A definitive guide to genetic mutations and their different types

INTRODUCTION

Changes in the genetic code that occur either spontaneously or as a result of mutagenesis can have a positive, negative or no influence on the overall function of cells. Sudden changes that occur in the genome are referred to as mutations.

These changes seldomly occur since DNA replication is a carefully controlled and guarded process that. Different types of mutations occur in cells. Some affect the entire chromosomal, while others are localized such as point mutations or small deletions and insertions.

Large genomic changes such as chromosomal mutations often involve either whole chromosome duplications that lead to adverse consequences such as trisomy 21 (Down’s syndrome).

Genetic mutations are separated into two major groups,

1. Point mutations
2. Frameshift mutations.

Point mutations refer to substitutions of one base for another. If a pyrimidine (adenine and guanine) or purine (cytosine or thymine) is replaced by another pyrimidine or purine, respectively, this is called a transition. If a purine is substituted by a pyrimidine, or vice versa, this called a transversion.

Frameshift mutations refer to base insertion or deletions (INDELS). When located in the coding region substitutions tend to have less disruptive consequences that insertion/deletions (INDELS). Due to the ease of incorporation when substituting like for like, transitions occur more frequently than transversions.

A large proportion of mutations that persist in the genome are in non-coding regions and are silent/neutral and do not generally affect gene expression. Mutations that do not change the resulting amino acid are called synonymous mutations and are likely to be located on the most variable position of the triplet code, the third position.

Mutations that change the translated amino acids are called non-synonymous mutations. Non-synonymous mutations can either be missense or nonsense mutations. Missense and nonsense mutations refer to a change in a single base of a codon that leads lead to an introduction of a different amino acid and a premature stop codon, respectively.

Linking genetic mutations to phenotype with ChIP seq data analysis
Connecting mutations to phenotypes and elucidating the mechanisms through which said mutations beget the presenting phenotypes remain the crucial foundation of medical research.

For example, different types of mutations that contribute to cancer have been analyzed (Ienga 2012). Here we will present examples of ChIP-seq studies of genetic mutations in hemophilia diseases, circadian rhythms and breast cancer.

Hemophilia mutations
Hemophilia B is a single gene clotting disease that results from mutations located in the coagulation factor IX (F9) promoter (Funnell and Crossley 2014).

The task of investigating the mechanisms through which these mutations cause the disease has been done via ChIP seq data analysis (Funnell et al. 2013, Brenig et al. 2018).

Transition and transversion mutations located at locus 26 create variants linked to hemophilia B Brandenburg (Funnell and Crossley 2014). Other transcription factor mutations linked to hemophilia B are located in the androgen receptor, CCAAT/enhancer-binding protein a (C/EBPa), hepatocyte nuclear factor 4a (HNF4a) and one cut homeobox (ONECUT) (Funnell et al. 2013). ChIP seq data analysis showed that ONECUT 1 and 2 transcription factors bind to the 5/6 site of the F9 promoter which when disrupted by transition and transversion mutations causes hemophilia B Leyden (Funnell et al. 2013, Funnell and Crossley 2014). Hemophilia B Leyden-like presentation in Hovawarts is associated with a transversion mutation (T -> A) located within HNF4a (Brenig et al. 2018).

ChIP-seq analysis of cancer mutations

Many point mutations and small INDELS have been linked with tumorgenesis (Lander et al. 2001).

ChIP seq data analysis of breast tumor carrying a frameshift mutation in the transcription factor, GATA3, found that the mutant product deregulates physiological protein turnover and mediates the response of cancer cells to estrogen signaling (Adomas et al. 2014).

ChIP seq data analysis of CRISPR-Cas9 generated Y537S and D538G mutants revealed that these cell lines showed ligand -independent growth and were endocrine resistant (Bahreini et al. 2017).

These findings hold important insights that can be used to direct translational studies.

Circadian rhythm associated mutations

Careful and precise maintenance of the circadian clock maintains a variety of timed events in biological systems. Offset circadian rhythms as a result of mutations in clock components have been linked with number of consequences in model organism studies (Alhopuro et al. 2010, Smith et al. 2010, Sulli et al. 2018).

In Neurospora crassa, ChIP-seq data analysis of the White Collar Complex (WCC) which is responsible for asexual spore development, amongst other things, found that a deletion mutation was responsible for out of sync expression of many transcription factor genes including genes necessary for daily activities in development (Smith et al. 2010).

In microsatellite unstable colorectal cancers there are a variety of CLOCK mutants resulting from deletions and insertion of Thymine in the coding region of CLOCK (Alhopuro et al. 2010). ChIP-seq data analysis of these mutants suggest that CLOCK is not just a tumor suppressor but also a caretaker that responds to DNA damage (Alhopuro et al. 2010). ChIP-seq analysis of the circadian regulators, nuclear receptors REV-ERBα and REV-ERBβ, in mice showed that these components can be used in treatment of different cancers through molecular inhibition of autophagy and lipogenesis when key tumor suppressors such as p53 are inactive (Sulli et al. 2018).

CONCLUSION
Mutations play a key role in disease development and presentation, and, therefore, treatment development. Different types of mutations affect biological pathways different way.

Thus, once identified it is important to study the mechanisms by which these mutations interfere with normal biological processes. ChIP-seq data analysis has been vital to understanding the effect different mutations lead to disease phenotypes. Pharmacological research is making major strides in translating next-generation studies into precision therapy.