Measurable Residual Disease Monitoring in AML With FLT3-ITD Treated With Intensive Chemotherapy Plus Midostaurin.

Measurable residual disease (MRD) monitoring in acute myeloid leukemia (AML) with FLT3 internal tandem duplication (FLT3-ITDpos) was hampered by the broad heterogeneity of ITD mutations. Using our recently developed FLT3-ITD paired-end next-generation sequencing (NGS)-based MRD assay with a limit of detection of 10-4 to 10-5, we evaluated the prognostic impact of MRD at different time-points in 157 FLT3-ITDpos AML patients enrolled in the AMLSG16-10 trial (NCT01477606) combining intensive chemotherapy with midostaurin followed by midostaurin maintenance. Achievement of MRD negativity (MRDneg) after two cycles of chemotherapy (Cy2) observed in 111/142 (78%) patients predicted for superior 4-year rates of cumulative incidence of relapse (CIR) (4y-CIR, 26% vs 46%; P=.001) and overall survival (OS) (4y-OS, 70% vs 44%; P=.012). This survival advantage was also seen for patients undergoing allogeneic hematopoietic-cell transplantation in first complete remission (4y-CIR, 14% vs 39%; P=.001; 4y-OS, 71% vs 49%; P=.029). Multivariate models for CIR and OS after Cy2 revealed FLT3-ITD MRDneg as the only consistent favorable variable for CIR (HR, 0.29; P=.006) and OS (HR, 0.39; P=.018). NPM1 co-mutation correlated with deeper molecular response as reflected by stronger MRD reduction and higher rate of FLT3-ITD MRDneg after Cy2. During follow-up, conversion from MRDneg to MRDpos was a strong, independent factor for inferior CIR (HR, 16.64; P<.001) and OS (HR, 4.05; P<.001). NGS-based FLT3-ITD MRD monitoring allows for the identification of patients at high risk of relapse and death following intensive chemotherapy plus midostaurin. Using NGS-based technology, FLT3-ITD emerges as a novel, clinically highly relevant target for MRD monitoring.

Emergence of community behaviors in the gut microbiota upon drug treatment.

Pharmaceuticals can directly inhibit the growth of gut bacteria, but the degree to which such interactions manifest in complex community settings is an open question. Here, we compared the effects of 30 drugs on a 32-species synthetic community with their effects on each community member in isolation. While most individual drug-species interactions remained the same in the community context, communal behaviors emerged in 26% of all tested cases. Cross-protection during which drug-sensitive species were protected in community was 6 times more frequent than cross-sensitization, the converse phenomenon. Cross-protection decreased and cross-sensitization increased at higher drug concentrations, suggesting that the resilience of microbial communities can collapse when perturbations get stronger. By metabolically profiling drug-treated communities, we showed that both drug biotransformation and bioaccumulation contribute mechanistically to communal protection. As a proof of principle, we molecularly dissected a prominent case: species expressing specific nitroreductases degraded niclosamide, thereby protecting both themselves and sensitive community members.

A catalog of small proteins from the global microbiome.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

The global macroeconomic burden of Alzheimer's disease and other dementias: estimates and projections for 152 countries or territories.

BACKGROUND: Alzheimer's disease and other dementias (ADODs) severely threaten the wellbeing of older people, their families, and communities, especially with projected exponential growth. Understanding the macroeconomic implications of ADODs for policy making is essential but under-researched. METHODS: We used a health-augmented macroeconomic model to calculate the macroeconomic burden of ADODs for 152 countries or territories, accounting for: the effect on labour supply of reduced working hours of informal caregivers; the effect on labour supply of ADODs-related mortality and morbidity; age-sex-specific differences in education, work experience, labour market participations, and informal caregivers; and treatment and formal care costs diverting from savings and investments. FINDINGS: ADODs will cost the world economy 14 513 billion international dollars (INT$, measured in the base year 2020; 95% uncertainty interval [UI] 12 106-17 778) from 2020 to 2050, equivalent to 0·421% (95% UI 0·351-0·515) of annual global GDP. Japan incurs the largest annual GDP loss at 1·463% (1·225-1·790). China (INT$2961 billion [2507-3564]), the USA (INT$2331 billion [1989-2829]), and Japan (INT$1758 billion [1471-2150]) face the largest absolute economic burdens. The economic burden of informal care ranges from 60·97% in high-income countries to 85·45% in lower-middle-income countries, and treatment and formal care costs range from 10·50% in lower-middle-income countries to 30·80% in high-income countries. INTERPRETATION: The macroeconomic burden of ADODs is substantial and unequally distributed across countries and regions. Global efforts to reduce the burden, especially with regard to informal care, are urgently needed. FUNDING: National Institute on Aging, National Institutes of Health; Chinese Academy of Engineering; Chinese Academy of Medical Sciences; Bill & Melinda Gates Foundation; Davos Alzheimer's Collaborative through Data for Decisions.

