Recently Confirmed Apoptosis-Inducing Lead Compounds Isolated from Marine Sponge of Potential Relevance in Cancer Treatment

Despite intense efforts to develop non-cytotoxic anticancer treatments, effective agents are still not available. Therefore, novel apoptosis-inducing drug leads that may be developed into effective targeted cancer therapies are of interest to the cancer research community. Targeted cancer therapies affect specific aberrant apoptotic pathways that characterize different cancer types and, for this reason, it is a more desirable type of therapy than chemotherapy or radiotherapy, as it is less harmful to normal cells. In this regard, marine sponge derived metabolites that induce apoptosis continue to be a promising source of new drug leads for cancer treatments. A PubMed query from 01/01/2005 to 31/01/2011 combined with hand-curation of the retrieved articles allowed for the identification of 39 recently confirmed apoptosis-inducing anticancer lead compounds isolated from the marine sponge that are selectively discussed in this review

Soil and Rhizosphere Associated Fungi in Gray Mangroves (Avicennia marina) from the Red Sea — A Metagenomic Approach

Covering a quarter of the world’s tropical coastlines and being one of the most threatened ecosystems, mangroves are among the major sources of terrestrial organic matter to oceans and harbor a wide microbial diversity. In order to protect, restore, and better understand these ecosystems, researchers have extensively studied their microbiology, yet few surveys have focused on their fungal communities. Our lack of knowledge is even more pronounced for specific fungal populations, such as the ones associated with the rhizosphere. Likewise, the Red Sea gray mangroves (Avicennia marina) remain poorly characterized, and understanding of their fungal communities still relies on cultivation-dependent methods. In this study, we analyzed metagenomic datasets from gray mangrove rhizosphere and bulk soil samples collected in the Red Sea coast, to obtain a snapshot of their fungal communities. Our data indicated that Ascomycota was the dominant phylum (76%–85%), while Basidiomycota was less abundant (14%–24%), yet present in higher numbers than usually reported for such environments. Fungal communities were more stable within the rhizosphere than within the bulk soil, both at class and genus level. This finding is consistent with the intrinsic patchiness in soil sediments and with the selection of specific microbial communities by plant roots. Our study indicates the presence of several species on this mycobiome that were not previously reported as mangrove-associated. In particular, we detected representatives of several commercially-used fungi, e.g., producers of secreted cellulases and anaerobic producers of cellulosomes. These results represent additional insights into the fungal community of the gray mangroves of the Red Sea, and show that they are significantly richer than previously reported

Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea

AbstractMangroves are unique, and endangered, coastal ecosystems that play a vital role in the tropical and subtropical environments. A comprehensive description of the microbial communities in these ecosystems is currently lacking, and additional studies are required to have a complete understanding of the functioning and resilience of mangroves worldwide.In this work, we carried out a metagenomic study by comparing the microbial community of mangrove sediment with the rhizosphere microbiome of Avicennia marina, in northern Red Sea mangroves, along the coast of Saudi Arabia. Our results revealed that rhizosphere samples presented similar profiles at the taxonomic and functional levels and differentiated from the microbiome of bulk soil controls. Overall, samples showed predominance by Proteobacteria, Bacteroidetes and Firmicutes, with high abundance of sulfate reducers and methanogens, although specific groups were selectively enriched in the rhizosphere. Functional analysis showed significant enrichment in ‘metabolism of aromatic compounds’, ‘mobile genetic elements’, ‘potassium metabolism’ and ‘pathways that utilize osmolytes’ in the rhizosphere microbiomes.To our knowledge, this is the first metagenomic study on the microbiome of mangroves in the Red Sea, and the first application of unbiased 454-pyrosequencing to study the rhizosphere microbiome associated with A. marina. Our results provide the first insights into the range of functions and microbial diversity in the rhizosphere and soil sediments of gray mangrove (A. marina) in the Red Sea

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Mu-Like Prophage Strong Gyrase Site Sequences: Analysis of Properties Required for Promoting Efficient Mu DNA Replication

The bacteriophage Mu genome contains a centrally located strong gyrase site (SGS) that is required for efficient prophage replication. To aid in studying the unusual properties of the SGS, we sought other gyrase sites that might be able to substitute for the SGS in Mu replication. Five candidate sites were obtained by PCR from Mu-like prophage sequences present in Escherichia coli O157:H7 Sakai, Haemophilus influenzae Rd, Salmonella enterica serovar Typhi CT18, and two strains of Neisseria meningitidis. Each of the sites was used to replace the natural Mu SGS to form recombinant prophages, and the effects on Mu replication and host lysis were determined. The site from the E. coli prophage supported markedly enhanced replication and host lysis over that observed with a Mu derivative lacking the SGS, those from the N. meningitidis prophages allowed a small enhancement, and the sites from the Haemophilus and Salmonella prophages gave none. Each of the candidate sites was cleaved specifically by E. coli DNA gyrase both in vitro and in vivo. Supercoiling assays performed in vitro, with the five sites or the Mu SGS individually cloned into a pUC19 reporter plasmid, showed that the Mu SGS and the E. coli or N. meningitidis sequences allowed an enhancement of processive, gyrase-dependent supercoiling, whereas the H. influenzae or Salmonella serovar Typhi sequences did not. While consistent with a requirement for enhanced processivity of supercoiling for a site to function in Mu replication, these data suggest that other factors are also important. The relevance of these observations to an understanding of the function of the SGS is discussed

Workflow of Analysis Pipeline.

<p>Given a set of binding proteins and depending on the model type, the analysis pipeline predicts the functional identity of one of a TF binding partner. After a pair of binding proteins is given as the input, the pipeline calculates numerous amino acid physico-chemical properties for the pair and generates a matrix of feature vectors. This matrix of feature vectors is submitted to the Random Forest classifier that returns the predicted functional class assignment and the associated confidence score. The latter is a measure of how strongly the average feature-class probabilities of all decision trees pointed to each class based on the information gain of those decision trees.</p