Antimicrobial resistance: a concise update.

Antimicrobial resistance (AMR) is a serious threat to global public health, with approximately 5 million deaths associated with bacterial AMR in 2019. Tackling AMR requires a multifaceted and cohesive approach that ranges from increased understanding of mechanisms and drivers at the individual and population levels, AMR surveillance, antimicrobial stewardship, improved infection prevention and control measures, and strengthened global policies and funding to development of novel antimicrobial therapeutic strategies. In this rapidly advancing field, this Review provides a concise update on AMR, encompassing epidemiology, evolution, underlying mechanisms (primarily those related to last-line or newer generation of antibiotics), infection prevention and control measures, access to antibiotics, antimicrobial stewardship, AMR surveillance, and emerging non-antibiotic therapeutic approaches. The Review also discusses the potential roles of artificial intelligence in addressing AMR, including antimicrobial susceptibility testing, AMR surveillance, antimicrobial stewardship, diagnosis, and antimicrobial drug discovery and development. This Review highlights the urgent need for addressing the global effects of AMR and for rapid advancement of relevant technology in this dynamic field.

Mining human microbiomes reveals an untapped source of peptide antibiotics.

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Menin inhibitors in pediatric acute leukemia: a comprehensive review and recommendations to accelerate progress in collaboration with adult leukemia and the international community.

Aberrant expression of HOX and MEIS1 family genes, as seen in KMT2A-rearranged, NUP98-rearranged, or NPM1-mutated leukemias leads to arrested differentiation and leukemia development. HOX family genes are essential gatekeepers of physiologic hematopoiesis, and their expression is regulated by the interaction between KMT2A and menin. Menin inhibitors block this interaction, downregulate the abnormal expression of MEIS1 and other transcription factors and thereby release the differentiation block. Menin inhibitors show significant clinical efficacy against KMT2A-rearranged and NPM1-mutated acute leukemias, with promising potential to address unmet needs in various pediatric leukemia subtypes. In this collaborative initiative, pediatric and adult hematologists/oncologists, and stem cell transplant physicians have united their expertise to explore the potential of menin inhibitors in pediatric leukemia treatment internationally. Our efforts aim to provide a comprehensive clinical overview of menin inhibitors, integrating preclinical evidence and insights from ongoing global clinical trials. Additionally, we propose future international, inclusive, and efficient clinical trial designs, integrating pediatric populations in adult trials, to ensure broad access to this promising therapy for all children and adolescents with menin-dependent leukemias.

Interfacial rheology of lanthanide binding peptide surfactants at the air-water interface.

Peptide surfactants (PEPS) are studied to capture and retain rare earth elements (REEs) at air-water interfaces to enable REE separations. Peptide sequences, designed to selectively bind REEs, depend crucially on the position of ligands within their binding loop domain. These ligands form a coordination sphere that wraps and retains the cation. We study variants of lanthanide binding tags (LBTs) designed to complex strongly with Tb3+. The peptide LBT5- (with net charge -5) is known to bind Tb3+ and adsorb with more REE cations than peptide molecules, suggesting that undesired non-specific coulombic interactions occur. Rheological characterization of interfaces of LBT5- and Tb3+ solutions reveal the formation of an interfacial gel. To probe whether this gelation reflects chelation among intact adsorbed LBT5-:Tb3+ complexes or destruction of the binding loop, we study a variant, LBT3-, designed to form net neutral LBT3-:Tb3+ complexes. Solutions of LBT3- and Tb3+ form purely viscous layers in the presence of excess Tb3+, indicating that each peptide binds a single REE in an intact coordination sphere. We introduce the variant RR-LBT3- with net charge -3 and anionic ligands outside of the coordination sphere. We find that such exposed ligands promote interfacial gelation. Thus, a nuanced requirement for interfacial selectivity of PEPS is proposed: that anionic ligands outside of the coordination sphere must be avoided to prevent the non-selective recruitment of REE cations. This view is supported by simulation, including interfacial molecular dynamics simulations, and interfacial metadynamics simulations of the free energy landscape of the binding loop conformational space.

From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Development of a dry eye index as a new biomarker of dry eye disease.

