Bacteria and bacteriophage consortia are associated with protective intestinal metabolites in patients receiving stem cell transplantation.

The microbiome is a predictor of clinical outcome in patients receiving allogeneic hematopoietic stem cell transplantation (allo-SCT). Microbiota-derived metabolites can modulate these outcomes. How bacteria, fungi and viruses contribute to the production of intestinal metabolites is still unclear. We combined amplicon sequencing, viral metagenomics and targeted metabolomics from stool samples of patients receiving allo-SCT (n = 78) and uncovered a microbiome signature of Lachnospiraceae and Oscillospiraceae and their associated bacteriophages, correlating with the production of immunomodulatory metabolites (IMMs). Moreover, we established the IMM risk index (IMM-RI), which was associated with improved survival and reduced relapse. A high abundance of short-chain fatty acid-biosynthesis pathways, specifically butyric acid via butyryl-coenzyme A (CoA):acetate CoA-transferase (BCoAT, which catalyzes EC 2.8.3.8) was detected in IMM-RI low-risk patients, and virome genome assembly identified two bacteriophages encoding BCoAT as an auxiliary metabolic gene. In conclusion, our study identifies a microbiome signature associated with protective IMMs and provides a rationale for considering metabolite-producing consortia and metabolite formulations as microbiome-based therapies.

Transient Mild Photothermia Improves Therapeutic Performance of Oral Nanomedicines with Enhanced Accumulation in the Colitis Mucosa.

The treatment outcomes of oral medications against ulcerative colitis (UC) have long been restricted by low drug accumulation in the colitis mucosa and subsequent unsatisfactory therapeutic efficacy. Here, high-performance pluronic F127 (P127)-modified gold shell (AuS)-polymeric core nanotherapeutics loading with curcumin (CUR) is constructed. Under near-infrared irradiation, the resultant P127-AuS@CURs generate transient mild photothermia (TMP; ≈42 °C, 10 min), which facilitates their penetration through colonic mucus and favors multiple cellular processes, including cell internalization, lysosomal escape, and controlled CUR release. This strategy relieves intracellular oxidative stress, improves wound healing, and reduces immune responses by polarizing the proinflammatory M1-type macrophages to the anti-inflammatory M2-type. Upon oral administration of hydrogel-encapsulating P127-AuS@CURs plus intestinal intralumen TMP, their therapeutic effects against acute and chronic UC are demonstrated to be superior to those of a widely used clinical drug, dexamethasone. The treatment of P127-AuS@CURs (+ TMP) elevates the proportions of beneficial bacteria (e.g., Lactobacillus and Lachnospiraceae), whose metabolites can also mitigate colitis symptoms by regulating genes associated with antioxidation, anti-inflammation, and wound healing. Overall, the intestinal intralumen TMP offers a promising approach to enhance the therapeutic outcomes of noninvasive medicines against UC.

Gut virome profiling identifies an association between temperate phages and colorectal cancer promoted by Helicobacter pylori infection.

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. While a close correlation between chronic Helicobacter pylori infection and CRC has been reported, the role of the virome has been overlooked. Here, we infected Apc-mutant mouse models and C57BL/6 mice with H. pylori and conducted a comprehensive metagenomics analysis of H. pylori-induced changes in lower gastrointestinal tract bacterial and viral communities. We observed an expansion of temperate phages in H. pylori infected Apc+/1638N mice at the early stage of carcinogenesis. Some of the temperate phages were predicted to infect bacteria associated with CRC, including Enterococcus faecalis. We also observed a high prevalence of virulent genes, such as flgJ, cwlJ, and sleB, encoded by temperate phages. In addition, we identified phages associated with pre-onset and onset of H. pylori-promoted carcinogenesis. Through co-occurrence network analysis, we found strong associations between the viral and bacterial communities in infected mice before the onset of carcinogenesis. These findings suggest that the expansion of temperate phages, possibly caused by prophage induction triggered by H. pylori infection, may have contributed to the development of CRC in mice by interacting with the bacterial community.

