Round 1

Reviewer 1 Report

The manuscript presents work,  in analyzing the SNPs in CYP11A1 gene that can lead to PCOS. Effect of this mutations are analyzed using set of publicly available bioinformatics tools. The manuscript touches the effect of these mutations in many aspect like sub-cellular localization, ontology, instability index, 3-D and 2-D structures , transmembrane topology etc.  The current tools use for the analysis has been described and it could benefit research and analysis of other important genes associated with diseases. However, I would suggest the authors to address the following aspects:

 

(1) Give a schematic diagram of in- silico analysis scheme as a Figure 1 which will be easy to understand for the readers

 

2) Is there any relationship among the identified SNPs? Does it alter the protein- protein network interaction of CYP11A1 with other proteins?

 

(3) I suggest to extend the study to use multiple bioinformatics tools which is normally use in this type of SNPs study along with CELLO2GO,ProtParam and PHYRE2. Using multiple bioinformatics tool in studying different aspect of protein mutation will help the authors to filter or make the claim stronger (I suggest using tools like SIFT, PolyPhen2, PROVEAN,I mutant, ITASSER,  PROCHECK, MutPred2)

 

4) Elaborate more on the discussion or conclusion part. Current manuscript discussion or conclusion part is too short for all the analysis that has been done on effect of SNPs.

 

  5) Increase the resolution of current figures so that it will be easier to see.

Author Response

University of Punjab

School of Biochemistry & Biotechnology

 Quaid-e-Azam Campus Lahore, Pakistan

 

 

                                                                                                                                    June 27, 2022

 

 

 

Subject: Rebutal Letter (Reviewer 1)

 

 

Reviewers comment 1:

                 No a specific SNP to explain the high excess of androgen in PCOS.

Reply:

CYP11A1 gene encodes for enzyme cholesterol desmolase which catalyzes the transformation of cholesterol into pregnenolone (a precursor of steroid hormones) in mitochondria. This is the very first step of steroidogenesis pathway. Any disturbance in this pathway may result in abnormal level of steroid hormones in body.

Single nucleotide polymorphisms in a gene might affect in multiple ways. Like they may cause the up-regulation or down-regulation of the gene. They may also cause the formation of a protein with improper structure leading to abnormal functioning. This project is an attempt to explore the mutations that can lead to any of these changes ultimately causing hormonal disturbances. Variations with adverse effects on 3D structure, physicochemical attributes or sub-cellular localization of protein might be the contributing to PCOS. This study has provided us an idea about the possible SNPs that are definitely disturbing the normal steroid hormones synthesis pathway in female body and hence, provides a strong base for future experimentation. Now, in order to find out the association of an individual SNP with high level of androgens, experiments involving control and experimental groups can be designed. Therefore, this project has enabled us to find out majority of the possible biomarkers for PCOS prognosis and diagnosis.

 

Reviewers comment 2:

                 Similarly, the SNP so far could not to explain the insulin resistance in PCOS.

Reply:

            PCOS is a multifactorial trait which is controlled by multiple genes. The focus of present study is CYP11A gene that is definitely associated with PCOS as it regulates steroidogenic hormones synthesis. This study was not an attempt to determine the SNPs that might be related with all the symptoms of PCOS like insulin resistance, hirsutism and irregular or no ovulation etc. Even the excessive research on this disease has not yet solved the mystery of PCOS definite causes. And the exact cause of PCOS is still unknown. In this scenario, we have tried our best to sort out this mystery by providing the variations that are associated with steroid hormonal disturbance which might help in detecting the probability of developing PCOS in females in future. Another fact is that insulin resistance is itself under the control of multiple genes and environmental factors. In order to identify individual SNP associated with insulin resistance in PCOS patients, a long range of experiments involving the experimental group comprising of PCOS patients with high insulin resistance and the control groups with PCOS patients with low insulin resistance or healthy females with insulin resistance are required. Because as per the literature, in many cases insulin resistance in healthy females is also of the same level as in PCOS patients (Cibula, 2004). The comparative analysis through long term experimentation can be helpful, only to some extent, in identification of insulin resistance associated SNPs.