PURPOSE: To evaluate signs and symptoms in patients diagnosed with dry eye disease (DED), divided into dry eye (DE) groups, in order to find a new biomarker that allows an accurate diagnosis, management and classification of DED. METHODS: This cross-sectional, observational study included 71 DED subjects. Subjective symptoms, visual quality and DE signs were assessed using the Ocular Surface Disease Index (OSDI), the Quality of Vision (QoV) questionnaire, best corrected distance visual acuity (VA), functional visual acuity (FVA), contrast sensitivity (CS), high- and low-order corneal aberrations (HOA and LOA, respectively), tear break-up time (TBUT), Meibomian Gland Dysfunction (MGD), Schirmer test, corneal staining, lid wiper epitheliopathy (LWE) and meibography. Participants were classified into three groups based on dryness severity using a cluster analysis, i.e., mild (N = 17, 55.8 ± 15.4 years), moderate (N = 41, 63.5 ± 10.6 years) and severe (N = 13, 65.0 ± 12.0). A new Dry Eye Severity Index (DESI) based on ocular surface signs has been developed and its association with symptoms, visual quality and signs was assessed. Comparisons between groups were made using Kruskal-Wallis and Chi-squared tests. Spearman correlation analysis was also performed. RESULTS: The DESI was based on three tests for DE signs: TBUT, Schirmer test and MGD. The DESI showed significant differences between different pairs of groups: Mild Dryness versus Moderate Dryness (p < 0.001), Mild Dryness versus Severe Dryness (p < 0.001) and Moderate Dryness versus Severe Dryness (p < 0.001). The DESI was significantly correlated with age (rho = -0.30; p = 0.01), OSDI score (rho = -0.32; p = 0.007), QoV score (rho = -0.35; p = 0.003), VA (rho = -0.34; p = 0.003), FVA (rho = -0.38; p = 0.001) and CS (rho = 0.42; p < 0.001) Also, significant differences between the severity groups were found for OSDI and QoV scores, VA, FVA, CS and MGD (p < 0.05). CONCLUSIONS: The DESI has good performance as a biomarker for the diagnosis, classification and management of DED.

Pandemic Times and Health Care Exclusion: Attitudes Toward Health Care Exclusion of Undocumented Immigrants.

CONTEXT: Most of the 11 million undocumented immigrants living in the United States are excluded from government healthcare programs. Yet, healthcare inequities pose significant dangers to all members of society during a pandemic. This project explores to what extent undocumented immigrants, in the context of a pandemic, can be seen as deserving of access to government healthcare programs. METHODS: The first survey experiment explores whether work ethic can affect perceptions of undocumented immigrants as deserving of government healthcare programs. The second survey experiment tests to what extent appeals to fairness and self-interest, during a pandemic, shape healthcare deservingness attitudes. FINDINGS: The results show that respondents view undocumented immigrants as less deserving of healthcare than citizens, even when undocumented immigrants have a solid work history. The second survey experiment, however, shows that appeals to fairness and self-interest trigger substantial increases in support for undocumented immigrants, both among Republicans and Democrats. CONCLUSIONS: The results suggest that while undocumented immigrants are seen as less deserving of access, appeals to fairness and self-interest can trigger increased support.

Venetoclax for pediatric patients with newly diagnosed acute myeloid leukemia.

This retrospective study at the University of Texas MD Anderson Cancer Center evaluated frontline venetoclax combination therapy in 11 pediatric/adolescent patients with acute myeloid leukemia (AML). Despite the small sample size and retrospective nature, the treatment demonstrated safety and potential efficacy, with most patients achieving early complete remission. Adverse events were consistent with other AML therapies, and no discontinuations due to toxicity occurred. While acknowledging study limitations, including selection bias and diverse concurrent therapies, this research underscores the promising role of venetoclax in pediatric AML. Further investigation is crucial to validate its long-term efficacy in this population.

Neuromicrobiology Comes of Age: The Multifaceted Interactions between the Microbiome and the Nervous System.

