology.

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).

Erratum: CamOptimus: a tool for exploiting complex adaptive evolution to optimise experiments and processes in biotechnology.

[This corrects the article DOI: 10.1099/mic.0.000477.].

From G Protein-coupled Receptor Structure Resolution to Rational Drug Design.

A number of recent technical solutions have led to significant advances in G protein-coupled receptor (GPCR) structural biology. Apart from a detailed mechanistic view of receptor activation, the new structures have revealed novel ligand binding sites. Together, these insights provide avenues for rational drug design to modulate the activities of these important drug targets. The application of structural data to GPCR drug discovery ushers in an exciting era with the potential to improve existing drugs and discover new ones. In this review, we focus on technical solutions that have accelerated GPCR crystallography as well as some of the salient findings from structures that are relevant to drug discovery. Finally, we outline some of the approaches used in GPCR structure based drug design.

An artificial neural network for membrane-bound catechol-O-methyltransferase biosynthesis with Pichia pastoris methanol-induced cultures.

BACKGROUND: Membrane proteins are important drug targets in many human diseases and gathering structural information regarding these proteins encourages the pharmaceutical industry to develop new molecules using structure-based drug design studies. Specifically, membrane-bound catechol-O-methyltransferase (MBCOMT) is an integral membrane protein that catalyzes the methylation of catechol substrates and has been linked to several diseases such as Parkinson's disease and Schizophrenia. Thereby, improvements in the clinical outcome of the therapy to these diseases may come from structure-based drug design where reaching MBCOMT samples in milligram quantities are crucial for acquiring structural information regarding this target protein. Therefore, the main aim of this work was to optimize the temperature, dimethylsulfoxide (DMSO) concentration and the methanol flow-rate for the biosynthesis of recombinant MBCOMT by Pichia pastoris bioreactor methanol-induced cultures using artificial neural networks (ANN). RESULTS: The optimization trials intended to evaluate MBCOMT expression by P. pastoris bioreactor cultures led to the development of a first standard strategy for MBCOMT bioreactor biosynthesis with a batch growth on glycerol until the dissolved oxygen spike, 3 h of glycerol feeding and 12 h of methanol induction. The ANN modeling of the aforementioned fermentation parameters predicted a maximum MBCOMT specific activity of 384.8 nmol/h/mg of protein at 30°C, 2.9 mL/L/H methanol constant flow-rate and with the addition of 6% (v/v) DMSO with almost 90% of healthy cells at the end of the induction phase. These results allowed an improvement of MBCOMT specific activity of 6.4-fold in comparison to that from the small-scale biosynthesis in baffled shake-flasks. CONCLUSIONS: The ANN model was able to describe the effects of temperature, DMSO concentration and methanol flow-rate on MBCOMT specific activity, as shown by the good fitness between predicted and observed values. This experimental procedure highlights the potential role of chemical chaperones such as DMSO in improving yields of recombinant membrane proteins with a different topology than G-coupled receptors. Finally, the proposed ANN shows that the manipulation of classic fermentation parameters coupled with the addition of specific molecules can open and reinforce new perspectives in the optimization of P. pastoris bioprocesses for membrane proteins biosynthesis.

Structure-based drug design for G protein-coupled receptors.

Our understanding of the structural biology of G protein-coupled receptors has undergone a transformation over the past 5 years. New protein-ligand complexes are described almost monthly in high profile journals. Appreciation of how small molecules and natural ligands bind to their receptors has the potential to impact enormously how medicinal chemists approach this major class of receptor targets. An outline of the key topics in this field and some recent examples of structure- and fragment-based drug design are described. A table is presented with example views of each G protein-coupled receptor for which there is a published X-ray structure, including interactions with small molecule antagonists, partial and full agonists. The possible implications of these new data for drug design are discussed.

Coastline changes and sedimentation related with the opening of an artificial channel: the Valo Grande Delta, SE Brazil.

The role played by human activity in coastline changes indicates a general tendency of retreating coasts, especially deltaic environments, as a result of the recent trend of sea level rise as well as the blockage of the transfer of sediments towards the coast, especially due to the construction of dams. This is particularly important in deltaic environments which have been suffering a dramatic loss of area in the last decades. In contrast, in this paper, we report the origin and evolution of an anthropogenic delta, the Valo Grande delta, on the south-eastern Brazilian coast, whose origin is related to the opening of an artificial channel and the diversion of the main flow of the Ribeira de Iguape River. The methodology included the analysis of coastline changes, bathymetry and coring, which were used to determine the sedimentation rates and grain-size changes over time. The results allowed us to recognize the different facies of the anthropogenic delta and establish its lateral and vertical depositional trends. Despite not being very frequent, anthropogenic deltas represent a favorable environment for the record of natural and anthropogenic changes in historical times and, thus, deserve more attention from researchers of different subjects.

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network.

Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and high-throughput alternative for protein extraction and digestion. Here, a refined paramagnetic bead-based digestion protocol is described for Opentrons® OT-2 platform (OT-2) as a versatile, reproducible, and affordable alternative for the automatic sample preparation for MS analysis. For this purpose, an artificial neural network (ANN) was applied to maximize the number of peptides without missed cleavages identified in HeLa extract by combining factors such as the quantity (µg) of trypsin/Lys-C and beads (MagReSyn® Amine), % (w/v) SDS, % (v/v) acetonitrile, and time of digestion (h). ANN model predicted the optimal conditions for the digestion of 50 µg of HeLa extract, pointing to the use of 2.5% (w/v) SDS and 300 µg of beads for sample preparation and long-term digestion (16h) with 0.15 µg Lys-C and 2.5 µg trypsin (≈1:17 ratio). Based on the results of the ANN model, the manual protocol was automated in OT-2. The performance of the automatic protocol was evaluated with different sample types, including human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, and mouse tissue cortex, showing great reproducibility and low sample-to-sample variability in all cases. In addition, we tested the performance of this method in the preparation of a challenging biological fluid such as rat bile, a proximal fluid that is rich in bile salts, bilirubin, cholesterol, and fatty acids, among other MS interferents. Compared to other protocols described in the literature for the extraction and digestion of bile proteins, the method described here allowed identify 385 unique proteins, thus contributing to improving the coverage of the bile proteome.

Insights from multi-omic modeling of neurodegeneration in xeroderma pigmentosum using an induced pluripotent stem cell system.

Xeroderma pigmentosum (XP) is caused by defective nucleotide excision repair of DNA damage. This results in hypersensitivity to ultraviolet light and increased skin cancer risk, as sunlight-induced photoproducts remain unrepaired. However, many XP patients also display early-onset neurodegeneration, which leads to premature death. The mechanism of neurodegeneration is unknown. Here, we investigate XP neurodegeneration using pluripotent stem cells derived from XP patients and healthy relatives, performing functional multi-omics on samples during neuronal differentiation. We show substantially increased levels of 5',8-cyclopurine and 8-oxopurine in XP neuronal DNA secondary to marked oxidative stress. Furthermore, we find that the endoplasmic reticulum stress response is upregulated and reversal of the mutant genotype is associated with phenotypic rescue. Critically, XP neurons exhibit inappropriate downregulation of the protein clearance ubiquitin-proteasome system (UPS). Chemical enhancement of UPS activity in XP neuronal models improves phenotypes, albeit inadequately. Although more work is required, this study presents insights with intervention potential.

Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris.

BACKGROUND: Novel analytical tools, which shorten the long and costly development cycles of biopharmaceuticals are essential. Metabolic flux analysis (MFA) shows great promise in improving our understanding of the metabolism of cell factories in bioreactors, but currently only provides information post-process using conventional off-line methods. MFA combined with real time multianalyte process monitoring techniques provides a valuable platform technology allowing real time insights into metabolic responses of cell factories in bioreactors. This could have a major impact in the bioprocessing industry, ultimately improving product consistency, productivity and shortening development cycles. RESULTS: This is the first investigation using Near Infrared Spectroscopy (NIRS) in situ combined with metabolic flux modelling which is both a significant challenge and considerable extension of these techniques. We investigated the feasibility of our approach using the industrial workhorse Pichia pastoris in a simplified model system. A parental P. pastoris strain (i.e. which does not synthesize recombinant protein) was used to allow definition of distinct metabolic states focusing solely upon the prediction of intracellular fluxes in central carbon metabolism. Extracellular fluxes were determined using off-line conventional reference methods and on-line NIR predictions (calculated by multivariate analysis using the partial least squares algorithm, PLS). The results showed that the PLS-NIRS models for biomass and glycerol were accurate: correlation coefficients, R2, above 0.90 and the root mean square error of prediction, RMSEP, of 1.17 and 2.90 g/L, respectively. The analytical quality of the NIR models was demonstrated by direct comparison with the standard error of the laboratory (SEL), which showed that performance of the NIR models was suitable for quantifying biomass and glycerol for calculating extracellular metabolite rates and used as independent inputs for the MFA (RMSEP lower than 1.5 × SEL). Furthermore, the results for the MFA from both datasets passed consistency tests performed for each steady state, showing that the precision of on-line NIRS is equivalent to that obtained by the off-line measurements. CONCLUSIONS: The findings of this study show for the first time the potential of NIRS as an input generating for MFA models, contributing to the optimization of cell factory metabolism in real-time.

Modeling NOM removal by softening in a surface water treatment plant.

Phenomenological models and hybrid phenomenological-chemometric models were developed to predict natural organic matter (NOM) removal based on the real water treatment data from the city of Minneapolis over a 3 year period. The analysis of the modeling results showed that the phenomenological model was able to capture the major variations of NOM removal but it tended to over predict the NOM removal in independent data sets. These results could be significantly improved by the hybrid model, which was less biased and much more accurate than the phenomenological model. The phenomenological model parameters showed low statistical confidence because the available data, collected in real water treatment conditions, was not sufficiently informative to identify the complex model structure. By comparison, the hybrid modeling method enabled a more reliable discrimination of the most important factors affecting NOM removal. The final hybrid model was implemented in an Excel spreadsheet and can be easily used for NOM removal prediction and the control of chemical dosing.