Targeted Therapy for EWS-FLI1 in Ewing Sarcoma.

Ewing sarcoma (EwS) is a rare and predominantly pediatric malignancy of bone and soft tissue in children and adolescents. Although international collaborations have greatly improved the prognosis of most EwS, the occurrence of macrometastases or relapse remains challenging. The prototypic oncogene EWS-FLI1 acts as an aberrant transcription factor that drives the cellular transformation of EwS. In addition to its involvement in RNA splicing and the DNA damage response, this chimeric protein directly binds to GGAA repeats, thereby modifying the transcriptional profile of EwS. Direct pharmacological targeting of EWS-FLI1 is difficult because of its intrinsically disordered structure. However, targeting the EWS-FLI1 protein complex or downstream pathways provides additional therapeutic options. This review describes the EWS-FLI1 protein partners and downstream pathways, as well as the related target therapies for the treatment of EwS.

Unveiling the hidden role of aquatic viruses in hydrocarbon pollution bioremediation.

Hydrocarbon pollution poses substantial environmental risks to water and soil. Bioremediation, which utilizes microorganisms to manage pollutants, offers a cost-effective solution. However, the role of viruses, particularly bacteriophages (phages), in bioremediation remains unexplored. This study examines the diversity and activity of hydrocarbon-degradation genes encoded by environmental viruses, focusing on phages, within public databases. We identified 57 high-quality phage-encoded auxiliary metabolic genes (AMGs) related to hydrocarbon degradation, which we refer to as virus-encoded hydrocarbon degradation genes (vHYDEGs). These genes are encoded by taxonomically diverse aquatic phages and highlight the under-characterized global virosphere. Six protein families involved in the initial alkane hydroxylation steps were identified. Phylogenetic analyses revealed the diverse evolutionary trajectories of vHYDEGs across habitats, revealing previously unknown biodegraders linked evolutionarily with vHYDEGs. Our findings suggest phage AMGs may contribute to alkane and aromatic hydrocarbon degradation, participating in the initial, rate-limiting hydroxylation steps, thereby aiding hydrocarbon pollution bioremediation and promoting their propagation. To support future research, we developed vHyDeg, a database containing identified vHYDEGs with comprehensive annotations, facilitating the screening of hydrocarbon degradation AMGs and encouraging their bioremediation applications.

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning.

Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

ViroProfiler: a containerized bioinformatics pipeline for viral metagenomic data analysis.

Bacteriophages play central roles in the maintenance and function of most ecosystems by regulating bacterial communities. Yet, our understanding of their diversity remains limited due to the lack of robust bioinformatics standards. Here we present ViroProfiler, an in-silico workflow for analyzing shotgun viral metagenomic data. ViroProfiler can be executed on a local Linux computer or cloud computing environments. It uses the containerization technique to ensure computational reproducibility and facilitate collaborative research. ViroProfiler is freely available at https://github.com/deng-lab/viroprofiler.

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning.

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

Toxic/Bioactive Peptide Synthesis Genes Rearranged by Insertion Sequence Elements Among the Bloom-Forming Cyanobacteria Planktothrix.