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Role of albumin in the metabolism and excretion of ochratoxin A.

Ochratoxin A (OTA) is known to be strongly bound to serum albumin, but it remains unknown how albumin affects its metabolism and kinetics. To close this gap, we used a mouse model, where heterozygous albumin deletion reduces serum albumin to concentrations similar to hypoalbuminemic patients and completely eliminates albumin by a homozygous knockout. OTA and its potential metabolites (OTα, 4-OH-OTA, 7'-OH-OTA, OTHQ, OP-OTA, OTB-GSH, OTB-NAC, OTB) were time-dependently analyzed in plasma, bile, and urine by LC-MS/MS and were compared to previously published hepatotoxicity and nephrotoxicity data. Homozygous albumin deletion strongly accelerated plasma clearance as well as biliary and urinary excretion of the parent compound and its hydroxylation products. Decreasing albumin in mice by the heterozygous and even more by the homozygous knockout leads to an increase in the parent compound in urine which corresponded to increased nephrotoxicity. The role of albumin in OTA-induced hepatotoxicity is more complex, since heterozygous but not homozygous nor wild-type mice showed a strong biliary increase in the toxic open lactone OP-OTA. Correspondingly, OTA-induced hepatotoxicity was higher in heterozygous than in wild-type and homozygous animals. We present evidence that albumin-mediated retention of OTA in hepatocytes is required for formation of the toxic OP-OTA, while complete albumin elimination leads to rapid biliary clearance of OTA from hepatocytes with less formation of OP-OTA. In conclusion, albumin has a strong influence on metabolism and toxicity of OTA. In hypoalbuminemia, the parent OTA is associated with increased nephrotoxicity and the open lactone with increased hepatotoxicity.

Chelate Complexes of 3d Transition Metal Ions─A Challenge for Electronic-Structure Methods?

Different electronic-structure methods were assessed for their ability to predict two important properties of the industrially relevant chelating agent nitrilotriacetic acid (NTA): its selectivity with respect to six different first-row transition metal ions and the spin-state energetics of its complex with Fe(III). The investigated methods encompassed density functional theory (DFT), the random phase approximation (RPA), coupled cluster (CC) theory, and the auxiliary-field quantum Monte Carlo (AFQMC) method, as well as the complete active space self-consistent field (CASSCF) method and the respective on-top methods: second-order N-electron valence state perturbation theory (NEVPT2) and multiconfiguration pair-density functional theory (MC-PDFT). Different strategies for selecting active spaces were explored, and the density matrix renormalization group (DMRG) approach was used to solve the largest active spaces. Despite somewhat ambiguous multi-reference diagnostics, most methods gave relatively good agreement with experimental data for the chemical reactions connected to the selectivity, which only involved transition-metal complexes in their high-spin state. CC methods yielded the highest accuracy followed by range-separated DFT and AFQMC. We discussed in detail that even higher accuracies can be obtained with NEVPT2, under the prerequisite that consistent active spaces along the entire chemical reaction can be selected, which was not the case for reactions involving Fe(III). A bigger challenge for electronic-structure methods was the prediction of the spin-state energetics, which additionally involved lower spin states that exhibited larger multi-reference diagnostics. Conceptually different, typically accurate methods ranging from CC theory via DMRG-NEVPT2 in combination with large active spaces to AFQMC agreed well that the high-spin state is energetically significantly favored over the other spin states. This was in contrast to most DFT functionals and RPA which yielded a smaller stabilization and some common DFT functionals and MC-PDFT even predicting the low-spin state to be energetically most favorable.

The Menin story in acute myeloid leukaemia-The road to success.

The treatment of acute myeloid leukaemia (AML) has changed fundamentally in the last decade with many new targeted therapies entering clinics. Some of the most interesting agents under development are Menin inhibitors which interfere with the interaction of Menin with wild-type (wt) KMT2A or a KMT2A-fusion protein and thereby downregulate the leukaemic gene expression (MEIS1, PBX3, HOX) in NPM1 mutant or KMT2A-rearranged leukaemia. Other HOX and MEIS1 expressing leukaemias may also be sensitive to Menin inhibition. Following the encouraging results as monotherapy in refractory and relapsed AML, the combination of Menin inhibitors with chemotherapeutic agents and other targeted drugs is being investigated clinically.

