The Habitat Filters of Microbiota-Nourishing Immunity.

An imbalance in the microbiota may contribute to many human illnesses, which has prompted efforts to rebalance it by targeting the microbes themselves. However, by supplying the habitat, the host wields a prominent influence over microbial growth at body surfaces, raising the possibility that rebalancing the microbiota by targeting our immune system would be a viable alternative. Host control mechanisms that sculpt the microbial habitat form a functional unit with the microbiota, termed microbiota-nourishing immunity, that confers colonization resistance against pathogens. The host components of microbiota-nourishing immunity can be viewed as habitat filters that select for microbial traits licensing growth and survival in host habitat patches. Here we review current knowledge of how host-derived habitat filters shape the size, species composition, and spatial heterogeneity of the microbiota and discuss whether these host control mechanisms could be harnessed for developing approaches to rebalance microbial communities during dysbiosis.

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.

Portable, On-Demand Biomolecular Manufacturing.

Synthetic biology uses living cells as molecular foundries for the biosynthesis of drugs, therapeutic proteins, and other commodities. However, the need for specialized equipment and refrigeration for production and distribution poses a challenge for the delivery of these technologies to the field and to low-resource areas. Here, we present a portable platform that provides the means for on-site, on-demand manufacturing of therapeutics and biomolecules. This flexible system is based on reaction pellets composed of freeze-dried, cell-free transcription and translation machinery, which can be easily hydrated and utilized for biosynthesis through the addition of DNA encoding the desired output. We demonstrate this approach with the manufacture and functional validation of antimicrobial peptides and vaccines and present combinatorial methods for the production of antibody conjugates and small molecules. This synthetic biology platform resolves important practical limitations in the production and distribution of therapeutics and molecular tools, both to the developed and developing world.

Age-Related Loss of Innate Immune Antimicrobial Function of Dermal Fat Is Mediated by Transforming Growth Factor Beta.

Dermal fibroblasts (dFBs) resist infection by locally differentiating into adipocytes and producing cathelicidin antimicrobial peptide in response to Staphylococcus aureus (S. aureus). Here, we show that neonatal skin was enriched with adipogenic dFBs and immature dermal fat that highly expressed cathelicidin. The pool of adipogenic and antimicrobial dFBs declined after birth, leading to an age-dependent loss of dermal fat and a decrease in adipogenesis and cathelidicin production in response to infection. Transforming growth factor beta (TGF-ß), which acted on uncommitted embryonic and adult dFBs and inhibited their adipogenic and antimicrobial function, was identified as a key upstream regulator of this process. Furthermore, inhibition of the TGF-ß receptor restored the adipogenic and antimicrobial function of dFBs in culture and increased resistance of adult mice to S. aureus infection. These results provide insight into changes that occur in the skin innate immune system between the perinatal and adult periods of life.

Hundreds of antimicrobial peptides create a selective barrier for insect gut symbionts.

The spatial organization of gut microbiota is crucial for the functioning of the gut ecosystem, although the mechanisms that organize gut bacterial communities in microhabitats are only partially understood. The gut of the insect Riptortus pedestris has a characteristic microbiota biogeography with a multispecies community in the anterior midgut and a monospecific bacterial population in the posterior midgut. We show that the posterior midgut region produces massively hundreds of specific antimicrobial peptides (AMPs), the Crypt-specific Cysteine-Rich peptides (CCRs) that have membrane-damaging antimicrobial activity against diverse bacteria but posterior midgut symbionts have elevated resistance. We determined by transposon-sequencing the genetic repertoire in the symbiont Caballeronia insecticola to manage CCR stress, identifying different independent pathways, including AMP-resistance pathways unrelated to known membrane homeostasis functions as well as cell envelope functions. Mutants in the corresponding genes have reduced capacity to colonize the posterior midgut, demonstrating that CCRs create a selective barrier and resistance is crucial in gut symbionts. Moreover, once established in the gut, the bacteria differentiate into a CCR-sensitive state, suggesting a second function of the CCR peptide arsenal in protecting the gut epithelia or mediating metabolic exchanges between the host and the gut symbionts. Our study highlights the evolution of an extreme diverse AMP family that likely contributes to establish and control the gut microbiota.

Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes.

It is unclear how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to the strong but ineffective inflammatory response that characterizes severe Coronavirus disease 2019 (COVID-19), with amplified immune activation in diverse cell types, including cells without angiotensin-converting enzyme 2 receptors necessary for infection. Proteolytic degradation of SARS-CoV-2 virions is a milestone in host viral clearance, but the impact of remnant viral peptide fragments from high viral loads is not known. Here, we examine the inflammatory capacity of fragmented viral components from the perspective of supramolecular self-organization in the infected host environment. Interestingly, a machine learning analysis to SARS-CoV-2 proteome reveals sequence motifs that mimic host antimicrobial peptides (xenoAMPs), especially highly cationic human cathelicidin LL-37 capable of augmenting inflammation. Such xenoAMPs are strongly enriched in SARS-CoV-2 relative to low-pathogenicity coronaviruses. Moreover, xenoAMPs from SARS-CoV-2 but not low-pathogenicity homologs assemble double-stranded RNA (dsRNA) into nanocrystalline complexes with lattice constants commensurate with the steric size of Toll-like receptor (TLR)-3 and therefore capable of multivalent binding. Such complexes amplify cytokine secretion in diverse uninfected cell types in culture (epithelial cells, endothelial cells, keratinocytes, monocytes, and macrophages), similar to cathelicidin's role in rheumatoid arthritis and lupus. The induced transcriptome matches well with the global gene expression pattern in COVID-19, despite using <0.3% of the viral proteome. Delivery of these complexes to uninfected mice boosts plasma interleukin-6 and CXCL1 levels as observed in COVID-19 patients.

TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation.

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Immune defenses in the urinary tract.

Recent advances in preclinical modeling of urinary tract infections (UTIs) have enabled the identification of key facets of the host response that influence pathogen clearance and tissue damage. Here, we review new insights into the functions of neutrophils, macrophages, and antimicrobial peptides in innate control of uropathogens and in mammalian infection-related tissue injury and repair. We also discuss novel functions for renal epithelial cells in innate antimicrobial defense. In addition, epigenetic modifications during bacterial cystitis have been implicated in bladder remodeling, conveying susceptibility to recurrent UTI. In total, contemporary work in this arena has better defined host processes that shape UTI susceptibility and severity and might inform the development of novel preventive and therapeutic approaches for acute and recurrent UTI.

Resistance to host antimicrobial peptides mediates resilience of gut commensals during infection and aging in Drosophila.

Resilience to short-term perturbations, like inflammation, is a fundamental feature of microbiota, yet the underlying mechanisms of microbiota resilience are incompletely understood. Here, we show that Lactiplantibacillus plantarum, a major Drosophila commensal, stably colonizes the fruit fly gut during infection and is resistant to Drosophila antimicrobial peptides (AMPs). By transposon screening, we identified L. plantarum mutants sensitive to AMPs. These mutants were impaired in peptidoglycan O-acetylation or teichoic acid D-alanylation, resulting in increased negative cell surface charge and higher affinity to cationic AMPs. AMP-sensitive mutants were cleared from the gut after infection and aging-induced gut inflammation in wild-type, but not in AMP-deficient flies, suggesting that resistance to host AMPs is essential for commensal resilience in an inflamed gut environment. Thus, our work reveals that in addition to the host immune tolerance to the microbiota, commensal-encoded resilience mechanisms are necessary to maintain the stable association between host and microbiota during inflammation.

Synthetic peptides that form nanostructured micelles have potent antibiotic and antibiofilm activity against polymicrobial infections.

The emergence of multidrug-resistant bacterial pathogens is a growing threat to global public health. Here, we report the development and characterization of a panel of nine-amino acid residue synthetic peptides that display potent antibacterial activity and the ability to disrupt preestablished microbial biofilms. The lead peptide (Peptide K6) showed bactericidal activity against Pseudomonas aeruginosa and Staphylococcus aureus in culture and in monocultures and mixed biofilms in vitro. Biophysical analysis revealed that Peptide K6 self-assembled into nanostructured micelles that correlated with its strong antibiofilm activity. When surface displayed on the outer membrane protein LamB, two copies of the Peptide K6 were highly bactericidal to Escherichia coli. Peptide K6 rapidly increased the permeability of bacterial cells, and resistance to this toxic peptide occurred less quickly than that to the potent antibiotic gentamicin. Furthermore, we found that Peptide K6 was safe and effective in clearing mixed P. aeruginosa-S. aureus biofilms in a mouse model of persistent infection. Taken together, the properties of Peptide K6 suggest that it is a promising antibiotic candidate and that design of additional short peptides that form micelles represents a worthwhile approach for the development of antimicrobial agents.

