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.

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.

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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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.

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.

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.

Feasible diet and circadian interventions reduce in vivo progression of FLT3-ITD-positive acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) with an internal tandem duplication in the fms-like tyrosine kinase receptor 3 gene (FLT3-ITD) is associated with poor survival, and few studies have examined the impact of modifiable behaviors, such as nutrient quality and timing, in this subset of acute leukemia. METHODS: The influence of diet composition (low-sucrose and/or low-fat diets) and timing of diet were tested in tandem with anthracycline treatment in orthotopic xenograft mouse models. A pilot clinical study to test receptivity of pediatric leukemia patients to macronutrient matched foods was conducted. A role for the circadian protein, BMAL1 (brain and muscle ARNT-like 1), in effects of diet timing was studied by overexpression in FLT3-ITD-bearing AML cells. RESULTS: Reduced tumor burden in FLT3-ITD AML-bearing mice was observed with interventions utilizing low-sucrose and/or low-fat diets, or time-restricted feeding (TRF) compared to mice fed normal chow ad libitum. In a tasting study, macronutrient matched low-sucrose and low-fat meals were offered to pediatric acute leukemia patients who largely reported liking the meals. Expression of the circadian protein, BMAL1, was heightened with TRF and the low-sucrose diet. BMAL1 overexpression and treatment with a pharmacological inducer of BMAL1 was cytotoxic to FLT3-ITD AML cells. CONCLUSIONS: Mouse models for FLT3-ITD AML show that diet composition and timing slows progression of FLT3-ITD AML growth in vivo, potentially mediated by BMAL1. These interventions to enhance therapy efficacy show preliminary feasibility, as pediatric leukemia patients responded favorable to preparation of macronutrient matched meals.

Synthetic angiotensin II peptide derivatives confer protection against cerebral and severe non-cerebral malaria in murine models.

Malaria can have severe long-term effects. Even after treatment with antimalarial drugs eliminates the parasite, survivors of cerebral malaria may suffer from irreversible brain damage, leading to cognitive deficits. Angiotensin II, a natural human peptide hormone that regulates blood pressure, has been shown to be active against Plasmodium spp., the etiologic agent of malaria. Here, we tested two Ang II derivatives that do not elicit vasoconstriction in mice: VIPF, a linear tetrapeptide, which constitutes part of the hydrophobic portion of Ang II; and Ang II-SS, a disulfide-bridged derivative. The antiplasmodial potential of both peptides was evaluated with two mouse models: an experimental cerebral malaria model and a mouse model of non-cerebral malaria. The latter consisted of BALB/c mice infected with Plasmodium berghei ANKA. The peptides had no effect on mean blood pressure and significantly reduced parasitemia in both mouse models. Both peptides reduced the SHIRPA score, an assay used to assess murine health and behavior. However, only the constrained derivative (Ang II-SS), which was also resistant to proteolytic degradation, significantly increased mouse survival. Here, we show that synthetic peptides derived from Ang II are capable of conferring protection against severe manifestations of malaria in mouse models while overcoming the vasoconstrictive side effects of the parent peptide.

Low-Cost Biosensor Technologies for Rapid Detection of COVID-19 and Future Pandemics.

Many systems have been designed for the detection of SARS-CoV-2, which is the virus that causes COVID-19. SARS-CoV-2 is readily transmitted, resulting in the rapid spread of disease in human populations. Frequent testing at the point of care (POC) is a key aspect for controlling outbreaks caused by SARS-CoV-2 and other emerging pathogens, as the early identification of infected individuals can then be followed by appropriate measures of isolation or treatment, maximizing the chances of recovery and preventing infectious spread. Diagnostic tools used for high-frequency testing should be inexpensive, provide a rapid diagnostic response without sophisticated equipment, and be amenable to manufacturing on a large scale. The application of these devices should enable large-scale data collection, help control viral transmission, and prevent disease propagation. Here we review functional nanomaterial-based optical and electrochemical biosensors for accessible POC testing for COVID-19. These biosensors incorporate nanomaterials coupled with paper-based analytical devices and other inexpensive substrates, traditional lateral flow technology (antigen and antibody immunoassays), and innovative biosensing methods. We critically discuss the advantages and disadvantages of nanobiosensor-based approaches compared to widely used technologies such as PCR, ELISA, and LAMP. Moreover, we delineate the main technological, (bio)chemical, translational, and regulatory challenges associated with developing functional and reliable biosensors, which have prevented their translation into the clinic. Finally, we highlight how nanobiosensors, given their unique advantages over existing diagnostic tests, may help in future pandemics.

The pupillary dilation reflex to a nociceptive stimulus as a tool for analgesia management: A diagnostic study.

BACKGROUND: More than 70% of patients demonstrate pain after endotracheal aspiration. Tools are needed to objectify the need for analgesia in non-communicative critically ill patients. OBJECTIVE: The objective of this study was to identify the lowest intensity electrical stimulus for detecting pain before daily care interventions. METHODS: Study of diagnostic tests to assess pupillometry to detect pain through the pupillary dilation response to noxious stimuli versus the Behavioural Pain Scale. Patients older than 18 years, under analgosedation, subjected to invasive ventilation, baseline Behavioural Pain Scale of 3, and Richmond Agitation-Sedation Scale between -1 and -4 were studied. We assessed the Behavioural Pain Scale and the pupillary dilation response to 10, 20, 30, and 40 mA stimuli. We studied the diagnostic performance based on sensitivity and specificity, negative predictive value, positive predictive value, and accuracy of the selected points after the different stimulations. AlgiScan® Pupillometer measured the pupillary dilation response. The presence of pain was considered as a Behavioural Pain Scale score of ≥4. Significance was defined as p <0.05. RESULTS: Measurements were performed on 31 patients. In the 20 mA stimulus, we found an area under the curve of 0.85 (0.69-1.0). The cut-off point of pupillary dilation was 11.5%, with a sensitivity of 100% (34.2-100) and a specificity of 75.9% (57.9-87.8). This point had an accuracy of 77.4 (60.2-88.6) and a Youden's Index of 0.8. CONCLUSIONS: Pupillary variation measurement during a 20 mA stimulus could help assess the need for analgesia before potentially painful interventions. Further studies are needed to confirm this. REGISTRATION: Phase 1 of the project PUPIPAIN ClinicalTrials.gov Identifier: NCT04078113.

Computational Design of Peptides for Biomaterials Applications.

Computer-aided molecular design and protein engineering emerge as promising and active subjects in bioengineering and biotechnological applications. On one hand, due to the advancing computing power in the past decade, modeling toolkits and force fields have been put to use for accurate multiscale modeling of biomolecules including lipid, protein, carbohydrate, and nucleic acids. On the other hand, machine learning emerges as a revolutionary data analysis tool that promises to leverage physicochemical properties and structural information obtained from modeling in order to build quantitative protein structure-function relationships. We review recent computational works that utilize state-of-the-art computational methods to engineer peptides and proteins for various emerging biomedical, antimicrobial, and antifreeze applications. We also discuss challenges and possible future directions toward developing a roadmap for efficient biomolecular design and engineering.