Image-based quantification of mitochondrial iron uptake via Mitoferrin-2.

Iron is a trace element that is critical for most living organisms and plays a key role in a wide variety of metabolic processes. In the mitochondrion, iron is involved in producing iron-sulfur clusters and synthesis of heme and kept within physiological ranges by concerted activity of multiple molecules. Mitochondrial iron uptake is mediated by the solute carrier transporters Mitoferrin-1 (SLC25A37) and Mitoferrin-2 (SLC25A28). While Mitoferrin-1 is mainly involved in erythropoiesis, the cellular function of the ubiquitously expressed Mitoferrin-2 remains less well defined. Furthermore, Mitoferrin-2 is associated with several human diseases, including cancer, cardiovascular and metabolic diseases, hence representing a potential therapeutic target. Here, we developed a robust approach to quantify mitochondrial iron uptake mediated by Mitoferrin-2 in living cells. We utilize HEK293 cells with inducible expression of Mitoferrin-2 and measure iron-induced quenching of rhodamine B[(1,10-phenanthroline-5-yl)-aminocarbonyl]benzyl ester (RPA) fluorescence and validate this assay for medium-throughput screening. This assay may allow identification and characterization of Mitoferrin-2 modulators and could enable drug discovery for this target.

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases.

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Terlipressin therapy is associated with increased risk of colonisation with multidrug-resistant bacteria in patients with decompensated cirrhosis.

BACKGROUND: Patients with cirrhosis are susceptible to develop bacterial infections that trigger acute decompensation (AD) and acute-on-chronic liver failure (ACLF). Infections with multidrug-resistant organisms (MDRO) are associated with deleterious outcome. MDRO colonisation frequently proceeds MDRO infections and antibiotic therapy has been associated with MDRO colonisation. AIM: The aim of the study was to assess the influence of non-antibiotic medication contributing to MDRO colonisation. METHODS: Three hundred twenty-four patients with AD and ACLF admitted to the ICU of Frankfurt University Hospital with MDRO screening were included. Regression models were performed to identify drugs associated with MDRO colonisation. Another cohort (n = 129) from Barcelona was included to validate. A third multi-centre cohort (n = 203) with metagenomic sequencing data of stool was included to detect antibiotic resistance genes. RESULTS: A total of 97 patients (30%) were identified to have MDRO colonisation and 35 of them (11%) developed MDRO infection. Patients with MDRO colonisation had significantly higher risk of MDRO infection than those without (p = 0.0098). Apart from antibiotic therapy (odds ratio (OR) 2.91, 95%-confidence interval (CI) 1.82-4.93, p < 0.0001), terlipressin therapy in the previous 14 days was the only independent covariate associated with MDRO colonisation in both cohorts, the overall (OR 9.47, 95%-CI 2.96-30.23, p < 0.0001) and after propensity score matching (OR 5.30, 95%-CI 1.22-23.03, p = 0.011). In the second cohort, prior terlipressin therapy was a risk factor for MDRO colonisation (OR 2.49, 95% CI 0.911-6.823, p = 0.075) and associated with risk of MDRO infection during follow-up (p = 0.017). The validation cohort demonstrated that antibiotic inactivation genes were significantly associated with terlipressin administration (p = 0.001). CONCLUSIONS: Our study reports an increased risk of MDRO colonisation in patients with AD or ACLF, who recently received terlipressin therapy, while other commonly prescribed non-antibiotic co-medications had negligible influence. Future prospective trials are needed to confirm these results.

High-throughput anaerobic screening for identifying compounds acting against gut bacteria in monocultures or communities.

The human gut microbiome is a key contributor to health, and its perturbations are linked to many diseases. Small-molecule xenobiotics such as drugs, chemical pollutants and food additives can alter the microbiota composition and are now recognized as one of the main factors underlying microbiome diversity. Mapping the effects of such compounds on the gut microbiome is challenging because of the complexity of the community, anaerobic growth requirements of individual species and the large number of interactions that need to be quantitatively assessed. High-throughput screening setups offer a promising solution for probing the direct inhibitory effects of hundreds of xenobiotics on tens of anaerobic gut bacteria. When automated, such assays enable the cost-effective investigation of a wide range of compound-microbe combinations. We have developed an experimental setup and protocol that enables testing of up to 5,000 compounds on a target gut species under strict anaerobic conditions within 5 d. In addition, with minor modifications to the protocol, drug effects can be tested on microbial communities either assembled from isolates or obtained from stool samples. Experience in working in an anaerobic chamber, especially in performing delicate work with thick chamber gloves, is required for implementing this protocol. We anticipate that this protocol will accelerate the study of interactions between small molecules and the gut microbiome and provide a deeper understanding of this microbial ecosystem, which is intimately intertwined with human health.