Cross talk between Paneth and tuft cells drives dysbiosis and inflammation in the gut mucosa.

Gut microbiota imbalance (dysbiosis) is increasingly associated with pathological conditions, both within and outside the gastrointestinal tract. Intestinal Paneth cells are considered to be guardians of the gut microbiota, but the events linking Paneth cell dysfunction with dysbiosis remain unclear. We report a three-step mechanism for dysbiosis initiation. Initial alterations in Paneth cells, as frequently observed in obese and inflammatorybowel diseases patients, cause a mild remodeling of microbiota, with amplification of succinate-producing species. SucnR1-dependent activation of epithelial tuft cells triggers a type 2 immune response that, in turn, aggravates the Paneth cell defaults, promoting dysbiosis and chronic inflammation. We thus reveal a function of tuft cells in promoting dysbiosis following Paneth cell deficiency and an unappreciated essential role of Paneth cells in maintaining a balanced microbiota to prevent inappropriate activation of tuft cells and deleterious dysbiosis. This succinate-tuft cell inflammation circuit may also contribute to the chronic dysbiosis observed in patients.

Tuft cells mediate commensal remodeling of the small intestinal antimicrobial landscape.

Succinate produced by the commensal protist Tritrichomonas musculis (T. mu) stimulates chemosensory tuft cells, resulting in intestinal type 2 immunity. Tuft cells express the succinate receptor SUCNR1, yet this receptor does not mediate antihelminth immunity nor alter protist colonization. Here, we report that microbial-derived succinate increases Paneth cell numbers and profoundly alters the antimicrobial peptide (AMP) landscape in the small intestine. Succinate was sufficient to drive this epithelial remodeling, but not in mice lacking tuft cell chemosensory components required to detect this metabolite. Tuft cells respond to succinate by stimulating type 2 immunity, leading to interleukin-13-mediated epithelial and AMP expression changes. Moreover, type 2 immunity decreases the total number of mucosa-associated bacteria and alters the small intestinal microbiota composition. Finally, tuft cells can detect short-term bacterial dysbiosis that leads to a spike in luminal succinate levels and modulate AMP production in response. These findings demonstrate that a single metabolite produced by commensals can markedly shift the intestinal AMP profile and suggest that tuft cells utilize SUCNR1 and succinate sensing to modulate bacterial homeostasis.

Repurposing HDAC inhibitors to enhance ribonuclease 4 and 7 expression and reduce urinary tract infection.

With the emergence of antibiotic-resistant bacteria, innovative approaches are needed for the treatment of urinary tract infections. Boosting antimicrobial peptide expression may provide an alternative to antibiotics. Here, we developed reporter cell lines and performed a high-throughput screen of clinically used drugs to identify compounds that boost ribonuclease 4 and 7 expression (RNase 4 and 7), peptides that have antimicrobial activity against antibiotic-resistant uropathogens. This screen identified histone deacetylase (HDAC) inhibitors as effective RNase 4 and RNase 7 inducers. Validation studies in primary human kidney and bladder cells confirmed pan-HDAC inhibitors as well as the HDAC class I inhibitor, MS-275, induce RNase 4 and RNase 7 to protect human kidney and bladder cells from uropathogenic Escherichia coli. When we administered MS-275 to mice, RNase 4 and 7 expression increased and mice were protected from acute transurethral E. coli challenge. In support of this mechanism, MS-275 treatment increased acetylated histone H3 binding to the RNASE4 and RNASE7 promoters. Overexpression and knockdown of HDAC class I proteins identified HDAC3 as a primary regulator of RNase 4 and 7. These results demonstrate the protective effects of enhancing RNase 4 and RNase 7, opening the door to repurposing medications as antibiotic conserving therapeutics for urinary tract infection.

