Research article

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

  • + Equal first authors
  • Received: 20 October 2021 Revised: 08 December 2021 Accepted: 21 December 2021 Published: 04 January 2022
  • Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

    Citation: Abdulhadi Ibrahim H. Bima, Ayman Zaky Elsamanoudy, Walaa F Albaqami, Zeenath Khan, Snijesh Valiya Parambath, Nuha Al-Rayes, Prabhakar Rao Kaipa, Ramu Elango, Babajan Banaganapalli, Noor A. Shaik. Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2310-2329. doi: 10.3934/mbe.2022107

    Related Papers:

    [1] Jiankang Wang, Zhefeng Xu, Minmin Jia . Distribution of values of Hardy sums over Chebyshev polynomials. AIMS Mathematics, 2024, 9(2): 3788-3797. doi: 10.3934/math.2024186
    [2] Zhenjiang Pan, Zhengang Wu . The inverses of tails of the generalized Riemann zeta function within the range of integers. AIMS Mathematics, 2023, 8(12): 28558-28568. doi: 10.3934/math.20231461
    [3] Taekyun Kim, Dae San Kim, Dmitry V. Dolgy, Jongkyum Kwon . Sums of finite products of Chebyshev polynomials of two different types. AIMS Mathematics, 2021, 6(11): 12528-12542. doi: 10.3934/math.2021722
    [4] Utkal Keshari Dutta, Prasanta Kumar Ray . On the finite reciprocal sums of Fibonacci and Lucas polynomials. AIMS Mathematics, 2019, 4(6): 1569-1581. doi: 10.3934/math.2019.6.1569
    [5] S. Akansha, Aditya Subramaniam . Exploring Chebyshev polynomial approximations: Error estimates for functions of bounded variation. AIMS Mathematics, 2025, 10(4): 8688-8706. doi: 10.3934/math.2025398
    [6] Jin Li . Barycentric rational collocation method for semi-infinite domain problems. AIMS Mathematics, 2023, 8(4): 8756-8771. doi: 10.3934/math.2023439
    [7] Muhammad Uzair Awan, Nousheen Akhtar, Artion Kashuri, Muhammad Aslam Noor, Yu-Ming Chu . 2D approximately reciprocal ρ-convex functions and associated integral inequalities. AIMS Mathematics, 2020, 5(5): 4662-4680. doi: 10.3934/math.2020299
    [8] Tingting Du, Zhengang Wu . Some identities involving the bi-periodic Fibonacci and Lucas polynomials. AIMS Mathematics, 2023, 8(3): 5838-5846. doi: 10.3934/math.2023294
    [9] Xiao Jiang, Shaofang Hong . On the denseness of certain reciprocal power sums. AIMS Mathematics, 2019, 4(3): 412-419. doi: 10.3934/math.2019.3.412
    [10] Waleed Mohamed Abd-Elhameed, Omar Mazen Alqubori, Ahmed Gamal Atta . A collocation procedure for the numerical treatment of FitzHugh–Nagumo equation using a kind of Chebyshev polynomials. AIMS Mathematics, 2025, 10(1): 1201-1223. doi: 10.3934/math.2025057
  • Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.



    The properties of orthogonal polynomials and recursive sequences are popular in number theory. They are important in theoretical research and application. The famous Chebyshev polynomials and Fibonacci polynomials are widely used in the field of function, approximation theory and difference equation. They also promote the development of both the branch of mathematics such as cryptography, combinatorics and application of discipline such as intelligent sensing, satellite positioning. Furthermore, they are close to the Fibonacci numbers and Lucas numbers. Therefore, a large number of scholars have investigated them and get many properties and identities.

    In the aspect of sums of reciprocals, Millin [1] originally studied the infinite sums of reciprocal Fibonacci series where the subscript is 2n. Based on the initial achievement, Good [2] further studied this issue and proved

    n=01F2n=752.

    Afterwards, Ohtsuka and Nakamura [3] deduced the infinite sum of reciprocal Fibonacci series

    (k=n1Fk)1={FnFn1,if n is even and n2 ,FnFn11,if n is odd and n1;

    and the infinite sum of reciprocal square Fibonacci series

    (k=n1F2k)1={FnFn11,if n is even and n2 ,FnFn1,if n is odd and n1;

    Similar properties were investigated in several different ways, see reference [4,5]. Falcón and Plaza [6,7,8] used Fibonacci polynomials to study Fibonacci numbers and get a lot of identities. For example,

    n1k=1Fk(x)Fnk(x)=(n1)xFn(x)+2nFn1(x)x2+4,
    nk=1Fk(x)=Fn+1(x)+Fn(x)1x

    where n and k are positive integers. This fact allows them to invest some integer sequences in a new and direct way. With these fundamental achievements, Wu and Zhang[9] proceeded generation and deduced the the infinite sum of reciprocal Fibonacci polynomials

    (k=n1Fk(x))1={Fn(x)Fn1(x),if n is even and n2 ,Fn(x)Fn1(x)1,if n is odd and n1;

    and the the infinite sum of reciprocal square Fibonacci polynomials

    (k=n1F2k(x))1={xFn(x)Fn1(x)1,if n is even and n2 ,xFn(x)Fn1(x),if n is odd and n1;

    where x is any positive integer. besides, Panda et al.[10] did some research about bounds for reciprocal sums in terms of balancing and Lucas-balancing sequences. Also, Dutta and Ray[11] found some identities about finite reciprocal sums of Fibonacci and Lucas polynomials.

