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Cross-Kingdom Siderophores: Biosynthesis, Ecology, and Biotechnological Applications.

Microbial siderophores are high-affinity iron-binding compounds which are produced by bacteria, fungi, and actinomycetes to obtain iron and survive and interact with different species in an iron-deficient environment. While the conventional research on siderophore systems deals mainly with the study within the same taxa, modern researchers have increased their inclination toward cross-kingdom integration of siderophore behavior and their impact on host-associated environments. This can be largely attributed to differences in biosynthetic gene clusters, receptor systems, and regulatory networks, which produce distinct genotype-to-phenotype results determining microbial cooperation and competition. Current advancements in genomic research, together with omics studies like transcriptomics, proteomics, and metabolomics, have created newer insights into how siderophores function. However, the present literature evidences multiple major gaps in multi-omics data because the link between genomes and metabolomes remains weak due to inconsistent regulatory data sets and failure in identifying producer-consumer relationships in polymicrobial systems. Additionally, major constraints like molecular instability, delivery system limitations, host toxicity, limitations in upscaling, and regulatory issues delimit the use of siderophores in medical treatment, agricultural practices, and environmental biotechnology. This review aims to bridge the existing knowledge about siderophore biochemistry, biosynthesis, ecological functions, and genetic regulation across kingdoms while integrating multi-omics outlook with translational considerations. Thus, by connecting molecular mechanisms with evolutionary cross-talk, this study aims to provide a system-level framework in the world of siderophore-mediated iron uptake and therefore shapes future directions in emerging fields of microbial engineering, precision therapies, and sustainable biotechnology.

Fur regulation↗

Hyperstructures, genome analysis and I-cells.

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.

Algorithms↗

Systems Analysis Reveals Contraceptive-Induced Alteration of Cervicovaginal Gene Expression in a Randomized Trial.

Hormonal contraceptives (HCs) are vital in managing the reproductive health of women. However, HC usage has been linked to perturbations in cervicovaginal immunity and increased risk of sexually transmitted infections. Here, we evaluated the impact of three HCs on the cervicovaginal environment using high-throughput transcriptomics. From 2015 to 2017, 130 adolescent females aged 15-19 years were enrolled into a substudy of UChoose, a single-site, open-label randomized, crossover trial (NCT02404038) and randomized to injectable norethisterone-enanthate (Net-En), combined oral contraceptives (COC), or etonorgesterol/ethinyl-estradiol-combined contraceptive vaginal ring (CCVR). Cervicovaginal samples were collected after 16 weeks of randomized HC use and analyzed by RNA-Seq, 16S rRNA gene sequencing, and Luminex analysis. Participants in the CCVR arm had a significant elevation of transcriptional networks driven by IL-6, IL-1, and NFKB, and lower expression of genes supporting epithelial barrier integrity. An integrated multivariate analysis demonstrated that networks of microbial dysbiosis and inflammation best discriminated the CCVR arm from the other contraceptive groups, while genes involved in epithelial cell differentiation were predictive of the Net-En and COC arms. Collectively, these data from a randomized trial represent the most comprehensive "omics" analyses of the cervicovaginal response to HCs and provide important mechanistic guidelines for the provision of HCs in sub-Saharan Africa.

HIV↗

Systematic analysis of snRNA genes reveals frequent RNU2-2 variants in dominant and recessive developmental and epileptic encephalopathies.

Variants in spliceosomal small nuclear RNA (snRNA) genes RNU4-2 (ReNU syndrome), RNU5B-1, and RNU2-2 have recently been linked to dominant neurodevelopmental disorders (NDDs), revealing a major, previously overlooked role for noncoding snRNAs in human disease. Here, we systematically analysed 200 potentially functional snRNA genes in a French cohort comprising 26,911 individuals with rare disorders and through international collaborations. We identify de novo and biallelic variants in RNU2-2 associated with both dominant and recessive NDDs in 126 individuals from 108 unrelated families. Recessive RNU2-2 NDD is at least twice as frequent as the dominant NDD caused by n.4G>A and n.35A>G, and often arises from a de novo variant in trans with an inherited allele, reflecting the high mutability of snRNA genes. Dominant and recessive RNU2-2-NDDs share overlapping clinical features with frequent epilepsy. Blood transcriptomics and DNA methylation analyses revealed subtle, variant-specific effects on splicing and episignatures. Our findings support a gradient-of-impact model and a continuum between dominant and recessive inheritance, establishing RNU2-2 variants as a frequent cause of NDDs, nearly as prevalent as ReNU syndrome.

Journal Article↗

LymphGen-Sig: Integrating Genetic and Transcriptional States to Predict Therapeutic Response in Diffuse Large B-Cell Lymphoma.

PURPOSE: Genetic classification may advance precision medicine in diffuse large B-cell lymphoma (DLBCL), but existing tools like LymphGen (LG) are limited by complexity and incomplete classification and do not incorporate nongenetic features that affect disease biology and therapeutic outcomes. To address these limitations, we developed LG-sig (LGsig), a gene expression-based platform that classifies all DLBCLs and harmonizes both genetic and nongenetic dimensions of the disease. METHODS: LGsig was built on the distinct subtype-specific gene expression signature of each LG class using paired genomic and transcriptomic data (National Cancer Institute/British Columbia Cancer Agency; N = 764). Model development was restricted to DLBCLs classified into MYD88L265P and CD79B mutations (MCD), BCL6 translocation and NOTCH2 mutations (BN2), EZH2 mutations and BCL2 translocation (EZB), or SGK1 and TET2 mutations (ST2). Gene features were selected by differential gene expression, with 294 genes being optimal for classification using a nearest shrunken centroid classifier. LGsig classifications were designated as MCDsig, BN2sig, ST2sig, and EZBsig. The final model was applied to RNAseq from archival samples from the POLARIX trial (N = 678) to assess outcomes after polatuzumab vedotin-R-CHP (pola-R-CHP) or rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) for each LGsig subtype. RESULTS: LGsig accurately identified LG subtypes using transcriptional data alone and extended assignments to all previously LG-unclassified cases. Importantly, LG-unclassified DLBCLs reassigned by LGsig mirrored the transcriptional and clinical features of their corresponding LG counterparts, supporting their reclassification. In addition, LGsig reassigned LG A53 DLBCLs, characterized by aneuploidy and TP53 alterations, into more biologically and therapeutically relevant LGsig clusters. Finally, LGsig improved the performance of LG as a biomarker in the POLARIX study, by identifying distinct DLBCL subtypes exhibiting a survival benefit with pola-R-CHP over R-CHOP in both LG-classified and LG-unclassified cases. CONCLUSION: LGsig expands molecular classification beyond current genetic classifiers in DLBCL by integrating both genetic and transcriptional dimensions of the disease to better inform subtype-specific therapeutic strategies.

Journal Article↗