DNA-binding and protein structure of nuclear factors likely acting in genetic information processing in the Paulinella chromatophore.

The chromatophores in Paulinella are evolutionary-early-stage photosynthetic organelles. Biological processes in chromatophores depend on a combination of chromatophore and nucleus-encoded proteins. Interestingly, besides proteins carrying chromatophore-targeting signals, a large arsenal of short chromatophore-targeted proteins (sCTPs; <90 amino acids) without recognizable targeting signals were found in chromatophores. This situation resembles endosymbionts in plants and insects that are manipulated by host-derived antimicrobial peptides. Previously, we identified an expanded family of sCTPs of unknown function, named here "DNA-binding (DB)-sCTPs". DB-sCTPs contain a ~45 amino acid motif that is conserved in some bacterial proteins with predicted functions in DNA processing. Here, we explored antimicrobial activity, DNA-binding capacity, and structures of three purified recombinant DB-sCTPs. All three proteins exhibited antimicrobial activity against bacteria involving membrane permeabilization, and bound to bacterial lipids in vitro. A combination of in vitro assays demonstrated binding of recombinant DB-sCTPs to chromatophore-derived genomic DNA sequences with an affinity in the low nM range. Additionally, we report the 1.2 Å crystal structure of one DB-sCTP. In silico docking studies suggest that helix α2 inserts into the DNA major grove and the exposed residues, that are highly variable between different DB-sCTPs, confer interaction with the DNA bases. Identification of photosystem II subunit CP43 as a potential interaction partner of one DB-sCTP, suggests DB-sCTPs to be involved in more complex regulatory mechanisms. We hypothesize that membrane binding of DB-sCTPs is related to their import into chromatophores. Once inside, they interact with the chromatophore genome potentially providing nuclear control over genetic information processing.

Intestinal biofilms: pathophysiological relevance, host defense, and therapeutic opportunities.

SUMMARYThe human intestinal tract harbors a profound variety of microorganisms that live in symbiosis with the host and each other. It is a complex and highly dynamic environment whose homeostasis directly relates to human health. Dysbiosis of the gut microbiota and polymicrobial biofilms have been associated with gastrointestinal diseases, including irritable bowel syndrome, inflammatory bowel diseases, and colorectal cancers. This review covers the molecular composition and organization of intestinal biofilms, mechanistic aspects of biofilm signaling networks for bacterial communication and behavior, and synergistic effects in polymicrobial biofilms. It further describes the clinical relevance and diseases associated with gut biofilms, the role of biofilms in antimicrobial resistance, and the intestinal host defense system and therapeutic strategies counteracting biofilms. Taken together, this review summarizes the latest knowledge and research on intestinal biofilms and their role in gut disorders and provides directions toward the development of biofilm-specific treatments.

Regulatory interactions between daptomycin- and bacitracin-responsive pathways coordinate the cell envelope antibiotic resistance response of Enterococcus faecalis.

Enterococcal infections frequently show high levels of antibiotic resistance, including to cell envelope-acting antibiotics like daptomycin (DAP). While we have a good understanding of the resistance mechanisms, less is known about the control of such resistance genes in enterococci. Previous work unveiled a bacitracin resistance network, comprised of the sensory ABC transporter SapAB, the two-component system (TCS) SapRS and the resistance ABC transporter RapAB. Interestingly, components of this system have recently been implicated in DAP resistance, a role usually regulated by the TCS LiaFSR. To better understand the regulation of DAP resistance and how this relates to mutations observed in DAP-resistant clinical isolates of enterococci, we here explored the interplay between these two regulatory pathways. Our results show that SapR regulates an additional resistance operon, dltXABCD, a known DAP resistance determinant, and show that LiaFSR regulates the expression of sapRS. This regulatory structure places SapRS-target genes under dual control, where expression is directly controlled by SapRS, which itself is up-regulated through LiaFSR. The network structure described here shows how Enterococcus faecalis coordinates its response to cell envelope attack and can explain why clinical DAP resistance often emerges via mutations in regulatory components.

Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure-activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4-8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.

As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.