Reference:

Cibula, D. Is insulin resistance an essential component of PCOS? The influence of confounding factors. Human Reproduction 2004, 19, 757-759.

 

Dr. Fatima Muccee

Assistant Professor

School of Biochemistry & Biotechnology

Email: [email protected]

 

 

Author Response File: Author Response.docx

Reviewer 2 Report

This is an interesting study to investigate SNP in PCOS. It could provided some informations regarding the mechanisms of PCOS. Some comments about this study:

1. No a specific SNP to explain the high excess of androgen in PCOS.

2. Similarly, the SNP so far could not to explain the insulin resistance in PCOS.

Author Response

University of Punjab

School of Biochemistry & Biotechnology

 Quaid-e-Azam Campus Lahore, Pakistan

 

 

                                                                                                                                                                              June 27 2022

 

Subject: Rebutal Letter (Reviewer 2

 

Reviewers comment 1:

Give a schematic diagram of in-silico analysis scheme as a Figure 1 which will be easy to understand for the readers.

 

Reply

            A schematic diagram of in-silico scheme as Figure 1 has been incorporated in manuscript in section 2 (Materials and Methods).

Figure 1: A schematic representation of in-silico analysis performed in present study

 

Reviewers comment 2

Is there any relationship among the identified SNPs? Does it alter the protein- protein network interaction of CYP11A1 with other proteins?

 

Reply

The relationship between SNPs documented in present study is not the focus of this project. Each SNP has been independently analyzed. However, several SNPs were found to have similarity in their effects on different attributes of encoded proteins.

As far as, the protein-protein interaction of CYP11A1 protein is concerned, not a single SNP was found to have any impact. The effect was confirmed through Search Tool for   the Retrieval of Interacting Genes i.e. STRING. As, the SNPs did not alter protein-protein interactions of CYP11A1 protein so the data has not been incorporated in manuscript. However, STRING results in some cases are attached as a proof (Figure A) in rebuttal letter for the kind information of reviewer.

 

Figure A: Analysis of interaction of CYP11A1 protein with different other proteins through STRING for different cases

 

 

 

Reviewers comment 3:

I suggest to extend the study to use multiple bioinformatics tools which is normally use in this type of SNPs study along with CELLO2GO, ProtParam and PHYRE2. Using multiple bioinformatics tool in studying different aspect of protein mutation will help the authors to filter or make the claim stronger (I suggest using tools like SIFT, PolyPhen2, PROVEAN,I mutant, ITASSER,  PROCHECK, MutPred2).

 

Reply:

            According to suggestion of Reviewer, we have added more bioinformatics tools i.e. SIFT and PolyPhen2 (SUBSECTION 2.2 and 3.8 and Table 1) and I mutant in (SUBSECTION 2.4 and 3.1 and Table 2) in order to determine the deleteriousness and effect of mutations on stability of mutated  proteins, respectively. Changes incorporated are highlighted yellow.

According to SIFT, mutations rs750026801, rs533078157, rs143655263, rs1316467116, rs1393077247, rs1440123283, rs1207802955, rs1416104210, rs1596159786, rs1228084259, rs746124429, rs1459305798, rs1046646548, rs1567051424, rs867506250, rs1220602604, rs200029503, rs1321165216, rs566280511, rs754698583, rs551306530, rs777925426 and rs200726137 are tolerated. While the SNPs rs1444841908, rs1402720332, rs746842413, rs1471344818, rs546976108, rs190764523, rs866217710, rs1258660118, rs762299364, rs1400474918, rs1567052760, rs1461423064, rs1021942880, rs1596159365, rs747101738, rs1402190131, rs748120824, rs1424340465, rs139449608, rs531531464, rs745719036, rs867506250, rs1208632679, rs752776256, rs1398357296, rs944327325, rs771663597, rs1448075161, rs1344040376, rs1416463682, rs780138488, rs774799229, rs757055824, rs758061011, rs1326008763, rs775664050, rs750026801, rs776056840, rs779154292, rs1217014229, rs549043326, rs1421587886, rs1178589612, rs755186597, rs1224774813, rs757299093, rs1555425667, rs762412759 , rs755975808, rs754610565, rs779413653, rs765916701, rs1368450780, rs747901197  and rs148124218 are found to have deleterious effect.

