Changing Images of the Gene

The Next Generation of Drug Delivery: Harnessing the Power of Bacteriophages

The use of biomaterials, such as bacteriophages, as drug delivery vehicles (DDVs) has gained increasing interest in recent years due to their potential to address the limitations of conventional drug delivery systems. Bacteriophages offer several advantages as drug carriers, such as high specificity for targeting bacterial cells, low toxicity, and the ability to be engineered to express specific proteins or peptides for enhanced targeting and drug delivery. In addition, bacteriophages have been shown to reduce the development of antibiotic resistance, which is a major concern in the field of antimicrobial therapy. Many initiatives have been taken to take up various payloads selectively and precisely by surface functionalization of the outside or interior of self-assembling viral protein capsids. Bacteriophages have emerged as a promising platform for the targeted delivery of therapeutic agents, including drugs, genes, and imaging agents. They possess several properties that make them attractive as drug delivery vehicles, including their ability to specifically target bacterial cells, their structural diversity, their ease of genetic manipulation, and their biocompatibility. Despite the potential advantages of using bacteriophages as drug carriers, several challenges and limitations need to be addressed. One of the main challenges is the limited host range of bacteriophages, which restricts their use to specific bacterial strains. However, this can also be considered as an advantage, as it allows for precise and targeted drug delivery to the desired bacterial cells. The use of biomaterials, including bacteriophages, as drug delivery vehicles has shown promising potential to address the limitations of conventional drug delivery systems. Further research is needed to fully understand the potential of these biomaterials and address the challenges and limitations associated with their use.

Systems biology: the next frontier for bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from 'omics platform technologies, in particular "downstream" technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.

The elusive concept of the gene

In recent years geneticists have witnessed many significant observations which have seriously shaken the traditional concept of the gene. These specifically include the facts that (1) the boundaries of transcriptional units are far from clear; in fact, whole chromosomes if not the whole genome seem to be continuums of genetic transcription, (2) many examples of gene fusion are known, (3) likewise many examples of so-called encrypted genes are known in the organelle genomes of microbial eukaryotes and in prokaryotes, and (4) in addition to the structure of the gene, its functional status can also be inheritable, and, further, (5) epigenetic extra-genomic modes of inheritance, called genetic restoration, seem to be a rather common phenomenon, meaning that organisms can sometimes rewrite their DNA on the basis of RNA messages inherited from generations past. I will briefly review these observations and discuss the difficulties of defining the gene, and then formulate a new view, which is called the relational or systemic concept of the gene. It has to be noted that genes assume their information content characteristics in the Shannonian sense as nucleotide sequences of DNA (or RNA). However, on the basis of this we cannot say anything about their information content in the semantic sense. The semantic information content of genes is context-dependent. Genes namely assume their biochemical characteristics usually only within living cells, their developmental characteristics only within living organisms, and their evolutionary characteristics only within populations of living organisms.

MicroRNomics: a newly emerging approach for disease biology

Genomic evidence reveals that gene expression in humans is precisely controlled in cellular, tissue-type, temporal, and condition-specific manners. Completely understanding the regulatory mechanisms of gene expression is therefore one of the most important issues in genomic medicine. Surprisingly, recent analyses of the human and animal genomes have demonstrated that the majority of RNA transcripts are relatively small, noncoding RNAs (sncRNAs), rather than large, protein coding message RNAs (mRNAs). Moreover, these sncRNAs may represent a novel important layer of regulation for gene expression. The most important breakthrough in this new area is the discovery of microRNAs (miRNAs). miRNAs comprise a novel class of endogenous, small, noncoding RNAs that negatively regulate gene expression via degradation or translational inhibition of their target mRNAs. As a group, miRNAs may directly regulate approximately 30% of the genes in the human genome. In keeping with the nomenclature of RNomics, which is to study sncRNAs on the genomic scale, "microRNomics" is coined here to describe a novel subdiscipline of genomics that studies the identification, expression, biogenesis, structure, regulation of expression, targets, and biological functions of miRNAs on the genomic scale. A growing body of exciting evidence suggests that miRNAs are important regulators of cell differentiation, proliferation/growth, mobility, and apoptosis. These miRNAs therefore play important roles in development and physiology. Consequently, dysregulation of miRNA function may lead to human diseases such as cancer, cardiovascular disease, liver disease, immune dysfunction, and metabolic disorders. microRNomics may be a newly emerging approach for human disease biology.

A model of globin evolution

Putative globins have been identified in 426 bacterial, 32 Archaeal and 67 eukaryote genomes. Among these sequences are the hitherto unsuspected presence of single domain sensor globins within Bacteria, Fungi, and a Euryarchaeote. Bayesian phylogenetic trees suggest that their occurrence in the latter two groups could be the result of lateral gene transfer from Bacteria. Iterated psiblast searches based on groups of globin sequences indicate that bacterial flavohemoglobins are closer to metazoan globins than to the other two lineages, the 2-over-2 globins and the globin-coupled sensors. Since Bacteria is the only kingdom to have all the subgroups of the three globin lineages, we propose a working model of globin evolution based on the assumption that all three lineages originated and evolved only in Bacteria. Although the 2-over-2 globins and the globin-coupled sensors recognize flavohemoglobins, there is little recognition between them. Thus, in the first stage of globin evolution, we favor a flavohemoglobin-like single domain protein as the ancestral globin. The next stage comprised the splitting off to single domain 2-over-2 and sensor-like globins, followed by the covalent addition of C-terminal domains resulting in the chimeric flavohemoglobins and globin-coupled sensors. The last stage encompassed the lateral gene transfers of some members of the three globin lineages to specific groups of Archaea and Eukaryotes.