The three-sided right-handed ß-helix is a versatile fold for glycan interactions.

Interactions between proteins and glycans are critical to various biological processes. With databases of carbohydrate-interacting proteins and increasing amounts of structural data, the three-sided right-handed ß-helix (RHBH) has emerged as a significant structural fold for glycan interactions. In this review, we provide an overview of the sequence, mechanistic, and structural features that enable the RHBH to interact with glycans. The RHBH is a prevalent fold that exists in eukaryotes, prokaryotes, and viruses associated with adhesin and carbohydrate-active enzyme (CAZyme) functions. An evolutionary trajectory analysis on structurally characterized RHBH-containing proteins shows that they likely evolved from carbohydrate-binding proteins with their carbohydrate-degrading activities evolving later. By examining three polysaccharide lyase and three glycoside hydrolase structures, we provide a detailed view of the modes of glycan binding in RHBH proteins. The 3-dimensional shape of the RHBH creates an electrostatically and spatially favorable glycan binding surface that allows for extensive hydrogen bonding interactions, leading to favorable and stable glycan binding. The RHBH is observed to be an adaptable domain capable of being modified with loop insertions and charge inversions to accommodate heterogeneous and flexible glycans and diverse reaction mechanisms. Understanding this prevalent protein fold can advance our knowledge of glycan binding in biological systems and help guide the efficient design and utilization of RHBH-containing proteins in glycobiology research.

Nonlinear metasurface engineering with disordered gold nanorods on ITO: a cost-effective approach to broadband response, polarization-independence, and weak nonlinear index dispersion.

The strong coupling of epsilon-near-zero materials with nanoantennas has demonstrated enhanced nonlinear optical responses, yet practical challenges persist. Here, we propose an alternative: an ultrathin metasurface featuring broadband response with a weakly dispersive nonlinear index, achieved through a simple implementation. Our metasurface, comprising a disordered gold nanorod array on indium tin oxide, exhibits polarization-independent behavior and a large average nonlinear refractive index of 5 cm2/GW across a broad wavelength range (1000-1300 nm). Enhanced performance is attributed to the weak coupling between gold nanorods and indium tin oxide, offering a cost-effective method for nonlinear optical metasurfaces and a flexible design in nanophotonic applications.

Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.

BACKGROUND: Approximately 50% of breast mucinous carcinomas (MCs) are oval and have the possibility of being misdiagnosed as fibroadenomas (FAs). We aimed to identify the key features that can help differentiate breast MC with an oval shape from FA on ultrasonography (US). METHODS: Seventy-six MCs from 71 consecutive patients and 50 FAs with an oval shape from 50 consecutive patients were included in our study. All lesions pathologically diagnosed. According to the Breast Imaging Reporting and Data System (BI-RADS), first, the ultrasonographic features of the MCs and FAs were recorded and a final category was assessed. Then, the differences in ultrasonographic characteristics between category 4 A (low-risk group) and category 4B-5 (medium-high- risk group) MCs were identified. Finally, other ultrasonographic features of MC and FA both with an oval shape were compared to determine the key factors for differential diagnosis. The Mann-Whitney test, χ2 test or Fisher's exact test was used to compare data between groups. RESULTS: MCs with an oval shape (81.2%) and a circumscribed margin (25%) on US were more commonly assessed in the low-risk group (BI-RADS 4 A) than in the medium-high-risk group (BI-RADS 4B-5) (20%, p < 0.001 and 0%, p = 0.001, respectively). Compared with those with FA, patients with MC were older, and tended to have masses with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement on US (p < 0.001, p < 0.001, and p = 0.003, respectively). CONCLUSION: The oval shape was the main reason for the underestimation of MCs. On US, an oval mass found in the breast of women of older age with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement was associated with an increased risk of being an MC, and should be subjected to active biopsy.

Protein Kinase N1 Level Predicts Acute Kidney Injury in Patients Undergoing Cardiac Surgery: A Prospective Cohort Study.

INTRODUCTION: The objective of this study was to examine the utility of protein kinase N1 (PKN1) as a biomarker of cardiac surgery-associated AKI (CSA-AKI). METHODS: A prospective cohort study of 110 adults undergoing on-pump cardiac surgery was conducted. The associations between post-operative PKN1 and CSA-AKI, AKI severity, need for renal replacement therapy (RRT), duration of AKI, length of ICU stay, and post-operative hospital stay were evaluated. RESULTS: Patients were categorized into three groups according to PKN1 tertiles. The incidence of CSA-AKI in the third tertile was 3.4-fold higher than that in the first. PKN1 was an independent risk factor for CSA-AKI. The discrimination of PKN1 to CSA-AKI assessed by ROC curve indicated that the AUC was 0.70, and the best cutoff was 5.025 ng/mL. This group (>5.025 ng/mL) was more likely to develop CSA-AKI (p < 0.001). The combined AUC of EuroSCORE, aortic cross-clamp time, and PKN1 was 0.82 (p < 0.001). A higher level of PKN1 was related to increased need for RRT, longer duration of AKI, and length of ICU and post-operative hospital stays. CONCLUSIONS: PKN1 could be a potential biomarker for the prediction of CSA-AKI. The combination of PKN1, EuroSCORE, and aortic cross-clamp time was likely to predict the occurrence of CSA-AKI.