It has been generally hypothesized that mobile elements can induce genomic rearrangements and influence the distribution and functionality of toxic/bioactive peptide synthesis pathways in microbes. In this study, we performed in depth genomic analysis by completing the genomes of 13 phylogenetically diverse strains of the bloom-forming freshwater cyanobacteria Planktothrix spp. to investigate the role of insertion sequence (IS) elements in seven pathways. Chromosome size varied from 4.7-4.8 Mbp (phylogenetic Lineage 1 of P. agardhii/P. rubescens thriving in shallow waterbodies) to 5.4-5.6 Mbp (Lineage 2 of P. agardhii/P. rubescens thriving in deeper physically stratified lakes and reservoirs) and 6.3-6.6 Mbp (Lineage 3, P. pseudagardhii/P. tepida including planktic and benthic ecotypes). Although the variation in chromosome size was positively related to the proportion of IS elements (1.1-3.7% on chromosome), quantitatively, IS elements and other paralogs only had a minor share in chromosome size variation. Thus, the major part of genomic variation must have resulted from gene loss processes (ancestor of Lineages 1 and 2) and horizontal gene transfer (HGT). Six of seven peptide synthesis gene clusters were found located on the chromosome and occurred already in the ancestor of P. agardhii/P. rubescens, and became partly lost during evolution of Lineage 1. In general, no increased IS element frequency in the vicinity of peptide synthesis gene clusters was observed. We found a higher proportion of IS elements in ten breaking regions related to chromosomal rearrangements and a tendency for colocalization of toxic/bioactive peptide synthesis gene clusters on the chromosome.

Differences in Gut Virome Related to Barrett Esophagus and Esophageal Adenocarcinoma.

The relationship between viruses (dominated by bacteriophages or phages) and lower gastrointestinal (GI) tract diseases has been investigated, whereas the relationship between gut bacteriophages and upper GI tract diseases, such as esophageal diseases, which mainly include Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC), remains poorly described. This study aimed to reveal the gut bacteriophage community and their behavior in the progression of esophageal diseases. In total, we analyzed the gut phage community of sixteen samples from patients with esophageal diseases (six BE patients and four EAC patients) as well as six healthy controls. Differences were found in the community composition of abundant and rare bacteriophages among three groups. In addition, the auxiliary metabolic genes (AMGs) related to bacterial exotoxin and virulence factors such as lipopolysaccharides (LPS) biosynthesis proteins were found to be more abundant in the genome of rare phages from BE and EAC samples compared to the controls. These results suggest that the community composition of gut phages and functional traits encoded by them were different in two stages of esophageal diseases. However, the findings from this study need to be validated with larger sample sizes in the future.

Challenges of Studying the Human Virome - Relevant Emerging Technologies.

In this review we provide an overview of current challenges and advances in bacteriophage research within the growing field of viromics. In particular, we discuss, from a human virome study perspective, the current and emerging technologies available, their limitations in terms of de novo discoveries, and possible solutions to overcome present experimental and computational biases associated with low abundance of viral DNA or RNA. We summarize recent breakthroughs in metagenomics assembling tools and single-cell analysis, which have the potential to increase our understanding of phage biology, diversity, and interactions with both the microbial community and the human body. We expect that these recent and future advances in the field of viromics will have a strong impact on how we develop phage-based therapeutic approaches.

Bacteriophages Isolated from Stunted Children Can Regulate Gut Bacterial Communities in an Age-Specific Manner.

Stunting, a severe and multigenerational growth impairment, globally affects 22% of children under the age of 5 years. Stunted children have altered gut bacterial communities with higher proportions of Proteobacteria, a phylum with several known human pathogens. Despite the links between an altered gut microbiota and stunting, the role of bacteriophages, highly abundant bacterial viruses, is unknown. Here, we describe the gut bacterial and bacteriophage communities of Bangladeshi stunted children younger than 38 months. We show that these children harbor distinct gut bacteriophages relative to their non-stunted counterparts. In vitro, these gut bacteriophages are infectious and can regulate bacterial abundance and composition in an age-specific manner, highlighting their possible role in the pathophysiology of child stunting. Specifically, Proteobacteria from non-stunted children increased in the presence of phages from younger stunted children, suggesting that phages could contribute to the bacterial community changes observed in child stunting.

Temperature Response of Planktonic Microbiota in Remote Alpine Lakes.

