Exploring unknown synergistic combinatorial plausible and alternative biological hypotheses for metallic components, in silico, post ETC-1922159 treatment in CRC † shriprakash sinha◦,F Metals play a crucial role in cell signaling and engage in various functions, disruption of which leads to severe pathological conditions. The recently developed Porcupine-Wnt inhibitor ETC1922159 has shown promise in colorectal cancer cases and known to affect the mechanism of the Wnt pathway, thus curbing, suppressing and inhibiting pathological conditions. Components involving metals have also been affected by the administration of ETC-1922159. Work by Byrne et al. 1 show that Selenium works with ABC transporters and more specifically at higher toxicity levels. SELENBP1 was found to be down regulated in CRC after treatment with ETC-159, possibly indicating the heavy amount of Selenium in colorectal cancer. Multiple members of ABC transporter family were found to be influenced after ETC-159 treatment in colorectal cancer. Family members belonging to such factors like ABC etc, might be involved synergistically in pathological case or otherwise. Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of such members that are involved in the pathway. Employing the unpublished search engine design of Sinha 2 , we present here, multiple plausible and alternative synergistic combinatorial biological hypotheses for the involved members, which emerge after a cross family member analysis of the in silico revelations pertaining to the components under investigation (say, SELENBP1 and ABC transporter family members). Additionally, we bolster our findings with presentation of correct approximate hits on multiple combinatorial hypotheses which exist as published confirmatory experimental results also. The manuscript provides with a range of cross family analysis among various factors which has been found to be influenced after administration of ETC-1922159 along with inferences on the revealed combinatorial hypotheses. Future steps would involve wet experiments for further investigation.
Introduction PORCN-WNT inhibitors W ORKING
†
DRAFT IN PROGRESS
Aspects of unpublished work presented as poster in the recently concluded Wnt Gordon Conference, from 6-11 August 2017, held in Stowe, VT 05672, USA. F Corresponding author contact:
[email protected] ◦ Worked as independent researcher till 2017. This work is licensed under a Creative Commons “Attribution-NonCommercial-ShareAlike 4.0 International” license.
The regulation of the Wnt pathway is dependent on the production and secretion of the WNT proteins. Thus, the inhibition of a causal factor like PORCN which contributes to the WNT secretion has been proposed to be a way to interfere with the Wnt cascade, which might result in the growth of tumor. Several groups have been engaged in such studies and known PORCN-WNT inhibitors that have been made available till now are IWP-L6 3 & 4 , C59 5 , LGK974 6 and ETC-1922159 7 . In this study, the focus of the at-
tention is on the implications of the ETC-1922159, after the drug has been administered. The drug is a enantiomer with a nanomolar activity and excellent bioavailability as claimed in Duraiswamy et al. 7 . Combinatorial search problem and a possible solution We have already addressed the issue of combinatorial search problem and a possible solution in Sinha 8 and Sinha 2 . The details of the methodology of this manuscript have been explained in great detail in Sinha 8 & its application in Sinha 2 and readers are requested to go through the same for gaining deeper insight into the working of the pipeline and its use for published data set generated from ETC-1922159. Fundamentals of Sinha 8 and Sinha 2 have been established in a published work by Sinha 9 . In order to understand the significance of the solution proposed to the problem of combinatorial search that the biologists face in revealing unknown biological search problem, these works are of importance. Using the same code in Sinha 8 and Sinha 2 , it was possible to generate the rankings for 2nd order combinations. The total number of gene combination w.r.t to a particular gene would be 2517 out of a list of 2518 genes. The goal of this manuscript is to analyse some of these 2rd order combinations.
Results and Discussion SELENBP1 - ABC transporter cross family analysis
R ANKING ABC FAMILY VS SELENBP1 SELENBP1 W. R . T ABC FAMILY R ANKING OF ABC FAMILY W. R . T laplace linear rbf laplace SELENBP1 - ABCA2 1476 2083 821 SELENBP1 - ABCA2 1169 SELENBP1 - ABCE1 2132 233 1932 SELENBP1 - ABCE1 1135 SELENBP1 - ABCF2 1163 890 828 SELENBP1 - ABCF2 844 R ANKING
OF
SELENBP1 linear rbf 1425 1405 126 508 1767 511
Table 1 2nd order interaction ranking between ABC w.r.t UBE2 family members.
U NEXPLORED COMBINATORIAL ABC w.r.t SELENP1 SELENBP1 ABCA2 SELENBP1 ABCE1 SELENBP1 ABCF2 SELENBP1 w.r.t ABC SELENBP1 ABCA2 SELENBP1 ABCF2 Table 2 2nd order combinatorial hypotheses between ABC and SELENBP1.