Annotating microbial functions with ProkFunFind.

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Targeting anticancer immunity in melanoma tumour microenvironment: unleashing the potential of adjuvants, drugs, and phytochemicals.

Melanoma poses a challenge in oncology because of its aggressive nature and limited treatment modalities. The tumour microenvironment (TME) in melanoma contains unique properties such as an immunosuppressive and high-density environment, unusual vasculature, and a high number of stromal and immunosuppressive cells. In recent years, numerous experiments have focused on boosting the immune system to effectively remove malignant cells. Adjuvants, consisting of phytochemicals, toll-like receptor (TLR) agonists, and cytokines, have shown encouraging results in triggering antitumor immunity and augmenting the therapeutic effectiveness of anticancer therapy. These adjuvants can stimulate the maturation of dendritic cells (DCs) and infiltration of cytotoxic CD8+ T lymphocytes (CTLs). Furthermore, nanocarriers can help to deliver immunomodulators and antigens directly to the tumour stroma, thereby improving their efficacy against malignant cells. The remodelling of melanoma TME utilising phytochemicals, agonists, and other adjuvants can be combined with current modalities for improving therapy outcomes. This review article explores the potential of adjuvants, drugs, and their nanoformulations in enhancing the anticancer potency of macrophages, CTLs, and natural killer (NK) cells. Additionally, the capacity of these agents to repress the function of immunosuppressive components of melanoma TME, such as immunosuppressive subsets of macrophages, stromal and myeloid cells will be discussed.

Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants.

BACKGROUND: Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations presents a considerable challenge. Currently, there is a notable lack of tools designed for large-scale characterization of phage receptor-binding proteins, which are crucial in determining the phage host range. RESULTS: In this study, we present SpikeHunter, a deep learning method based on the ESM-2 protein language model. With SpikeHunter, we identified 231,965 diverse phage-encoded tailspike proteins, a crucial determinant of phage specificity that targets bacterial polysaccharide receptors, across 787,566 bacterial genomes from 5 virulent, antibiotic-resistant pathogens. Notably, 86.60% (143,200) of these proteins exhibited strong associations with specific bacterial polysaccharides. We discovered that phages with identical tailspike proteins can infect different bacterial species with similar polysaccharide receptors, underscoring the pivotal role of tailspike proteins in determining host range. The specificity is mainly attributed to the protein's C-terminal domain, which strictly correlates with host specificity during domain swapping in tailspike proteins. Importantly, our dataset-driven predictions of phage-host specificity closely match the phage-host pairs observed in real-world phage therapy cases we studied. CONCLUSIONS: Our research provides a rich resource, including both the method and a database derived from a large-scale genomics survey. This substantially enhances understanding of phage specificity determinants at the strain level and offers a valuable framework for guiding phage selection in therapeutic applications.

What happens when left meets right under equimolar and non-equimolar co-assembly of short peptide stereoisomers?

The co-assembly of different peptide chains usually leads to the formation of intricate architectures and sophisticated functions in biological systems. Although the co-assembly of stereoisomeric peptides represents a facile and flexible strategy for the synthesis of peptide-based nanomaterials with novel structures and potentially interesting properties, there is a lack of a general knowledge on how different isomers pack during assembly. Through the combined use of simulations and experimental observations, we report that heterochiral pairing is preferred to homochiral pairing at the molecular scale but self-sorting dictates beyond the molecular level for the mixtures of the short stereoisomeric ß-sheet peptides I3K (Ile-Ile-Ile-Lys). Furthermore, we demonstrate that flat ß-sheets and fibril morphology are always preferred to twisted ones during heterochiral pairing and subsequent assembly. However, the heterochiral pairing into flat morphology is not always at an equimolar ratio. Instead, a non-equimolar ratio (1:2) is observed for the mixing of homochiral LI3LK and heterochiral LI3DK, whose strand twisting degrees differ greatly. Such a study provides a paradigm for understanding the co-assembly of stereoisomeric peptides at the molecular scale and harnessing their blending for targeted nanostructures.

Controlled Assembly and Disassembly of Higher-Order Peptide Nanotubes.