    As we know, the first and the second kind of Chebyshev polynomials are usually defined as follows: Tn+2(x)=2xTn+1(x)Tn(x), n0, with the initial values T0(x)=1, T1(x)=x; Un+2(x)=2xUn+1(x)Un(x), n0, with the initial values U0(x)=1, U1(x)=2x; Then from the second-order linear recurrence sequences we have

    Tn(x)=12[(x+x21)n+(xx21)n],Un(x)=12x21[(x+x21)n+1(xx21)n+1].

    Based on these sequences, many scholars used these polynomials to study the Fibonacci sequences and the Lucas sequences and have investigated them and got many properties of Fn and Ln. For example, Zhang[12] used the Chebyshev polynomials and has obtain the general formulas involving Fn and Ln

    a1+a2++ak+1=nFm(a1+1)Fm(a2+1)Fm(ak+1+1)=(i)mnFk+1m2kk!U(k)n+k(imLm2).
    a1+a2++ak+1=n+k+1Lm(a1+1)Lm(a2+1)Lm(ak+1+1)
    =(i)m(n+k+1)2k!k+1h=0(im+2Lm2)h(k+1)!h!(k+1h)!U(k)n+2k+1h(imLm2),

    where k, m are any positive integers, a1, a2, ak+1 are nonnegative integers and i is the square root of 1. Wu and Yang[13] also studied Chebyshev polynomials and got a lot of properties. Besides, Dilcher and Stolarsky[14] established several related results involving resultants and discriminants about Chebyshev polynomials. Furthermore, bounds about the discriminant of the Chebyshev polynomials were given by Filipovski[15].

    A variety of sums about Chebyshev polynomials are hot issues in the number theory all the time. For example, Cesarano [16] gained several conclusions about the generating function of Chebyshev polynomials

    n=0ξnTn+l(x)=(1ξx)Tl(x)ξ(1x2)Ul112ξx+ξ2

    and the identical equation

    n=0ξnUn1+l(x)=ξTl(x)(1ξx)Ul112ξx+ξ2

    In this, ξ is a real number and 1<ξ<1. Furthermore, Knopfmacher et al.[17] did some research and got the result as follows:

    1Um(x)=1m+1mj=1(1)j+1sin2θjxcosθj

    and the identical equation

    1+Um1(x)Um(x)=1m+1mj=1[1+(1)j+1]sin2θjxcosθj,

    where θj=jπm+1, m is a positive integer.

    In this paper, we combine Ohtsuka and Falcón's ideas. Then we consider the subseries of infinite sums derived from the reciprocals of the Chebyshev polynomials and prove the following:

    Theorem 1. For any positive integer n, m and x, we have the following formula

    (k=n1Tmk(x))1=Tmn(x)Tmnm(x)

    Theorem 2. For any positive integer n, and x, we have the following formula

    (k=n1Uk(x))1=Un(x)Un1(x)1.

    Theorem 3. For any positive integer n, and x, we have the following formula

    (k=n1U2k(x))1=U2n(x)U2n1(x)1.

    With Falcón's enlightening, we can apply similar method into deduction of partial sums of Chebyshev polynomials. For convenient expression, we firstly set

    Gn(x)=Un1(x)Un(x)+Un1(x)Un+1(x)
    Mn(x)=Un2(x)Un(x)+Un1(x)Un(x)

    and obtain:

    Theorem 4. For any positive integer n,

    2n+1k=0k2Uk(x)=12(2n+1)U2n+1(x)Gn(x)+1+(n+1)U2n+2(x)+Mn(x).

    Theorem 5. For any positive integer n,

    2nk=0k2Tk(x)=12(2n+1)2U2n1(x)U2n(x)+2(n+1)2U2n+2(x)U2n+1(x).

    Theorem 6. For any positive integer n,

    2nk=0k3Tk(x)=4(n+1)3U2n+2(x)+12(2n+1)3U2n+1(x)Gn(x)(3n+32)U2n1(x)3nU2n(x)+3Mn(x)1.

    In order to prove the results of the infinite sums of reciprocal Chebyshev polynomials, several lemmas are needed.

    Let α=x+x21 and β=xx21, then we have the following lemmas.

    Lemma 1. For any positive integer n, we have

    U2n(x)=1+Un1(x)Un+1(x),
    U2n(x)=4x2+Un2(x)Un+2(x).

    Proof. From the definition of Chebyshev polynomials, we have

    U2n(x)Un1(x)Un+1(x)=(αn+1βn+1)2(αnβn)(αn+2βn+2)(αβ)2=α2n+2+β2n+2+α2+β22α2n+2β2n+2(αβ)2=1.U2n(x)Un2(x)Un+2(x)=(αn+1βn+1)2(αn1βn1)(αn+3βn+3)(αβ)2=α2n+2+β2n+2+α4+β42α2n+2β2n+2(αβ)2=(α+β)2=4x2.

    Lemma 2. For any positive integer n, we have

    T2n(x)=Tn1(x)Tn+1(x)+1x2,
    T2n(x)=Tn2(x)Tn+2(x)+4x2(1x2).

    Proof. From the definition of Chebyshev polynomials, we have

    T2n(x)Tn1(x)Tn+1(x)=14[(αn+βn)2(αn1+βn1)(αn+1+βn+1)]=14[α2+β22]=14(αβ)2=1x2.T2n(x)Tn2(x)Tn+2(x)=14[(αn+βn)2(αn2+βn2)(αn+2+βn+2)]=14[α2n+β2nα4β4+2α2nβ2n]=14(α+β)2(αβ)2=4x2(1x2).