According to PolyPhen, mutations rs750026801, rs533078157, rs143655263, rs1316467116, rs1258660118, rs1393077247, rs1440123283, rs1207802955, rs1416104210, rs1596159786, rs1459305798, rs747101738, rs745719036, rs1208632679, rs1220602604, rs200029503, rs1321165216, rs1448075161, rs566280511, rs754698583, rs551306530, rs777925426 and rs200726137 are categorized as benign. While the SNPs rs1444841908, rs1402720332, rs746842413, rs1471344818, rs546976108, rs190764523, rs866217710, rs762299364, rs1400474918, rs1567052760, rs1461423064, rs1228084259, rs1021942880, rs746124429, rs1596159365, rs1402190131, rs748120824, rs1424340465, rs139449608, rs1046646548, rs1567051424, rs531531464, rs752776256, rs1398357296, rs944327325, rs771663597, rs1344040376, rs1416463682, rs780138488, rs774799229, rs757055824, rs758061011, rs1326008763, rs775664050, rs750026801, rs776056840, rs779154292, rs1217014229, rs549043326, rs1421587886, rs1178589612, rs755186597, rs1224774813, rs757299093, rs1555425667, rs762412759 , rs755975808, rs754610565, rs779413653, rs765916701, rs1368450780, rs747901197 and rs148124218 are possibly damaging.

 

Table 1: SNPs of CYP11A1 gene retrieved from Ensemble database, their consequence type and categorization of cases

 