The controlled peptide self-assembly and disassembly are not only implicated in many cellular processes but also possess huge application potential in a wide range of biotechnology and biomedicine. ß-sheet peptide assemblies possess high kinetic stability, so it is usually hard to disassemble them rapidly. Here, we reported that both the self-assembly and disassembly of a designed short ß-sheet peptide IIIGGHK could be well harnessed through the variations of concentration, pH, and mechanical stirring. Microscopic imaging, neutron scattering, and infrared spectroscopy were used to track the assembly and disassembly processes upon these stimuli, especially the interconversion between thin, left-handed protofibrils and higher-order nanotubes with superstructural right-handedness. The underlying rationale for these controlled disassembly processes mainly lies in the fact that the specific His-His interactions between protofibrils were responsive to these stimuli. By taking advantage of the peptide self-assembly and disassembly, the encapsulation of the hydrophobic drug curcumin and its rapid release upon stimuli were achieved. Additionally, the peptide hydrogels facilitated the differentiation of neural cells while maintaining low cell cytotoxicity. We believe that such dynamic and reversible structural transformation in this work provides a distinctive paradigm for controlling the peptide self-assembly and disassembly, thus laying a foundation for practical applications of peptide assemblies.

BilR is a gut microbial enzyme that reduces bilirubin to urobilinogen.

Metabolism of haem by-products such as bilirubin by humans and their gut microbiota is essential to human health, as excess serum bilirubin can cause jaundice and even neurological damage. The bacterial enzymes that reduce bilirubin to urobilinogen, a key step in this pathway, have remained unidentified. Here we used biochemical analyses and comparative genomics to identify BilR as a gut-microbiota-derived bilirubin reductase that reduces bilirubin to urobilinogen. We delineated the BilR sequences from similar reductases through the identification of key residues critical for bilirubin reduction and found that BilR is predominantly encoded by Firmicutes species. Analysis of human gut metagenomes revealed that BilR is nearly ubiquitous in healthy adults, but prevalence is decreased in neonates and individuals with inflammatory bowel disease. This discovery sheds light on the role of the gut microbiome in bilirubin metabolism and highlights the significance of the gut-liver axis in maintaining bilirubin homeostasis.

Deep learning combining mammography and ultrasound images to predict the malignancy of BI-RADS US 4A lesions in women with dense breasts: a diagnostic study.

OBJECTIVES: The authors aimed to assess the performance of a deep learning (DL) model, based on a combination of ultrasound (US) and mammography (MG) images, for predicting malignancy in breast lesions categorized as Breast Imaging Reporting and Data System (BI-RADS) US 4A in diagnostic patients with dense breasts. METHODS: A total of 992 patients were randomly allocated into the training cohort and the test cohort at a proportion of 4:1. Another, 218 patients were enrolled to form a prospective validation cohort. The DL model was developed by incorporating both US and MG images. The predictive performance of the combined DL model for malignancy was evaluated by sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The combined DL model was then compared to a clinical nomogram model and to the DL model trained using US image only and to that trained MG image only. RESULTS: The combined DL model showed satisfactory diagnostic performance for predicting malignancy in breast lesions, with an AUC of 0.940 (95% CI: 0.874-1.000) in the test cohort, and an AUC of 0.906 (95% CI: 0.817-0.995) in the validation cohort, which was significantly higher than the clinical nomogram model, and the DL model for US or MG alone ( P <0.05). CONCLUSIONS: The study developed an objective DL model combining both US and MG imaging features, which was proven to be more accurate for predicting malignancy in the BI-RADS US 4A breast lesions of patients with dense breasts. This model may then be used to more accurately guide clinicians' choices about whether performing biopsies in breast cancer diagnosis.

Applications of de Bruijn graphs in microbiome research.

High-throughput sequencing has become an increasingly central component of microbiome research. The development of de Bruijn graph-based methods for assembling high-throughput sequencing data has been an important part of the broader adoption of sequencing as part of biological studies. Recent advances in the construction and representation of de Bruijn graphs have led to new approaches that utilize the de Bruijn graph data structure to aid in different biological analyses. One type of application of these methods has been in alternative approaches to the assembly of sequencing data like gene-targeted assembly, where only gene sequences are assembled out of larger metagenomes, and differential assembly, where sequences that are differentially present between two samples are assembled. de Bruijn graphs have also been applied for comparative genomics where they can be used to represent large sets of multiple genomes or metagenomes where structural features in the graphs can be used to identify variants, indels, and homologous regions in sequences. These de Bruijn graph-based representations of sequencing data have even begun to be applied to whole sequencing databases for large-scale searches and experiment discovery. de Bruijn graphs have played a central role in how high-throughput sequencing data is worked with, and the rapid development of new tools that rely on these data structures suggests that they will continue to play an important role in biology in the future.