    Lemma 3. For any positive integer n and m, we have

    Tn(Tm(x))=Tnm(x),
    Un(Tm(x))=Um(n+1)1(x)Um1(x).

    Proof. See Reference [12].

    Lemma 4. For any positive integer n and x, we have

    1Tn(x)+1Tn+1(x)<1Tn(x)Tn1(x)1Tn+2(x)Tn+1(x),1Tn(x)+1Tn+1(x)>1Tn(x)Tn1(x)+11Tn+2(x)Tn+1(x)+1.

    Proof. The first inequality equivalent to

    Tn(x)+Tn+1(x)Tn(x)Tn+1(x)<Tn+2(x)Tn+1(x)Tn(x)+Tn1(x)(Tn(x)Tn1(x))(Tn+2(x)Tn+1(x)), (2.1)

    or

    [Tn(x)+Tn+1(x)](Tn(x)Tn1(x)+1)(Tn+2(x)Tn+1(x)+1)<Tn(x)Tn+1(x)[Tn+2(x)Tn+1(x)Tn(x)+Tn1(x)],

    Then we have

    T2n(x)Tn+2(x)+T2n+1(x)Tn1(x)<Tn1(x)Tn+2(x)Tn(x)+Tn1(x)Tn+2(x)Tn+1(x),

    applying Lemma 2, inequality (2.1) is equivalent to

    (1x2)[Tn1(x)+Tn+2(x)]<0. (2.2)

    For any positive x and n1, 1x2<0 and Tn1(x)+Tn+2(x)>0. Thus it is very easy to check inequality (2.2) is true. Similarly, we can consider the second inequality of Lemma 4. The second inequality is equivalent to

    Tn(x)+Tn+1(x)Tn(x)Tn+1(x)>Tn+2(x)Tn+1(x)Tn(x)+Tn1(x)(Tn(x)Tn1(x)+1)(Tn+2(x)Tn+1(x)+1), (2.3)

    or

    T2n(x)Tn+2(x)Tn1(x)Tn(x)Tn+2(x)Tn1(x)Tn+1(x)Tn+2(x)+Tn(x)Tn+2(x)+Tn+1(x)Tn+2(x)+T2n+1(x)Tn1(x)T2n+1(x)+T2n(x)Tn(x)Tn1(x)Tn+1(x)Tn1(x)+Tn(x)+Tn+1(x)>0,

    applying Lemma 2, inequality (2.3) is equivalent to

    (Tn+1(x)(x21))Tn+2(x)(Tn(x)+(x21))Tn1(x)+Tn(x)+Tn+1(x)>0. (2.4)

    For any positive x and n1,

    (Tn+1(x)(x21))Tn+2(x)(Tn(x)+(x21))Tn1(x)>0

    Thus it is very easy to check inequality (2.4) is true.

    Lemma 5. For any positive integer n and x,

    1Un(x)+1Un+1(x)>1Un(x)Un1(x)1Un+2(x)Un+1(x),1Un(x)+1Un+1(x)<1Un(x)Un1(x)11Un+2(x)Un+1(x)1.

    Prove. The first inequality is equivalent to

    Un+1(x)+Un(x)Un+1(x)Un(x)>Un+2(x)Un+1(x)Un(x)+Un1(x)(Un(x)Un1(x))(Un+2(x)Un+1(x)), (2.5)

    or

    [Un+1(x)+Un(x)](Un(x)Un1(x))(Un+2(x)Un+1(x))>Un+1(x)Un(x)[Un+2(x)Un+1(x)Un(x)+Un1(x)],

    Then we have

    U2n(x)Un+2(x)+U2n+1(x)Un1(x)>Un(x)Un+2(x)Un1(x)+Un1(x)Un+1(x)Un+2(x),

    applying Lemma 1, inequality (2.5) is equivalent to

    Un+2(x)+Un1(x)>0. (2.6)

    For any positive x and n1, it is very easy to check inequality (2.6) is true. Similarly, we can consider the second inequality of Lemma 5.

    Un+1(x)+Un(x)Un+1(x)Un(x)<Un+2(x)Un+1(x)Un(x)+Un1(x)(Un(x)Un1(x)1)(Un+2(x)Un+1(x)1), (2.7)

    or

    U2n(x)Un+2(x)Un1(x)Un(x)Un+2(x)Un1(x)Un+1(x)Un+2(x)Un(x)Un+2(x)Un+1(x)Un+2(x)+U2n+1(x)Un1(x)+U2n+1(x)U2n(x)+Un(x)Un1(x)+Un+1(x)Un1(x)+Un(x)+Un+1(x)<0,

    applying Lemma 1, inequality (2.7) equivalent to

    Un+2(x)+Un1(x)+Un(x)Un1(x)+Un(x)+Un+1(x)<Un+1(x)Un+2(x). (2.8)

    For any positive x and n1, it is very easy to check inequality (2.8) is true.

    Lemma 6. For any positive integers n and x, we have

    1U2n(x)+1U2n+1(x)>1U2n(x)U2n1(x)1U2n+2(x)U2n+1(x),1U2n(x)+1U2n+1(x)<1U2n(x)U2n1(x)11U2n+2(x)U2n+1(x)1.