SNPs #	Region of gene	SNP ID	Nucleotide change	Case	Consequence type	Amino acid change	Amino acid position	SIFT	Poly-phen
1	Exon 1	rs750026801	tAt>tt	1	Frame shift mutation	Y>X	85	---	---
2		rs999606408	Ccc>Tcc	2	Missense mutation	P>S	7	0.45	0
3		rs1444841908	Ctg>Atg			L>M	20	0.01	0.621
4		rs533078157	aTc>aCc			I>T	40	0.39	0.003
5		rs1402720332	AaC>aaA			N>K	61	0.04	0.999
6		rs746842413	ttC>ttA			F>L	82	0	0.986
7		rs143655263	Gtc>Atc			V>I	79	0.44	0
8		rs1316467116	Cta>Gta			L>V	60	0.24	0.018
9		rs1471344818	tCc>tTc			S>F	41	0.04	0.838
10	Exon 2	rs546976108	Gag>Aag			E>K	91	0	1
11		rs190764523	Ggc>Agc			G>S	94	0	0.998
12		rs866217710	Gat>Aat			D>N	106	0.02	0.981
13	Exon 3	rs1258660118	Tcg>Acg			S>T	144	0.01	0.096
14		rs1393077247	aAg>aGg			K>R	148	1	0.075
15		rs1440123283	gAg>gGg			E>G	162	0.2	0.006
16		rs762299364	cTg>cCg			L>P	182	0	0.999
17		rs1400474918	Gag>Aag			E>K	208	0	0.952
18	Exon 2	rs776056840	Cga>Tga	3	Stop gained mutation	R>*	120	---	---
19		rs779154292	atC>at	4	Frame shift mutation	I>X	102	---	---
20		rs1217014229	Cac>CCac	5		H>PX	130	---	---
21	Exon 3	rs549043326	tCg>tAg	6	Stop gained mutation	S>*	144	---	---
22		rs1421587886	GcA>gc	7	Frame shift mutation	A>X	173	---	---
23		rs1178589612	cTg>cg	8		L>X	170	---	---
24	Exon 4	rs1567052760	gCc>gTc	9	Missense mutation	A>V	230	0.03	0.872
25		rs1207802955	Atc>Gtc			I>V	237	1	0.007
26		rs1461423064	cTc>cCc			L>P	248	0	0.998
27		rs1416104210	ttC - ttG			F>L	258	0.9	0.001
28		rs1596159786	Acc>Gcc			T>A	262	0.24	0.006
29		rs1228084259	Gct>Tct			A>S	268	0.62	0.506
30 31		rs1021942880	gCa>gTa			A>V	269	0	0.989
	Exon 5	rs746124429	tTc>tCc			F>S	284	0.21	0.509
32		rs1459305798	Tgg>Cgg			W>R	286	1	0.001
33		rs1596159365	tAc>tCc			Y>S	298	0	0.995
34		rs747101738	Ctg>Gtg			L>V	324	0.01	0.286
35		rs1402190131	gCa>gAa			A>E	325	0	0.991
36		rs748120824	gAc>gGc			D>G	329	0	0.98
37		rs1424340465	aCg>aTg			T>M	330	0	0.997
38	Exon 6	rs139449608	aCg>aTg			T>M	331	0	1
39		rs1046646548	aTg>aCg			M>T	333	0.11	0.481
40		rs1567051424	tAt>tTt			Y>F	340	0.53	0.991
41		rs531531464	Gag>Aag			E>K	341	0	1
42		rs745719036	cAg>cGg			Q>R	372	0.05	0.051
43	Exon 4	rs755186597	Cga>Tga	10	Stop gained mutation	R>*	232	---	---
44	Exon 5	rs1224774813	Cag>Tag	11		Q>*	282	---	---
45		rs757299093	Ata>ta	12	Frame shift mutation	I> -	279	---	---
46		rs1555425667	Gag>ag	13		E>X	314	---	---
47	Exon 7	rs867506250	Cac>Tac	14	Missense mutation	H>Y	388	0.43	0.938
48		rs1208632679	Tcc>Ccc			S>P	391	0.01	0.437
49		rs752776256	Cag>Aag			Q>K	395	0	0.967
50		rs1220602604	Ctt>Gtt			L>V	398	1	0.037
51		rs200029503	Gta>Tta			V>L	399	0.12	0.012
52		rs1398357296	Gtt>Ttt			V>F	403	0	0.869
53		rs1321165216	Ctt>Att			L>I	404	0.29	0.239
54		rs944327325	Gat>Cat			D>H	406	0	0.748
55		rs771663597	aTt>aCt			I>T	409	0	0.656
56		rs1448075161	Gcc>Acc			A>T	411	0.03	0.409
57	Exon8	rs1344040376	Caa>Gaa			Q>E	416	0.04	0.986
58		rs1416463682	tAt>tGt			Y>C	420	0	0.998
59		rs780138488	Cca>Tca			P>S	437	0	1
60		rs774799229	cTg>cCg			L>P	463	0	0.992
61		rs757055824	Gct>Act			A>T	468	0	1
62		rs758061011	Atg>Gtg			M>V	472	0	0.977
63		rs1326008763	Atc>Gtc			I>V	474	0.01	0.135
64	Exon 9	rs566280511	Atg>Ctg			M>L	479	0.18	0.011
65		rs754698583	Gtt>Att			V>I	485	1	0.007
66		rs775664050	gTg>gAg			V>E	493	0	0.997
67		rs551306530	aTg>aCg			M>T	502	0.17	0.085
68		rs148124218	Ccc>Tcc			P>S	513	0	0.999
69		rs777925426	Ttt>Ctt			F>L	514	1	0
70		rs200726137	aAc>aTc			N>I	515	0.06	0.127
71	Exon 7	rs1454328072	Cga>Tga	15	Stop gained mutation	R>*	405	---	---
72	Exon 8	rs762412759	Cga>Tga	16		R>*	424	---	---
73		rs755975808	Cga>Tga	17		R>*	439	---	---
74	Exon 9	rs754610565	Cag>Tag	18		Q> *	521	---	---
75		rs779413653	Tga>Aga	19		*>R	522	---	---
76		rs765916701	Caa>Taa	20		Q>*	488	---	---
77		rs1368450780	Cag>Tag	21		Q>*	516	---	---
78		rs747901197	Gaa>Taa	22		E>*	517	---	---

