Page 62 - Biennial Report 2018-20 Jun 2021

The NF-kB activation model was used for predictive analysis. Comparing experimental data on
the overexpression or knockouts of specific genes afforded qualitative agreement between
model predictions and the experimental results. This model will be further used for the
mechanistic study of pathogenesis of atherosclerosis and psoriasis. To obtain an elaborate view
of the gene regulatory network underlying the development of these disease comorbidities, data
mining was carried out. After getting genes for diabetic dyslipidemia associated complications
assisted by text mining and manual curation, miRNAs associated with them with the same
strategy were also identified. In this path, 20 reported miRNAs for atherosclerosis, and six
miRNAs for psoriasis were identified. From the PubMed literature abstracts related to psoriasis,
300 genes were found to be implicated in its pathogenesis.

Using automated text mining and manual curation of dyslipidemia related literature 214 genes
implicated in dyslipidemia were also identified. Many of these genes are enriched in PPAR, PI3K-
AKT, and adipocytokine signaling pathways.

Atherosclerosis miRNAs are: hsa-mir-33, hsa-mir-148a, hsa-mir-122, hsa-mir-33a, hsa-mir33b,
hsa-mir-107, hsa-mir-28-5p, hsa-mir-29b, hsa-mir-29a, hsa-mir-758, hsa-mir-106, hsamir-370,
hsa-mir-27, hsa-mir-613, hsa-mir-302a, hsa-mir-168, hsa-mir-143, hsa-mir-145, hsa-mir-17, and
hsa-mir-92, respectively. From these miRNAs we identified target genes, such as, hsa-mir-29b-
3p target DPP4, LEP, LPL, and SIRT1 genes; hsa-mir-29b-5p target HGF; hsa-mir-29a-3p target
SIRT1, LPL, LEP, and DPP4; hsa-mir-29a-3p target HGF, PON1; hsa-mir-758-3p target DPP4 gene;
hsa-mir613 target EDN1, HGF, TCF7L2 genes; hsa-mir-302a target PPARA; hsa-mir-143-3p target
ALB and has-mir-5p target DPP4 and MTHFR genes. Psoriasis miRNAs are: hsa-mir-212, hsa-mir-
132, hsa-mir-33b, hsa-mir-370, hsa-mir-326, and hsa-mir-146a respectively.

Atherosclerosis inflammatory connections: NFKB is known to act as a key regulator of
inflammatory responses and induces the expression of many pro-inflammatory genes,
connections for the previously identified genes were studied with this signaling pathway.

Plaque formation in atherosclerosis is basically due to LDL deposition over the coronary arterial
wall that is a major carrier of lipid transport throughout the body. In diabetic dyslipidemia, level
of LDL and triglycerides level increases in blood and this increase in TG level results in elevation
of free fatty acids through hydrolysis of triglycerides by lipoprotein lipase. These elevated FFAs
promote activation of TLR4. TLR4 further promotes the activation of TAK1 by Ubiquitilation of
TRAF6 mediated by MYD88-TIRAP adaptors that recruits IRAK1 to activate IRAK4 which activates
TRAF6. TAK1 further activates the IKK complex that leads to phosphorylation of IKB protein and
consequently activates NFKB that further induces expression of inflammatory mediators such as
cytokines, chemokines or co-stimulatory molecules.

Psoriasis inflammatory connections: In psoriasis modelling, all the cytokines namely IL12B, IL23A,
and IL23R were connected to the STAT1, STAT3 and STAT4 transcription factor. After review of
several literature, the down regulation of STAT1, EHF, STAT3 were found in psoriasis condition.
So, based on this evidence, a network of diabetic dyslipidemia associated psoriasis is in the
process of being built. The miRNAs that target these genes are also being examined for the
network building.

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