    Proof. The first inequality is equivalent to

    U2n(x)+U2n+1(x)U2n(x)U2n+1(x)>U2n+2(x)U2n+1(x)U2n(x)+U2n1(x)(U2n+2(x)U2n+1(x))(U2n(x)U2n1(x)), (2.9)

    or

    [U2n(x)+U2n+1(x)](U2n+2(x)U2n+1(x))(U2n(x)U2n1(x))>U2n(x)U2n+1(x)[U2n+2(x)U2n+1(x)U2n(x)+U2n1(x)],

    Then we have

    U4n(x)U2n+2(x)U2n(x)U2n+2(x)U2n1(x)U2n+1(x)U2n+2(x)U2n1(x)+U4n+1(x)U2n1(x)>0,

    applying Lemma 1, inequality (2.9) is equivalent to

    U2n+2(x)+2Un1(x)Un+1(x)U2n+2(x)+U2n1(x)+2Un(x)Un+2(x)U2n1(x)>0. (2.10)

    For any positive x and n1, it is very easy to check inequality (2.10) is true. Similarly, we can consider the second inequality of Lemma 6. The second inequality is equivalent to

    U2n(x)+U2n+1(x)U2n(x)U2n+1(x)<U2n+2(x)U2n+1(x)U2n(x)+U2n1(x)(U2n+2(x)U2n+1(x)1)(U2n(x)U2n1(x)1), (2.11)

    or

    U4n(x)U2n+2(x)U4n(x)U2n(x)U2n+2(x)U2n1(x)U2n+1(x)U2n+2(x)U2n1(x)+U4n+1(x)U2n1(x)+U2n(x)U2n1(x)+U2n+1(x)U2n1(x)U2n(x)U2n+2(x)U2n+1(x)U2n+2(x)+U4n+1(x)+U2n(x)+U2n+1(x)<0,

    applying Lemma 1, inequality (2.11) is equivalent to

    U2n(x)U2n1(x)+U2n(x)+U2n+1(x)+U2n+2(x)+2Un1(x)Un+1(x)U2n+2(x)+U2n1(x)
    2Un(x)Un+2(x)U2n1(x)+2Un(x)Un+2(x)<U2n+1(x)U2n+2(x)+2Un1(x)Un+1(x). (2.12)

    For any positive x and n1, it is very easy to check inequality (2.12) is true.

    Aiming to prove the results of the partial sums of Chebyshev polynomials, the lemmas below are necessary.

    Lemma 7. For any positive integer n2

    Tn(x)=12Un(x)12Un2(x)nk=1Tk(x)=12Un(x)+12Un1(x)12

    Prove. The general term formula of Chebyshev polynomials is as follows

    Tn(x)=12[(x+x21)n+(xx21)n]Un(x)=12x21[(x+x21)n+1(xx21)n+1]

    For convenient proving, we set α=x+x21, β=xx21, and easily verify α+β=2x, αβ=1. Thus, according to the definition we get

    12Un(x)12Un2(x)=12(αn+1βn+1αβαn1βn1αβ)=12(αβ)[αn1(α21)βn1(β21)]=12(αβ)[αn1(α2αβ)βn1(β2αβ)]=12(αβ)[αn(αβ)+βn(αβ)]=12(αn+βn).

    This proves the first equation. And next we prove the second equation

    nk=1Tk(x)=12nk=2Uk(x)12nk=2Uk2(x)+T1(x)=12nk=2Uk(x)12n2k=0Uk(x)+T1(x)=12Un(x)+12Un1(x)T1(x)12+T1(x)=12Un(x)+12Un1(x)12.

    This proves Lemma 7.

    Lemma 8. For any positive integer n

    2nk=1Uk(x)=Un1(x)Un(x)+Un1(x)Un+1(x), (2.13)
    2n1k=1Uk(x)=Un2(x)Un(x)+Un1(x)Un(x). (2.14)

    Prove. In accordance of the general term formula of Chebyshev polynomials, it is not hard to get

    U2n+1(x)=Un(x)Un+1(x)Un(x)Un1(x),U2n+2(x)=U2n+1(x)Un+1(x)Un1(x)1,U2n+1(x)=Un+2(x)Un(x)+1.

    Easily test that when n=1, identical Eq (2.13) is right. Supposing that n=m, Eq (2.13) is right. Then when n=m+1,

    2m+2k=1Uk(x)=Um1(x)Um+1(x)+Um1(x)Um(x)+U2m+1(x)+U2m+2(x)=Um+1(x)Um(x)+U2m+11=Um(x)Um+2(x)+Um+1(x)Um(x).

    Applying mathematical induction, it is not hard to prove identical Eq (2.14). This proves Lemma 8.

    Lemma 9. For any positive integers n,

    2nk=0kTk(x)=12(2n+1)U2n1(x)+(n+1)U2n(x)Gn(x)2nk=0kUk(x)=12Un+1(x)+12Un(x)Gn(x)

    Prove. According to Lemma 7, we have

    n+1k=0Tk(x)=Un+1(x)+Un(x)+12.

    Through derivation on the left and right sides, we get

    nk=0(k+1)Uk(x)=Un+1(x)+Un(x)2.

    Applying Lemma 7 and Lemma 8, we obtain

    2nk=1kUk(x)=12U2n+1(x)+12U2n(x)2nk=1Uk(x)=12U2n+1(x)+12U2n(x)Gn(x)2nk=1kTk(x)=x+122nk=2kUk(x)122nk=2kUk2(x)=x+122nk=2kUk(x)122n2k=0(k+2)Uk(x)=(n+12)U2n1(x)+(n+1)U2n(x)2nk=1Uk(x)=(n+12)U2n1(x)+(n+1)U2n(x)Gn(x).