 

 

The results of I Mutant are as follows;

3.1. Effect of SNPs on mutated protein stability

      Fifty-nine missense SNPs addressed in present study were examined for their influence on stability of proteins. It was found that all the missense mutations except rs1258660118, rs1393077247, rs1424340465, rs139449608, rs867506250, rs1208632679, rs752776256, rs1416463682 and rs200726137 were contributing to destabilization of corresponding mutated proteins (Table 2).

      Table 2. Prediction of SNPs effect on stability of protein through I Mutant Suite

#	SNP ID	DDG (kcal/mole)	Stability	RI
1	rs999606408	-0.47	Decrease	5
2	rs1444841908	-1.33	Decrease	8
3	rs533078157	-2.24	Decrease	9
4	rs1402720332	-0.31	Decrease	5
5	rs746842413	-1.20	Decrease	8
6	rs143655263	-0.21	Decrease	4
7	rs1316467116	-1.62	Decrease	8
8	rs1471344818	0.03	Increase	3
9	rs546976108	-0.89	Decrease	8
10	rs190764523	-1.53	Decrease	9
11	rs866217710	-1.16	Decrease	5
12	rs1258660118	0.09	Increase	5
13	rs1393077247	0.07	Increase	3
14	rs1440123283	-1.04	Decrease	8
15	rs762299364	-1.80	Decrease	7
16	rs1400474918	-1.08	Decrease	8
17	rs1567052760	-0.35	Decrease	4
18	rs1207802955	-1.00	Decrease	9
19	rs1461423064	-1.65	Decrease	3
20	rs1416104210	-1.63	Decrease	8
21	rs1596159786	-1.31	Decrease	9
22	rs1228084259	-0.74	Decrease	9
23	rs1021942880	-0.19	Decrease	2
24	rs746124429	-1.93	Decrease	9
25	rs1459305798	-1.16	Decrease	9
26	rs1596159365	-1.41	Decrease	6
27	rs747101738	-1.47	Decrease	8
28	rs1402190131	-0.42	Decrease	3
29	rs748120824	-0.40	Decrease	2
30	rs1424340465	0.24	Increase	2
31	rs139449608	0.13	Increase	1
32	rs1046646548	-0.67	Decrease	5
33	rs1567051424	0.08	Increase	3
34	rs531531464	-0.69	Decrease	7
35	rs745719036	-0.05	Decrease	1
36	rs867506250	0.22	Increase	5
37	rs1208632679	-0.12	Increase	4
38	rs752776256	-0.56	Increase	1
39	rs1220602604	-1.75	Decrease	7
40	rs200029503	-1.63	Decrease	8
41	rs1398357296	-1.61	Decrease	8
42	rs1321165216	-1.52	Decrease	9
43	rs944327325	-0.96	Decrease	9
44	rs771663597	-2.38	Decrease	9
45	rs1448075161	-0.67	Decrease	7
46	rs1344040376	-0.22	Decrease	1
47	rs1416463682	-1.03	Increase	1
48	rs780138488	-1.58	Decrease	8
49	rs774799229	-1.62	Decrease	6
50	rs757055824	-0.69	Decrease	6
51	rs758061011	-0.69	Decrease	8
52	rs1326008763	-0.64	Decrease	6
53	rs566280511	-0.94	Decrease	7
54	rs754698583	-0.73	Decrease	8
55	rs775664050	-1.59	Decrease	7
56	rs551306530	-1.06	Decrease	6
57	rs148124218	-1.15	Decrease	6
58	rs777925426	-0.48	Decrease	0
59	rs200726137	1.23	Increase	7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Reviewers comment 4:

Elaborate more on the discussion or conclusion part. Current manuscript discussion or conclusion part is too short for all the analysis that has been done on effect of SNPs.