Alpine lakes are considered pristine freshwater ecosystems and sensitive to direct and indirect changes in water temperature as induced by climate change. The bacterial plankton constitutes a key component in the water column and bacterial metabolic activity has direct consequences for water quality. In order to understand bacterial response to global temperature rise in five alpine lakes located in the Austrian Alps (1700-2188 m a.S.L.) water temperature was compared within a decadal period. Depth-integrated samples were characterized in community composition by 16S rDNA deep-amplicon sequencing early [56 ± 16 (SD) days after ice break up] and later (88 ± 16 days) in the growing season. Within the 10 years period, temperature rise was observed through reduced ice cover duration and increased average water temperature. During the early growing season, the average water temperature recorded between circulation in spring until sampling date (WAS), and the day of autumn circulation, as well as chemical composition including dissolved organic carbon influenced bacterial community composition. In contrast, only nutrients (such as nitrate) were found influential later in the growing season. Metabolic theory of ecology (MTE) was applied to explain the dependence of taxonomic richness on WAS in mathematical terms. The calculated activation energy exceeded the frequently reported prediction emphasizing the role of WAS during early growing season. Accordingly, the relative abundance of predicted metabolism related genes increased with WAS. Thus, the dominant influence of temperature after ice break up could be explained by overall climate change effects, such as a more intense warming in spring and an overall higher amplitude of temperature variation.

Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments.

The world is facing a hard battle against soil pollution such as heavy metals. Metagenome sequencing, 16S rRNA sequencing, and quantitative polymerase chain reaction (qPCR) were used to examine microbial adaptation mechanism to contaminated sediments under natural conditions. Results showed that sediment from a tributary of the Yellow River, which was named Dongdagou River (DDG) supported less bacterial biomass and owned lower richness than sediment from Maqu (MQ), an uncontaminated site in the upper reaches of the Yellow River. Additionally, microbiome structures in these two sites were different. Metagenome sequencing and functional gene annotations revealed that sediment from DDG contains a larger number of genes related to DNA recombination, DNA damage repair, and heavy-metal resistance. KEGG pathway analysis indicated that the sediment of DDG contains a greater number of enzymes associated with heavy-metal resistance and reduction. Additionally, the bacterial phyla Proteobacteria, Bacteroidetes, and Firmicutes, which harbored a larger suite of metal-resistance genes, were found to be the core functional phyla in the contaminated sediments. Furthermore, sediment in DDG owned higher viral abundance, indicating virus-mediated heavy-metal resistance gene transfer might be an adaptation mechanism. In conclusion, microbiome of sediment from DDG has evolved into an integrated system resistant to long-term heavy-metal pollution.

Large-scale Direct Targeting for Drug Repositioning and Discovery.

A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug's affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

Large-scale exploration and analysis of drug combinations.

MOTIVATION: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. RESULTS: We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. AVAILABILITY AND IMPLEMENTATION: The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

Insights from systems pharmacology into cardiovascular drug discovery and therapy.

BACKGROUND: Given the complex nature of cardiovascular disease (CVD), information derived from a systems-level will allow us to fully interrogate features of CVD to better understand disease pathogenesis and to identify new drug targets. RESULTS: Here, we describe a systematic assessment of the multi-layer interactions underlying cardiovascular drugs, targets, genes and disorders to reveal comprehensive insights into cardiovascular systems biology and pharmacology. We have identified 206 effect-mediating drug targets, which are modulated by 254 unique drugs, of which, 43% display activities across different protein families (sequence similarity < 30%), highlighting the fact that multitarget therapy is suitable for CVD. Although there is little overlap between cardiovascular protein targets and disease genes, the two groups have similar pleiotropy and intimate relationships in the human disease gene-gene and cellular networks, supporting their similar characteristics in disease development and response to therapy. We also characterize the relationships between different cardiovascular disorders, which reveal that they share more etiological commonalities with each other rooted in the global disease-disease networks. Furthermore, the disease modular analysis demonstrates apparent molecular connection between 227 cardiovascular disease pairs. CONCLUSIONS: All these provide important consensus as to the cause, prevention, and treatment of various CVD disorders from systems-level perspective.