    This proves Lemma 9.

    In this section, we will prove our theorems. For the infinite sums of reciprocal Chebyshev polynomials, firstly we prove Theorem 1. For any positive integer n and x, using Lemma 4, we have

    k=n1Tk(x)=k=s(1T2k1(x)+1T2k(x))<k=s(1T2k1(x)T2k2(x)1T2k+1(x)T2k(x))=1Tn(x)Tn1(x)

    In the similar way, we have

    k=n1Tk(x)=k=s(1T2k1(x)+1T2k(x))>k=s(1T2k1(x)T2k2(x)+11T2k+1(x)T2k(x)+1)=1Tn(x)Tn1(x)+1.

    And then we have

    (k=n1Tk(x))1=Tn(x)Tn1(x)

    and then let x=Tm(x), according to Lemma 3, we can get

    (k=n1Tmk(x))1=Tmn(x)Tmnm(x)

    This proved Theorem 1.

    Next, Theorem 2 will be proved. For any positive integer n and x, using Lemma 5, we have

    k=n1Uk(x)=k=s(1U2k1(x)+1U2k(x))<k=s(1U2k1(x)U2k2(x)11U2k+1(x)U2k(x)1)=1Un(x)Un1(x)1.

    In the similar way, we have

    k=n1Uk(x)=k=s(1U2k1(x)+1U2k(x))>k=s(1U2k1(x)U2k2(x)1U2k+1(x)U2k(x))=1Un(x)Un1(x).

    And then we have

    Un(x)Un1(x)1<(k=n1Uk(x))1<Un(x)Un1(x).

    that is

    (k=n1Uk(x))1=Un(x)Un1(x)1.

    This proved Theorem 2.

    Then we shall prove Theorem 3. Using Lemma 6, we can get

    k=n1U2k(x)=k=s(1U22k1(x)+1U22k(x))<k=s(1U22k1(x)U22k2(x)11U22k+1(x)U22k(x)1)=1U2n(x)U2n1(x)1.

    In the similar way, we have

    k=n1U2k(x)=k=s(1U22k1(x)+1U22k(x))>k=s(1U22k1(x)U22k2(x)1U22k+1(x)U22k(x))=1U2n(x)U2n1(x)

    and then we can get

    (k=n1U2k(x))1=U2n(x)U2n1(x)1.

    This proved Theorem 3.

    For the partial sums of Chebyshev polynomials, firstly we shall prove Theorem 4. According to Lemma 9, we have

    2n+2k=0kTk(x)=12(2n+3)U2n+1(x)+(n+2)U2n+2(x)Gn+1(x)

    Through simultaneous derivation on the left and right sides, we deduce

    2n+1k=1k2Uk1(x)=12(2n+3)U2n+1(x)+(n+2)U2n+2(x)Gn(x).

    According to Lemma 8 and Lemma 9 we get

    2n+1k=0k2Uk(x)=2n+2k=1k2Uk1(x)22n+1k=1kUk(x)2n+1k=0Uk(x)=2n+2k=1k2Uk1(x)22n+1k=1(k+1)Uk(x)+2n+1k=0Uk(x)=(n+12)U2n+1(x)+(n+1)U2n+2(x)Gn(x)+Mn(x)+1.

    Applying Lemma 7 and Lemma 8, we get

    2nk=0k2Tk(x)=x+122nk=2k2Uk(x)122nk=2k2Uk2(x)=x+122nk=2k2Uk(x)122n2k=0(k+2)2Uk(x).

    Simplify the above, we have

    2nk=0k2Tk(x)=12(2n+1)2U2n1(x)+2(n+1)2U2n+2(x)U2n+1(x)U2n(x)

    This proved Theorem 4 and Theorem 5.

    Theorem 6 shall be proved below. According to Lemma 7 and Lemma 8, we have

    2nk=0k3Tk(x)=x+122nk=2k3Uk(x)122nk=2k3Uk2(x)=x+122nk=2k3Uk(x)122n2k=0(k+2)3Uk(x)=2n2k=2(3k2+6k+4)Uk(x)+12(2n+1)3Un(x)+4(n+1)3U2n+2(x)26x4=4(n+1)3U2n+2(x)+12(2n+1)3U2n+1(x)Gn(x)(3n+32)U2n1(x)3nU2n(x)+3Mn(x)1.

    This proved Theorem 6.

    In this paper, the infinite sums of reciprocals and the partial sums derived from Chebyshev polynomials are studied. For the infinite sums of reciprocals, we apply the floor function to the reciprocals of these sums to obtain Theorem 1, Theorem 2 and Theorem 3 involving the Chebyshev polynomials. Simultaneously, we get Theorem 4, Theorem 5 and Theorem 6 about the partial sums of Chebyshev polynomials by the relation of two types of Chebyshev polynomials. Our results can enrich the related research domain with respect to orthogonal polynomials and recursive sequences. Besides, the results are hoped to be applied into other branches of mathematics or other disciplines out of mathematics.

    The authors would like to thank Xi'an Shiyou University for the support of this research.

    The authors declare there is no conflicts of interest in this paper.