 

Reply:

            The discussion and conclusion parts have been elaborated. Data from six different papers has been added and cited in Discussion part. Changes and references incorporated are highlighted yellow.

 

Reviewers comment 4:

Increase the resolution of current figures so that it will be easier to see.

 

Reply:

The resolution of figures has been increased to 600 dpi and incorporated in manuscript.

 

 

Dr. Fatima Muccee

Assistant Professor

School of Biochemistry & Biotechnology

Email: [email protected]

 

 

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Abstract can be modified with new analysis result and tools used

Figure 6 should be under the heading "Effect of SNPs on trans-membrane topology of mutated proteins "

Author Response

University of Punjab

School of Biochemistry & Biotechnology

 Quaid-e-Azam Campus Lahore, Pakistan

 

 

                                                                                                                                    July 1, 2022

 

 

 

Subject: Rebutal Letter (Reviewer 1)

 

 

Reviewers comment 1:

                 Abstract can be modified with new analysis result and tools used.

Reply:

The new analysis tools i.e. I-Mutant, SIFT and PolyPhen and the results of corresponding tools have been incorporated in Abstract through Track changes.

Abstract: PCOS is a reproductive disorder with multiple etiologies, mainly characterized by excess production of androgens. It is equally contributed by genes and environment. CYP11A1 gene is imperative for steroidogenesis so any dysregulation or mutation in this gene can lead to PCOS pathogenesis. Therefore, nucleotide diversity in this gene can be helpful in spotting likelihood of developing PCOS. Present study was initiated to investigate the effect of single nucleotide polymorphisms in human CYP11A1 gene on different attributes of encoded mutated proteins. i. e. sub-cellular localization, ontology, half-life, isoelectric point, instability index, aliphatic index, extinction coefficient, 3-D and 2-D structures and transmembrane topology.­­­ For this purpose, initially CDS and SNPs were retrieved for desired gene from Ensembl followed by translation of CDS using EXPASY tool. The protein sequence obtained was subjected to different tools including CELLO2GO, ProtParam, PHYRE2, I-Mutant, SIFT and PolyPhen. It was found that out of seventy-eight SNPs analyzed in this project, seventeen mutations i.e. rs750026801 in exon 1, rs776056840, rs779154292 and rs1217014229 in exon 2, rs549043326 in exon 3, rs755186597 in exon 4, rs1224774813, rs757299093 and rs1555425667 in exon 5, rs1454328072 in exon 7, rs762412759 and rs755975808 in exon 8 and rs754610565, rs779413653, rs765916701, rs1368450780 and rs747901197 in exon 9 considerably altered the structure, sub-cellular localization and physicochemical characteristics of mutated proteins. Among the fifty-nine missense SNPs documented in present study, fifty-five and fifty-three were found to be deleterious according to SIFT and PolyPhen tools, respectively. Forty-nine missense mutations were analyzed to have decreasing effect on stability of mutant proteins. Hence, these genetic variants can serve as potential biomarkers in human females for determining the probability of being predisposed to PCOS.

Reviewers comment 2:

                

Figure 6 should be under the heading "Effect of SNPs on trans-membrane topology of mutated proteins"

Reply:

            Figure 6 has been incorporated under the heading “Effect of SNPs on trans-membrane topology of mutated proteins"

 

 

 

 

 

 

Dr. Fatima Muccee

Assistant Professor

School of Biochemistry & Biotechnology

Email: [email protected]

 

 

Author Response File: Author Response.docx