The past decade has seen an explosion in our knowledge about the interactions between gut microbiota, the central nervous system, and the immune system. The gut-brain axis has recently gained much attention due to its role in regulating host physiology. This review explores recent findings concerning potential pathways linking the gut-brain axis to the initiation, pathophysiology, and development of neurological disorders. Our objective of this work is to uncover causative factors and pinpoint particular pathways and therapeutic targets that may facilitate the translation of experimental animal research into practical applications for human patients. We highlight three distinct yet interrelated mechanisms: (1) disruptions of both the intestinal and blood-brain barriers, (2) persistent neuroinflammation, and (3) the role of the vagus nerve.

Characteristics and outcomes of children, adolescent, and young adult patients with myelodysplastic neoplasms: A single-center retrospective analysis.

Myelodysplastic syndrome, or myelodysplastic neoplasms, are a rare finding in pediatric, adolescent, and young adult (AYA) patients. More literature is needed to highlight trends of survival or treatment resistance in subpopulations to improve treatment. Here we report a single center retrospective analysis of pediatric and AYA patients from 2000 to 2022 including molecular and cytogenetic data. Using the IPSS-R and IPSS-M, which have been reported exclusively in adults, and excluding patients with bone marrow failure syndromes, we analyzed 119 pediatric and AYA patients with myelodysplastic neoplasms. Therapy-related myelodysplastic neoplasms were present in 36 % of patients, and 31 % of patients developed acute myeloid leukemia. The 5-year overall survival (OS) rate for the entire cohort was 45 %. Contrary to young adults and older adults, mutations were not common in pediatrics. Those who underwent stem cell transplant (SCT)(at any time) had significantly longer median OS. Although SCT at any time improved OS in the de novo myelodysplastic neoplasm group, the choice of the initial treatment with intensive chemotherapy, hypomethylating agents, or SCT did not significantly alter OS. Median OS was shorter in the pediatric group (<18 years old) and longer for those with isolated deletion of 5q or TET2 mutation, but these were not significant findings. Median OS was significantly shorter in those with monosomy 7 or 7q deletion and those with therapy-related myelodysplastic neoplasms. These findings build on previously reported findings and encourage the use of SCT along with molecular and cytogenetic analysis.

Dual quorum-sensing control of purine biosynthesis drives pathogenic fitness of Enterococcus faecalis.

Enterococcus faecalis is a resident of the human gut, though upon translocation to the blood or body tissues, it can be pathogenic. Here we discover and characterize two peptide-based quorum-sensing systems that transcriptionally modulate de novo purine biosynthesis in E. faecalis. Using a comparative genomic analysis, we find that most enterococcal species do not encode this system; E. moraviensis, E. haemoperoxidus and E. caccae, three species that are closely related to E. faecalis, encode one of the two systems, and only E. faecalis encodes both systems. We show that these systems are important for the intracellular survival of E. faecalis within macrophages and for the fitness of E. faecalis in a murine wound infection model. Taken together, we combine comparative genomics, microbiological, bacterial genetics, transcriptomics, targeted proteomics and animal model experiments to describe a paired quorum sensing mechanism that directly influences central metabolism and impacts the pathogenicity of E. faecalis.

Computational Design of Pore-Forming Peptides with Potent Antimicrobial and Anticancer Activities.

Peptides that form transmembrane barrel-stave pores are potential alternative therapeutics for bacterial infections and cancer. However, their optimization for clinical translation is hampered by a lack of sequence-function understanding. Recently, we have de novo designed the first synthetic barrel-stave pore-forming antimicrobial peptide with an identified function of all residues. Here, we systematically mutate the peptide to improve pore-forming ability in anticipation of enhanced activity. Using computer simulations, supported by liposome leakage and atomic force microscopy experiments, we find that pore-forming ability, while critical, is not the limiting factor for improving activity in the submicromolar range. Affinity for bacterial and cancer cell membranes needs to be optimized simultaneously. Optimized peptides more effectively killed antibiotic-resistant ESKAPEE bacteria at submicromolar concentrations, showing low cytotoxicity to human cells and skin model. Peptides showed systemic anti-infective activity in a preclinical mouse model of Acinetobacter baumannii infection. We also demonstrate peptide optimization for pH-dependent antimicrobial and anticancer activity.

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.

Deep-learning-enabled antibiotic discovery through molecular de-extinction.

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.