Numerous experimental research studies have demonstrated that our DNA can be affected by waves from all spectrums – acoustic, electromagnetic and scalar waves can alter its genetic code and lead to mutations, leading to new scientific advances known as wave genome research. These discoveries have given rise to an entirely new field known as wave genomics.
DNA’s structure resembles that of a solenoid or coil; the movement of electrical charges creates magnetic fields which radiate electromagnetic waves into space.
Activation of genes
Wave genome has emerged from quantum biology research. Scientists have discovered that DNA can be affected by acoustic, electromagnetic and scalar waves which influence its structure, producing new genetic information in its wake. This theory may have important ramifications for developing medicines and medical procedures and gene therapy approaches in general – perhaps even gene therapies themselves in future using waves as therapy agents.
Recent discoveries of waves affecting DNA molecules has inspired scientists to create techniques for imaging their shape and function. One such technique involves measuring changes in conformation as a function of frequency – changes caused by electromagnetic and acoustic fields which alter double helix vibrations; scientists believe these waves can be detected using detectors which produce voltage proportional to DNA vibration levels.
Linkage disequilibrium (LD) may amplify a transient wave of advancement for neutral genes closely linked with selective genes; however, recombination gradually breaks down their genetic hitchhiking effects along frontal waves of advantageous genes, leading to their frequency decreasing subsequently and eventually losing ground to more advantageous variants.
Neutral genes may spread faster when close to selective genes; however, their relationship can be complicated as mating systems may impede or encourage the spread of advantageous genes.
Recent studies have demonstrated that depletion of Rad21 can significantly disrupt nuclear organization during zygotic gene activation (ZGA) and delay its process, suggesting it’s necessary for initiating RNAPII foci at ZGA as well as altering embryonic cell conformation. Furthermore, other epigenetic features like chromatin domains and TADs also show delays by depleting Rad21.
These studies reveal the complexity of the Wave Genome‘s role in controlling the spread of beneficial genes is more subtle than previously assumed. Mating systems appear to act as barriers against their spread and may even suppress them if close to neutral marker loci – this result is consistent with predictions that the spread rate of neutral genes depends on how easily linked to select markers.
Chromatin states
A chromatin state model is a mathematical representation of local and global transitions in chromatin structure, providing a framework for interpreting data across disparate organ systems, developmental time points and individual genes (such as transcriptional coverage via microarrays or sequencing). An ideal chromatin state model must fulfill two requirements: it must contain structural features as well as have quantifiable effects on gene expression.
Many approaches have been used to analyze chromatin states, such as DNA accessibility profiling and histone modification profiling. Yet it remains unclear how the various states interact or whether inter-state conversion occurs. One hypothesis suggests that remodeling proteins have different preferences for DNA sequences which influence transition between states; another possibility could involve histone methylation/acetylation occurring between states as an intermediary measure.
Chromatin states are defined by specific patterns of chromatin marks and DNA accessibility. State 1 features low DNA accessibility with an abundance of H3K4me1, H2AZ, CTCF binding sites and TF binding sites which is associated with active promoter chromatin; state 2 features more accessible DNA access with an abundance of H3K36me3, Pol II occupancy and large numbers of acetylation marks, while also often being found associated with repression/heterochromatic regions and typically being richer for H3K27me3.
States 1-29 correspond to intergenic regions, promoters, and genes that have been transcribing. States 40-45 include heterochromatic or repressed regions without histone modifications whereas transcribing states are associated with higher histone modifications and greater DNA accessibility.
Recently, there has been an upsurge of activity in the field of chromatin state discovery and annotation. Numerous methods have been developed for learning chromatin states from genome-wide data, including GATE47, GCHME48, TreeHMM49, DiHMM50 and CMINT51. Furthermore, new software applications for performing analyses like IDEAS52 and Segway-GBR53 were created; their development enabled creation of an accurate yet scalable chromatin state model.
Promoter architecture
Structure of gene promoters has an enormous effect on transcription levels. This is due to chromosome and DNA shapes influencing how and degree electrons are distributed within them, which in turn determines their topology, winding coiling packing pattern as well as how many electrons can be found within an area molecule molecule; and location and type of TF binding site influence these electrons movement – providing valuable data that allows us to calculate genetic potential or predict its function of the chromosome itself.
Conventional wisdom holds that, due to chromatin level control features such as promoter architecture’s lack of predictability, gene expression cannot easily be predicted from knowledge of promoter architecture alone. This fact can be seen through weak correlations between promoter divergence and expression divergence across paralogs within a genome; however, its failure could simply reflect an inability to measure promoter architecture accurately or applying inappropriate parameters.
To test our hypothesis, we compared various variables describing the promoter architecture of genes with known expression profiles. These variables included GC-content in 1kb window around promoter, 3kb region upstream of TSS (GC_big), 20 kbps window surrounding TSS (GC_small) and frequency of CpG sites (CpG). Furthermore, we compared distributions of TF binding sites between proximal promoter regions and distal chromatin for any significant differences.
We used these data to generate a correlogram with eleven variables, showing their correlation with gene expression levels. Our strongest correlations were promoter GC-content and GC_big, both showing close relationship to expression level; isochore GC-content and GC3 had weak relationships, suggesting their influence is limited on transcription units activated. Thus confirming that wave genome theory holds validly; our results prove it by showing how chromosomes can form and store quantum holographic biocomputers.
MiR-430
MiR-430 is associated with the Wave Genome. Its expression and activity occur at Maternal-to-Zygotic Transition (MZT) during zebrafish embryo development; an event during which genomic DNA transforms from chromatin into transcriptionally active genes. Multiple factors regulate MZT such as nuclear architecture proteins Cohesin and CTCF as well as histone modifications as well as Nanog and its homologs Pou5f1 and Sox2.
Genome-wide analyses have revealed that MZT occurs in two phases. In the initial stage, maternal chromatin is reorganized to support new gene transcription; then comes a subsequent stage where genes repress each other through gene repression mediated by nuclear architectural protein Rad21 and histone mark H3K27ac respectively. BRD4 is required for MZT as it depends upon histone modification H3K27ac as well as HDM2, Nanog and Pou5f1::Sox2.
Mapping gene expression at cellular resolution during MZT shows that chromosomes acquire metaphase-like properties during this phase. At this time, nascent RNA and Pol II accumulate near transcribed loci with some genes showing increased accumulation, such as miR-430 locus.
MiR-430 gene contains a TATA box and Nanog and Pou5f1::Sox2 binding sites at its proximal promoter region, so we generated a 650-bp proximal fragment from it and inserted it into heterologous genomic locations as reporter transgenic lines to generate reporter transgenics that were tested using ChIP-seq assay for activity at MZT using Nanog and Sox2 regulatory elements; results demonstrated that miR-430 gene activation by both Nanog and Sox2 regulatory elements, thus validating its multicopy state in wave genome.
Estimating miR-430 copies was performed by comparing BLAST bit scores against six single copy genes with similar proximal promoter sequences, yielding a comma-separated file that included genomic coordinates of these proximal promoters as well as total raw read counts as well as labeled counts for each sample.