    [1] A. S. Al-Goblan, M. A. Al-Alfi, M. Z. Khan. Mechanism linking diabetes mellitus and obesity, Diabetes Metab. Syndr. Obes., 7 (2014), 587–591. doi: 10.2147/dmso.S67400 doi: 10.2147/dmso.S67400
    [2] A. A. Rao, N. M. Tayaru, H. Thota, S. B. Changalasetty, L. S. Thota, S. Gedela, Bioinformatic analysis of functional proteins involved in obesity associated with diabetes, Int. J. Biomed. Sci., 4 (2008), 70–73.
    [3] P. E. Scherer, J. A. Hill, Obesity, diabetes, and cardiovascular diseases: A compendium, Circ. Res., 118 (2016), 1703–1705. doi: 10.1161/circresaha.116.308999 doi: 10.1161/circresaha.116.308999
    [4] G. R. Babu, G. V. S. Murthy, Y. Ana, P. Patel, R. Deepa, S. E. B. Neelon, et al. Association of obesity with hypertension and type 2 diabetes mellitus in India: A meta-analysis of observational studies, World J. Diabetes, 9 (2018), 40–52. doi: 10.4239/wjd.v9.i1.40 doi: 10.4239/wjd.v9.i1.40
    [5] A. Medina-Remón, R. Kirwan, R. M. Lamuela-Raventós, R. Estruch. Dietary patterns and the risk of obesity, type 2 diabetes mellitus, cardiovascular diseases, asthma, and neurodegenerative diseases, Crit. Rev. Food Sci. Nutr., 58 (2018), 262–296. doi: 10.1080/10408398.2016.1158690 doi: 10.1080/10408398.2016.1158690
    [6] G. A. Bray, Medical consequences of obesity, J. Clin. Endocrinol. Metab., 89 (2004), 2583–2589. doi: 10.1210/jc.2004-0535 doi: 10.1210/jc.2004-0535
    [7] J. S. M. Sabir, A. El Omri, B. Banaganapalli, N. Aljuaid, A. M. S. Omar, A. Altaf, et al., Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis, PLoS One, 15 (2020), e0228400. doi: 10.1371/journal.pone.0228400 doi: 10.1371/journal.pone.0228400
    [8] M. B. Zimering, V. Delic, B. A. Citron, Gene expression changes in a model neuron cell line exposed to autoantibodies from patients with traumatic brain injury and/or Type 2 diabetes, Mol. Neurobiol., (2021). doi: 10.1007/s12035-021-02428-4 doi: 10.1007/s12035-021-02428-4
    [9] T. O. Kilpeläinen, T. A. Lakka, D. E. Laaksonen, J. Lindström, J. G. Eriksson, T. T. Valle, et al., SNPs in PPARG associate with type 2 diabetes and interact with physical activity, Med. Sci. Sports Exerc., 40 (2008), 25–33. doi: 10.1249/mss.0b013e318159d1cd doi: 10.1249/mss.0b013e318159d1cd
    [10] J. J. Jia, X. Zhang, C. R. Ge, M. Jois, The polymorphisms of UCP2 and UCP3 genes associated with fat metabolism, obesity and diabetes, Obes. Rev., 10 (2009), 519–526. doi: 10.1111/j.1467-789X.2009.00569.x doi: 10.1111/j.1467-789X.2009.00569.x
    [11] D. Meyre, N. Bouatia-Naji, A. Tounian, C. Samson, C. Lecoeur, V. Vatin, et al., Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes, Nat. Genet., 37 (2005), 863–867. doi: 10.1038/ng1604 doi: 10.1038/ng1604
    [12] T. M. Frayling, N. J. Timpson, M. N. Weedon, E. Zeggini, R. M. Freathy, C. M. Lindgren, et al., A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity, Science, 316 (2007), 889–894. doi: 10.1126/science.1141634 doi: 10.1126/science.1141634
    [13] M. Hong, S. Tao, L. Zhang, L.-T. Diao, X. Huang, S. Huang, et al., RNA sequencing: New technologies and applications in cancer research, J. Hematol. Oncol., 13 (2020), 166. doi: 10.1186/s13045-020-01005-x doi: 10.1186/s13045-020-01005-x
    [14] G. Laenen, L. Thorrez, D. Börnigen, Y. Moreau, Finding the targets of a drug by integration of gene expression data with a protein interaction network, Mol. Biosyst., 9 (2013), 1676–1685. doi: 10.1039/c3mb25438k doi: 10.1039/c3mb25438k
    [15] R. Roy, L. N. Winteringham, T. Lassmann, A. R. R. Forrest. Expression levels of therapeutic targets as indicators of sensitivity to targeted therapeutics, Mol. Cancer Ther., 18 (2019), 2480–2489. doi: 10.1158/1535-7163.Mct-19-0273 doi: 10.1158/1535-7163.Mct-19-0273
    [16] R. Edgar, M. Domrachev, A. E. Lash, Gene expression omnibus: NCBI gene expression and hybridization array data repository, Nucleic Acids Res., 30 (2002), 207–210. doi: 10.1093/nar/30.1.207 doi: 10.1093/nar/30.1.207
    [17] S. W. Wingett, S. Andrews, FastQ screen: A tool for multi-genome mapping and quality control, F1000Res, 7 (2018), 1338. doi: 10.12688/f1000research.15931.2 doi: 10.12688/f1000research.15931.2
    [18] A. M. Bolger, M. Lohse, B. Usadel, Trimmomatic: A flexible trimmer for Illumina sequence data, Bioinformatics, 30 (2014), 2114–2120. doi: 10.1093/bioinformatics/btu170 doi: 10.1093/bioinformatics/btu170
