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.

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.

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.

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.

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.

Influence of antimicrobial peptides on the bacterial membrane curvature and vice versa.

Many factors contribute to bacterial membrane stabilization, including steric effects between lipids, membrane spontaneous curvature, and the difference in the number of neighboring molecules. This forum provides an overview of the physicochemical properties associated with membrane curvature and how this parameter can be tuned to design more effective antimicrobial peptides.

Combination low-intensity chemotherapy plus inotuzumab ozogamicin, blinatumomab and rituximab for pediatric patients with relapsed/refractory B-cell acute lymphoblastic leukemia.

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Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties.

Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators such as IL-6, TNF-α, and MCP-1. Moreover, eight of the peptides identified, collagenin 3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may play a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.

Polyproline peptide targets Klebsiella pneumoniae polysaccharides to collapse biofilms.

Hypervirulent Klebsiella pneumoniae is known for its increased extracellular polysaccharide production. Biofilm matrices of hypervirulent K. pneumoniae have increased polysaccharide abundance and are uniquely susceptible to disruption by peptide bactenecin 7 (bac7 (1-35)). Here, using confocal microscopy, we show that polysaccharides within the biofilm matrix collapse following bac7 (1-35) treatment. This collapse led to the release of cells from the biofilm, which were then killed by the peptide. Characterization of truncated peptide analogs revealed that their interactions with polysaccharide were responsible for the biofilm matrix changes that accompany bac7 (1-35) treatment. Ultraviolet photodissociation mass spectrometry with the parental peptide or a truncated analog bac7 (10-35) reveal the important regions for bac7 (1-35) complexing with polysaccharides. Finally, we tested bac7 (1-35) using a murine skin abscess model and observed a significant decrease in the bacterial burden. These findings unveil the potential of bac7 (1-35) polysaccharide interactions to collapse K. pneumoniae biofilms.

Biological Markers of High-Risk Childhood Acute Lymphoblastic Leukemia.

Childhood acute lymphoblastic leukemia (ALL) has witnessed substantial improvements in prognosis; however, a subset of patients classified as high-risk continues to face higher rates of relapse and increased mortality. While the National Cancer Institute (NCI) criteria have traditionally guided risk stratification based on initial clinical information, recent advances highlight the pivotal role of biological markers in shaping the prognosis of childhood ALL. This review delves into the emerging understanding of high-risk childhood ALL, focusing on molecular, cytogenetic, and immunophenotypic markers. These markers not only contribute to unraveling the underlying mechanisms of the disease, but also shed light on specific clinical patterns that dictate prognosis. The paradigm shift in treatment strategies, exemplified by the success of tyrosine kinase inhibitors in Philadelphia chromosome-positive leukemia, underscores the importance of recognizing and targeting precise risk factors. Through a comprehensive exploration of high-risk childhood ALL characteristics, this review aims to enhance our comprehension of the disease, offering insights into its molecular landscape and clinical intricacies in the hope of contributing to future targeted and tailored therapies.

Challenges in computational discovery of bioactive peptides in 'omics data.

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.