Skip to main content

New findings : Myosin, D3, Actins, Autophagy/Phagocytosis

It is time to look at some new findings as these were identified by Machine Learning and Network Analysis.

Before continuing please note that in previous posts we discussed the importance of Endoplasmic Reticulum Stress, the Unfolded Protein Response and Genes AXL, GRB2, MGP, TYRO3, MERTK, GGCX, GAS6, SH2B3.

Recall also that Sulfation has been also selected as important.


The latest findings suggest the following Topics as being relevant to the Research presented in this Blog :

CYP27A1 and VDBP
LXR (Liver X Receptor )
Actins (G-Actin, F-Actin)
Myosin
Phagocytosis / Autophagy


On the following algorithmic run, Machine Learning identifies relevant Topics to this Research :





These topics (and several additional ones) are further investigated below :

CYP27A1

According to [7], CYP27A1 metabolises Vitamin D3. CYP27A1 was selected by Network Analysis.


Regarding VDBNP, according to [6] :

VDBP, a glycosylated-globulin, plays an important role not only in transporting vitamin D and circulating metabolites, but also in other biological functions to affect carcinogenesis. It is a member of the circulating actin scavenger system that prevents formation of F-actin networks and harmful effects following from cell or tissue damage12. VDBP is involved in macrophage activation by converting as group-specific component protein derived macrophage activating factor (GcMAF) and neutrophil chemotaxis by enhancing the chemotactic effect of complement-derived peptides



LXR - TAM Receptors


Recall that LXR (shown in black rectangle) was found in the Network Analysis chart in a previous post : http://algogenomics.blogspot.com/2017/05/machine-learning-nlp-and-network.html






From [1] we read :

 “Additionally, cholesterol-dependent activation of Liver X receptor (LXR) transcription factors has been shown to upregulate the expression of MER in macrophages as a positive feedback mechanism promoting engulfment of apoptotic cells and immunosuppression

“Thus, TAM receptors are responsible for creating an anti-inflammatory and reparative setting around apoptotic sites that prevents inflammatory responses towards self-derived antigens and restores tissue homeostasis.”


TAM receptors regulate the bilateral communication between dendritic and lymphoid T cells in different immunostimulatory conditions. Once activated, adaptive immune cells must communicate back with innate cells to avoid uncontrolled and chronic activation of the immune system”


Recall also that Dr Mark Davis (Stanford University) has found that T Cells are activated in CFS. 


Interestingly, according to [2], MERTK  :

“We find that MERTK and PROS1 are expressed in human T cells upon TCR activation and drive an autocrine proproliferative mechanism. Collectively, these results suggest that MERTK on DCs controls T cell activation and expansion through the competition for PROS1 interaction with MERTK in the T cells. In conclusion, this report identified MERTK as a potent suppressor of T cell response.”


MYOSINS

Myosins comprise a superfamily of ATP-dependent motor proteins and are best known for their role in muscle contraction and their involvement in a wide range of other motility processes in eukaryotes. They are responsible for actin-based motility

In [3] we find yet another association of MERTK with phagocytosis and Myosin :


Mertk can drive redistribution of dynamic protein, myosin II, from F-actin bundles to the sites of phagocytic cup. A large body of evidence shows that Mertk plays an essential role in phagocytosis through activation of its downstream signalling which is critical for cytoskeletal rearrangement during phagocytosis.”


Next we look at Myosin 9B (https://en.wikipedia.org/wiki/MYO9B)

In [4] we find that several SNPs on MYO9B are associated with autoimmune diseases :

“Our data demonstrate consistent association with the A allele and AAA haplotype of three SNPs in the MYO9B gene, which were previously reported to be associated with CD in the Dutch population. This suggests that genetic variation in MYO9B is associated with CID, SLE, and RA and that MYO9B is a general risk factor for autoimmunity”

Interestingly, MYO9B is also found to be associated with impaired Phagocytosis as found in  [5] :

“The rapid recruitment of phagocytic cells to inflammatory “hot spots” is a basic function of the innate immune system. Given the profound effect of Myo9b deficiency on membrane protrusive activity and motility, it would be reasonable to expect that monocyte recruitment is impaired in vivo. Indeed, we found that the accumulation of monocytes and macrophages in the peritoneal cavity of Myo9b−/− mice intraperitoneally injected with the chemoattractant C5a was severely blunted. Hence, Myo9b clearly has an important function in innate immune responses. Another essential function of professional “big eaters” such as macrophages is phagocytosis. Rho-family GTPases are intimately involved in controlling the cytoskeletal dynamics during phagocytosis (44), and we would predict that Myo9b is also important for the local coordination of membrane extensions and particle internalization.”