    [19] A. Dobin, C. A. Davis, F. Schlesinger, J. Drenkow, C. Zaleski, S. Jha, et al., STAR: Ultrafast universal RNA-seq aligner, Bioinformatics, 29 (2013), 15–21. doi: 10.1093/bioinformatics/bts635 doi: 10.1093/bioinformatics/bts635
    [20] Y. Liao, G. K. Smyth, W. Shi, featureCounts: An efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics, 30 (2014), 923–930. doi: 10.1093/bioinformatics/btt656 doi: 10.1093/bioinformatics/btt656
    [21] M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15 (2014), 550. doi: 10.1186/s13059-014-0550-8 doi: 10.1186/s13059-014-0550-8
    [22] R. Kolde, pheatmap: Pretty Heatmaps. R package version 1.0. 8, in, Available, 2015.
    [23] K. Blighe, S. Rana, M. Lewis, EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling (2019), R Package Version, 1 (2018).
    [24] M. H. Schaefer, J. F. Fontaine, A. Vinayagam, P. Porras, E. E. Wanker, M. A. Andrade-Navarro, HIPPIE: Integrating protein interaction networks with experiment based quality scores, PLoS One, 7 (2012), e31826. doi: 10.1371/journal.pone.0031826 doi: 10.1371/journal.pone.0031826
    [25] G. Alanis-Lobato, M. A. Andrade-Navarro, M. H. Schaefer, HIPPIE v2.0: Enhancing meaningfulness and reliability of protein-protein interaction networks, Nucleic Acids Res., 45 (2017), D408–D414. doi: 10.1093/nar/gkw985 doi: 10.1093/nar/gkw985
    [26] P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498–2504. doi: 10.1101/gr.1239303 doi: 10.1101/gr.1239303
    [27] Y. Tang, M. Li, J. Wang, Y. Pan, F. X. Wu. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks, Biosystems, 127 (2015), 67–72. doi: 10.1016/j.biosystems.2014.11.005 doi: 10.1016/j.biosystems.2014.11.005
    [28] S. Wasserman, K. Faust, Social network analysis: Methods and applications, (1994).
    [29] S. P. Borgatti, Centrality and network flow, Social networks, 27 (2005), 55–71. doi: 10.1016/j.socnet.2004.11.008 doi: 10.1016/j.socnet.2004.11.008
    [30] L. C. Freeman, Centrality in social networks conceptual clarification, Social networks, 1 (1978), 215–239. doi: 10.1016/0378-8733(78)90021-7 doi: 10.1016/0378-8733(78)90021-7
    [31] M. E. Newman, The mathematics of networks, The new palgrave encyclopedia of economics, 2 (2008), 1–12.
    [32] G. George, S. Valiya Parambath, S. B. Lokappa, J. Varkey, Construction of Parkinson's disease marker-based weighted protein-protein interaction network for prioritization of co-expressed genes, Gene, 697 (2019), 67–77. doi: 10.1016/j.gene.2019.02.026 doi: 10.1016/j.gene.2019.02.026
    [33] C. Durón, Y. Pan, D. H. Gutmann, J. Hardin, A. Radunskaya, Variability of betweenness centrality and its effect on identifying essential genes, Bull. Math. Biol., 81 (2019), 3655–3673. doi: 10.1007/s11538-018-0526-z doi: 10.1007/s11538-018-0526-z
    [34] J. Chen, E. E. Bardes, B. J. Aronow, A. G. Jegga, ToppGene Suite for gene list enrichment analysis and candidate gene prioritization, Nucleic Acids Res., 37 (2009), W305–311. doi: 10.1093/nar/gkp427 doi: 10.1093/nar/gkp427
    [35] C. S. Greene, A. Krishnan, A. K. Wong, E. Ricciotti, R. A. Zelaya, D. S. Himmelstein, et al., Understanding multicellular function and disease with human tissue-specific networks, Nat. Genet., 47 (2015), 569–576. doi: 10.1038/ng.3259 doi: 10.1038/ng.3259
    [36] G. Koscielny, P. An, D. Carvalho-Silva, J. A. Cham, L. Fumis, R. Gasparyan, et al., Open Targets: A platform for therapeutic target identification and validation, Nucleic Acids Res., 45 (2017), D985–d994. doi: 10.1093/nar/gkw1055 doi: 10.1093/nar/gkw1055
    [37] C. L. Haase, A. Tybjærg-Hansen, B. G. Nordestgaard, R. Frikke-Schmidt, HDL cholesterol and risk of Type 2 diabetes: A mendelian randomization study, Diabetes, 64 (2015), 3328–3333. doi: 10.2337/db14-1603 doi: 10.2337/db14-1603
    [38] M. A. Javed Shaikh, R. S. H. Singh, S. Rawat, S. Pathak, A. Mishra, et al., Role of various gene expressions in etiopathogenesis of Type 2 diabetes mellitus, Adv. Mind. Body Med., 35 (2021), 31 –39. PMID: 34237027.
    [39] T. Liu, J. Liu, L. Hao, Network pharmacological study and molecular docking analysis of qiweitangping in treating diabetic coronary heart disease, Evid. Based Complement. Alternat. Med., 2021 (2021), 9925556. doi: 10.1155/2021/9925556 doi: 10.1155/2021/9925556
    [40] N. N. Sahly, B. Banaganapalli, A. N. Sahly, A. H. Aligiraigri, K. K. Nasser, T. Shinawi, et al., Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches, Syst. Biol. Reprod. Med., 67 (2021), 209–220. doi: 10.1080/19396368.2021.1876179 doi: 10.1080/19396368.2021.1876179