SPIRE: a Searchable, Planetary-scale mIcrobiome REsource.

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

Immunoproteasome function maintains oncogenic gene expression in KMT2A-complex driven leukemia.

Pharmacologic targeting of chromatin-associated protein complexes has shown significant responses in KMT2A-rearranged (KMT2A-r) acute myeloid leukemia (AML) but resistance frequently develops to single agents. This points to a need for therapeutic combinations that target multiple mechanisms. To enhance our understanding of functional dependencies in KMT2A-r AML, we have used a proteomic approach to identify the catalytic immunoproteasome subunit PSMB8 as a specific vulnerability. Genetic and pharmacologic inactivation of PSMB8 results in impaired proliferation of murine and human leukemic cells while normal hematopoietic cells remain unaffected. Disruption of immunoproteasome function drives an increase in transcription factor BASP1 which in turn represses KMT2A-fusion protein target genes. Pharmacologic targeting of PSMB8 improves efficacy of Menin-inhibitors, synergistically reduces leukemia in human xenografts and shows preserved activity against Menin-inhibitor resistance mutations. This identifies and validates a cell-intrinsic mechanism whereby selective disruption of proteostasis results in altered transcription factor abundance and repression of oncogene-specific transcriptional networks. These data demonstrate that the immunoproteasome is a relevant therapeutic target in AML and that targeting the immunoproteasome in combination with Menin-inhibition could be a novel approach for treatment of KMT2A-r AML.

Epigenetic targeting to enhance acute myeloid leukemia-directed immunotherapy.

AML is a malignant disease of hematopoietic progenitor cells with unsatisfactory treatment outcome, especially in patients that are ineligible for intensive chemotherapy. Immunotherapy, comprising checkpoint inhibition, T-cell engaging antibody constructs, and cellular therapies, has dramatically improved the outcome of patients with solid tumors and lymphatic neoplasms. In AML, these approaches have been far less successful. Discussed reasons are the relatively low mutational burden of AML blasts and the difficulty in defining AML-specific antigens not expressed on hematopoietic progenitor cells. On the other hand, epigenetic dysregulation is an essential driver of leukemogenesis, and non-selective hypomethylating agents (HMAs) are the current backbone of non-intensive treatment. The first clinical trials that evaluated whether HMAs may improve immune checkpoint inhibitors' efficacy showed modest efficacy except for the anti-CD47 antibody that was substantially more efficient against AML when combined with azacitidine. Combining bispecific antibodies or cellular treatments with HMAs is subject to ongoing clinical investigation, and efficacy data are awaited shortly. More selective second-generation inhibitors targeting specific chromatin regulators have demonstrated promising preclinical activity against AML and are currently evaluated in clinical trials. These drugs that commonly cause leukemia cell differentiation potentially sensitize AML to immune-based treatments by co-regulating immune checkpoints, providing a pro-inflammatory environment, and inducing (neo)-antigen expression. Combining selective targeted epigenetic drugs with (cellular) immunotherapy is, therefore, a promising approach to avoid unintended effects and augment efficacy. Future studies will provide detailed information on how these compounds influence specific immune functions that may enable translation into clinical assessment.

Computational exploration of the global microbiome for antibiotic discovery.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Antibacterial activity of novel bacterial topoisomerase inhibitors against key veterinary pathogens.

Multidrug-resistant bacteria infect companion animals and livestock in addition to their devastating impact on human health. Novel Bacterial Topoisomerase Inhibitors (NBTIs) with excellent activity against Gram-positive bacteria have previously been identified as promising new antibacterial agents. Herein, we evaluate the antibacterial activity of these NBTIs against a variety of important veterinary pathogens and demonstrate outstanding in vitro activity, especially against staphylococci.

Classical and quantum trial wave functions in auxiliary-field quantum Monte Carlo applied to oxygen allotropes and a CuBr2 model system.

In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results. In the field of material science, we study the electronic structure properties of cuprates through the quasi-1D Fermi-Hubbard model derived from CuBr2, where we find that trial wave functions with both significantly larger fidelities and lower energies over a mean-field solution do not necessarily lead to AFQMC results closer to the exact ground state energy.