Energy-dependent RNA methylation (5)

By: James V. Kohl | Published on: June 7, 2016

Deadly fungus uses unexpected system to control its virulence


The QSP1 gene codes for a peptide, a short string of amino acids. In an intriguing sidelight, the researchers showed that C. neoformans secretes a precursor to this peptide that matures outside the cell. The fully formed peptide is then imported back into the cell to regulate virulence via its actions on a receptor that is yet to be identified.

My comment: Top down causation links virulence from nutrient energy-dependent quorum sensing in a fungus to nutrient energy-dependent quorum sensing in bacteria and all other living genera via the physiology of reproduction and supercoiled DNA. Virulence is determined by amino acid substitutions.
Energy-dependent RNA methylation and amino acid substitutions can be linked to healthy longevity. Virus-driven energy theft can be linked from energy-dependent replication of viruses to the mutations that cause all pathology.
The molecular mechanisms of RNA methylation that link RNA-directed DNA methylation from the innate immune system to to the species-specific behaviors of mammals via what is currently known about sensing, secreting, and signaling in all cell types of all living genera are now well known to all serious scientists. Apparently, however, the mechanisms are not known to neo-Darwinian theorists and/or skeptics who believe in their pseudoscientific nonsense.
There is probably a need for someone to link RNA methylation from energy-dependent learning and memory to mammalian behavior to show theorists that they have nothing to fall back on.
See for example:  Epigenetics and Genetics of Viral Latency


Epigenetics and Viral Chromatin
DNA in the nucleus must be assembled into some form of nucleoprotein structure to avoid DNA damage signaling and nucleolytic attack.

Conclusion (with my emphasis):

This review is far from comprehensive or complete and yet hopefully shows the enormous complexity of viral latency and its regulation at the genetic and epigenetic levels. This information provides great opportunity for the development of innovative and highly selective therapeutic intervention. As viral latency is responsible for life-long pathogenesis and mortality risk, the tasks ahead are in sight, but challenges remain.

See also: Structural diversity of supercoiled DNA

Our data provide relative comparisons of supercoiling-dependent twisted, writhed, curved, and kinked conformations and associated base exposure. Each of these structural features may be differentially recognized by the proteins, nucleic acids, and small molecules that modulate DNA metabolic processes.

See also: GAM: a web-service for integrated transcriptional and metabolic network analysis
My comment: This allows you to link ecological variation to energy dependent metabolic networks and genetic networks to ecological adaptation.

See also: MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions

MutaBind evaluates the effects of variations and disease mutations on protein-protein interactions. It predicts if a mutation disrupts an interaction and calculates the change in binding affinity. The structure of a protein-protein complex is required for this method.

My comment: This allows you to link energy dependent from mutations to all pathology.
See also: HbVar: A Database of Human Hemoglobin Variants and Thalassemias
My comment: This will help you differentiate between the definition of “mutation” and facts about RNA methylation and amino acid substitutions that differentiate more than 1180 hemoglobin variants.
Compare what is currently known to all serious scientists to the inferences of theorists who have failed to link what is known about biophysically constrained RNA-mediated cell type differentiation to behavior.

See: Protein Contacts, Maximum Entropy in Biology, and Why Behavioural Mechanisms Matter: the PLOS Comp Biol May Issue

Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean?

It has recently been argued that there is no reason to assume that protein sequences should follow maximum entropy distributions, and that it is therefore puzzling that the max-ent formalism is successful for predicting interacting residues in proteins. Erik van Nimwegen argues that such apparent puzzles result from a misconception of the meaning of the max-ent formalism and, more generally, of the meaning of probabilities.
The Maximum Entropy Fallacy Redux?
Maximum entropy has a long and contested history in statistical physics, the field in which it was first introduced. In contrast to the positive evaluation of maximum entropy in science presented by Erik van Nimwegen (above), Erik Aurell contributes to the ongoing discussion about the use of maximum-entropy models in the modeling of biological data by arguing that max-ent provides no grounds to believe in direct coupling analysis (DCA).
To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter
Mutualistic cooperation often requires multiple individuals to behave in a coordinated fashion. Hence, while the evolutionary stability of mutualistic cooperation poses no particular theoretical difficulty, its evolutionary emergence faces a chicken-and-egg problem: an individual cannot benefit from cooperating unless other individuals already do so. Arthur Bernard and colleagues use simulations in evolutionary robotics to study the consequences of this problem.

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