Also interestingly , F-Actin is mentioned several times in [5]. Recall also that F-Actin was also mentioned in [3] along with MERTK and Myosin


We now look to an entry of Wikipedia regarding the Cytoskeleton :

Microfilaments are composed of linear polymers of G-actin proteins, and generate force when the growing (plus) end of the filament pushes against a barrier, such as the cell membrane. They also act as tracks for the movement of myosin molecules that attach to the microfilament and "walk" along them. Myosin motoring along F-actin filaments generates contractile forces in so-called actomyosin fibers, both in muscle as well as most non-muscle cell types.[14] Actin structures are controlled by the Rho family of small GTP-binding proteins such as Rho itself for contractile acto-myosin filaments ("stress fibers"),


It is time now to look at the involvement on how Endoplasmic Reticulum Stress,  Misfolded Proteins and the Unfolded Protein Response is pertinent to the information shown above :


According to [8] :

In response to the challenge of misfolded proteins, autophagy has a crucial function as an adaptive ‘self-eating’ process by which cellular components are encapsulated within autophagosomes and degraded. Similarly to the unfolded protein response (UPR), autophagy can result in either cell survival or cell death167,168. The mechanisms by which the UPR induces autophagy are incompletely understood, but probably involve signalling through PKR-like ER kinase (PERK)–eukaryotic translation initiation factor 2α (eIF2α) and inositol-requiring enzyme 1α (IRE1α)169.

Since I am not a medical professional I will not proceed to a complete hypothesis, however the possibility that a combination of the following Genes should be investigated resulting in impaired autophagy, autoimmunity, inflammatory processes, B and T-Cell Activation, Oxidative Stress, Endoplasmic Reticulum Stress and the Syndromes discussed in this Blog (Gene list not inclusive - red color signifies importance)

GAS6 MERTK, GGCX, VKORC1,TYRO3,PLCG1FASLGMYO9B, SH2B3, RHOA,PROS1,GC,NR1H3, CYP27A1, NR1H4

Since I do not have any relevant genome patient data, Researchers are kindly requested to further investigate Genes discussed in this post. The mechanisms described may be relevant to additional diseases and conditions not discussed in this post.



Additional Findings will be posted soon.


Researchers are also kindly asked to Reference this Research. Thank you.







References





[2] : https://www.ncbi.nlm.nih.gov/pubmed/25624460






Comments

Popular posts from this blog

Machine Learning, NLP and Network Analysis-Guided Medical Research : A Case Study

Can Machine Learning help us in identifying the origin of several Medical Syndromes?

In previous posts we have seen how approximately 8 Million PubMed abstracts were collected and analyzed using Natural Language Processing (NLP) techniques. This NLP Processing is the basis for generating Data that may then be used as Input to several Machine Learning algorithms.

In this Case Study our Goal is to identify relevant Medical Topics (Topics include Genes, Biological Pathways, etc) that are most likely to direct Medical Researchers towards the origin(s) of the following Syndromes :

-Post-Finasteride Syndrome
-Post-Accutane Syndrome
-Chronic Fatigue Syndrome
-Fibromyalgia
-Gulf-War Syndrome
-Post-Treatment Lyme disease Syndrome

Before continuing, please read the following post for important disclaimershere


Note that the results shown below originate strictly from output of Machine Learning Algorithms / Network Analysis. No Human intervention has been made apart from the fact that Candidate T…

Welcome to Algo-genomics

I decided to start this Blog because i wanted to document my 4-year effort on identifying what is behind several syndromes that have no known treatment such as the Post-Finasteride Syndrome (known as PFS) and also Chronic Fatigue Syndrome (also known as CFS).
I would also like to draw attention from Researchers that could potentially use/validate the hypotheses that will be discussed in this Blog.
My first effort has focused to  "Post-Finasteride Syndrome", a syndrome with a debilitating set of symptoms that persist for a small percentage of people that have taken the drug Finasteride. The problems that are associated with Post-Finasteride Syndrome  can be found  on the Post-Finasteride Foundation Website :

http://pfsfoundation.org

As Research progressed, i began realizing that there were several syndromes that had very similar/overlapping symptoms. According to the hypothesis being discussed here, these potentially associated Syndromes of unknown origin are the following :