HYPOTHESES
SELENBP1 - IL cross family analysis
R ANKING
OF
IL
R ANKING IL FAMILY VS SELENBP1 SELENBP1 R ANKING OF SELENBP1 W. R . T linear rbf laplace 951 1277 IL1RL2 - SELENBP1 1756 275 156 IL17D - SELENBP1 347 1670 359 1075 IL17RB - SELENBP1 2415 2585 IL17RD - SELENBP1 2506 907 862 IL33 - SELENBP1 532 2001 1803 ILF2 - SELENBP1 1885 1782 1316 ILF3 - SELENBP1 599 616 1122 ILF3.AS1 - SELENBP1 2327
FAMILY W. R . T
IL1RL2 - SELENBP1 IL17D - SELENBP1 IL17RB - SELENBP1 IL17RD - SELENBP1 IL33 - SELENBP1 ILF2 - SELENBP1 ILF3 - SELENBP1 ILF3.AS1 - SELENBP1
laplace 1296 505 1218 2075 644 2044 1839 1062
IL FAMILY linear rbf 252 2367 1880 2451 1672 596 2482 133 2174 2315 1518 2166 1213 294 248 908
Table 3 2nd order interaction ranking between ABC w.r.t SELENBP1 family members.
U NEXPLORED COMBINATORIAL HYPOTHESES IL w.r.t SELENP1 IL-1RL2/17D/17RB/33/F3.AS1 SELENBP1 SELENBP1 w.r.t IL IL-17RB/F3/F3.AS1 - SELENBP1 Table 4 2nd order combinatorial hypotheses between ABC and SELENBP1.
COX - SCO1 cross family analysis
R ANKING
COX FAMILY laplace COX10 - SCO1 2402 COX10.AS1 - SCO1 597 COX14 - SCO1 2561 COX18 - SCO1 2200 OF
R ANKING IL FAMILY VS SELENBP1 W. R . T SCO1 R ANKING OF SCO1 W. R . T COX FAMILY laplace linear rbf linear rbf 1043 1529 COX10 - SCO1 1407 2479 2152 1478 787 COX10.AS1 - SCO1 574 2228 538 2374 2628 SCO1 - COX14 2610 2410 553 1329 2029 SCO1 - COX18 2526 1978 17
Table 5 2nd order interaction ranking between COX w.r.t SCO1 family members.
U NEXPLORED COMBINATORIAL SCO1 w.r.t COX COX10.AS1 SCO1 COX w.r.t SCO1 COX10 SCO1 Table 6 2nd order combinatorial hypotheses between COX and SCO1.
HYPOTHESES
COX - SCO1 cross family analysis
R ANKING
COX FAMILY laplace COX10 - SCO1 2402 COX10.AS1 - SCO1 597 COX14 - SCO1 2561 COX18 - SCO1 2200 OF
R ANKING COX SCO1 linear rbf 1043 1529 1478 787 2374 2628 1329 2029
W. R . T
VS SCO1 R ANKING OF SCO1
FAMILY
COX10 - SCO1 COX10.AS1 - SCO1 SCO1 - COX14 SCO1 - COX18
W. R . T COX FAMILY laplace linear rbf 1407 2479 2152 574 2228 538 2610 2410 553 2526 1978 17
Table 7 2nd order interaction ranking between COX w.r.t SCO1 family members.
U NEXPLORED COMBINATORIAL SCO1 w.r.t COX COX10.AS1 SCO1 COX w.r.t SCO1 COX10 SCO1 Table 8 2nd order combinatorial hypotheses between COX and SCO1.
HYPOTHESES
EXOSC - STEAP3 cross family analysis
R ANKING EXOSC STEAP3 W. R . T EXOSC FAMILY laplace linear rbf EXOSC2 - STEAP3 345 883 1540 EXOSC3 - STEAP3 1249 1606 1128 EXOSC5 - STEAP3 956 1426 1294 EXOSC6 - STEAP3 1048 1516 1443 EXOSC7 - STEAP3 801 1196 891 EXOSC8 - STEAP3 610 371 415 EXOSC9 - STEAP3 1966 1130 709 R ANKING
VS STEAP3 R ANKING OF EXOSC
FAMILY W. R . T
EXOSC2 - STEAP3 EXOSC3 - STEAP3 EXOSC5 - STEAP3 EXOSC6 - STEAP3 EXOSC7 - STEAP3 EXOSC8 - STEAP3 EXOSC9 - STEAP3
laplace 506 2357 1126 1937 2332 1922 1061
FAMILY
OF
STEAP3 linear rbf 824 802 1217 1745 1641 1751 291 1760 921 1517 2003 1670 1275 418
Table 9 2nd order interaction ranking between EXOSC family w.r.t STEAP3.
U NEXPLORED COMBINATORIAL HYPOTHESES STEAP3 w.r.t EXOSC EXOSC-2/3/5/6/7/8/9 STEAP3 EXOSC w.r.t STEAP3 EXOSC-2/3/5/7/9 STEAP3 Table 10 2nd order combinatorial hypotheses between EXOSC and STEAP3.
Conclusion
2 S. Sinha, bioRxiv, 2017, 180927.
Conflict of interest
3 B. Chen, M. E. Dodge, W. Tang, J. Lu, Z. Ma, C.-W. Fan, S. Wei, W. Hao, J. Kilgore, N. S. Williams et al., Nature chemical biology, 2009, 5, 100–107.
There are no conflicts to declare.
Acknowledgements Source of Data Data used in this research work was released in a publication in 10 . The ETC-1922159 was released in Singapore in July 2015 under the flagship of the Agency for Science, Technology and Research (A*STAR) and Duke-National University of Singapore Graduate Medical School (Duke-NUS).
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