    [41] B. Banaganapalli, N. Al-Rayes, Z. A. Awan, F. A. Alsulaimany, A. S. Alamri, R. Elango, et al., Multilevel systems biology analysis of lung transcriptomics data identifies key miRNAs and potential miRNA target genes for SARS-CoV-2 infection, Comput. Biol. Med., 135 (2021), 104570. doi: 10.1016/j.compbiomed.2021.104570 doi: 10.1016/j.compbiomed.2021.104570
    [42] A. Mujalli, B. Banaganapalli, N. M. Alrayes, N. A. Shaik, R. Elango, J. Y. Al-Aama, Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis, Genomics, 112 (2020), 5072–5085. doi: 10.1016/j.ygeno.2020.09.004 doi: 10.1016/j.ygeno.2020.09.004
    [43] T. Ideker, R. Nussinov, Network approaches and applications in biology, PLoS Comput. Biol., 13 (2017), e1005771–e1005771. doi: 10.1371/journal.pcbi.1005771 doi: 10.1371/journal.pcbi.1005771
    [44] D. O. Holland, B. H. Shapiro, P. Xue, M. E. Johnson, Protein-protein binding selectivity and network topology constrain global and local properties of interface binding networks, Sci. Rep., 7 (2017), 5631. doi: 10.1038/s41598-017-05686-2 doi: 10.1038/s41598-017-05686-2
    [45] Y. Gao, X. Chang, J. Xia, S. Sun, Z. Mu, X. Liu, Identification of HCC-related genes based on differential partial correlation network, Front Genet, 12 (2021), 672117. doi: 10.3389/fgene.2021.672117 doi: 10.3389/fgene.2021.672117
    [46] C. Liu, L. Lu, Q. Kong, Y. Li, H. Wu, W. Yang, et al., Developing discriminate model and comparative analysis of differentially expressed genes and pathways for bloodstream samples of diabetes mellitus type 2, BMC Bioinform., 15 Suppl 17 (2014), S5. doi: 10.1186/1471-2105-15-s17-s5 doi: 10.1186/1471-2105-15-s17-s5
    [47] G. Prashanth, B. Vastrad, A. Tengli, C. Vastrad, I. Kotturshetti, Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules, BMC Endocr. Disord., 21 (2021), 80. doi: 10.1186/s12902-021-00718-5 doi: 10.1186/s12902-021-00718-5
    [48] X. Yao, J. Yan, K. Liu, S. Kim, K. Nho, S. L. Risacher, et al., Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules, Bioinformatics, 33 (2017), 3250–3257. doi: 10.1093/bioinformatics/btx344 doi: 10.1093/bioinformatics/btx344
    [49] R. L. J. van Meijel, E. E. Blaak, G. H. Goossens, Chapter 1 - Adipose tissue metabolism and inflammation in obesity, in: R. A. Johnston, B. T. Suratt (Eds.), Mechanisms and Manifestations of Obesity in Lung Disease, Academic Press, 2019, pp. 1–22.
    [50] C. Fotis, A. Antoranz, D. Hatziavramidis, T. Sakellaropoulos, L. G. Alexopoulos, Network-based technologies for early drug discovery, Drug Discovery Today, 23 (2018), 626–635. doi: 10.1016/j.drudis.2017.12.001 doi: 10.1016/j.drudis.2017.12.001
    [51] J. M. Fernandez-Real, J. A. Menendez, J. M. Moreno-Navarrete, M. Blüher, A. Vazquez-Martin, M. J. Vázquez, et al., Extracellular fatty acid synthase: A possible surrogate biomarker of insulin resistance, Diabetes, 59 (2010), 1506–1511. doi: 10.2337/db09-1756 doi: 10.2337/db09-1756
    [52] A. Ray, Tumor-linked HER2 expression: Association with obesity and lipid-related microenvironment, Horm. Mol. Biol. Clin. Investig., 32 (2017). doi: 10.1515/hmbci-2017-0020 doi: 10.1515/hmbci-2017-0020
    [53] F. J. Ruiz-Ojeda, A. Méndez-Gutiérrez, C. M. Aguilera, J. Plaza-Díaz, Extracellular matrix remodeling of adipose tissue in obesity and metabolic diseases, Int. J. Mol. Sci., 20 (2019). doi: 10.3390/ijms20194888 doi: 10.3390/ijms20194888
    [54] P. Järgen, A. Dietrich, A. W. Herling, H. P. Hammes, P. Wohlfar, The role of insulin resistance in experimental diabetic retinopathy-Genetic and molecular aspects, PLoS One, 12 (2017), e0178658. doi: 10.1371/journal.pone.0178658 doi: 10.1371/journal.pone.0178658
    [55] M. C. Tse, X. Liu, S. Yang, K. Ye, C. B. Chan, Fyn regulates adipogenesis by promoting PIKE-A/STAT5a interaction, Mol. Cell. Biol., 33 (2013), 1797–1808. doi: 10.1128/mcb.01410-12 doi: 10.1128/mcb.01410-12
    [56] C. C. Bastie, H. Zong, J. Xu, B. Busa, S. Judex, I. J. Kurland, et al., Integrative metabolic regulation of peripheral tissue fatty acid oxidation by the SRC kinase family member Fyn, Cell Metab., 5 (2007), 371–381. doi: 10.1016/j.cmet.2007.04.005 doi: 10.1016/j.cmet.2007.04.005
    [57] E. Yamada, J. E. Pessin, I. J. Kurland, G. J. Schwartz, C. C. Bastie, Fyn-dependent regulation of energy expenditure and body weight is mediated by tyrosine phosphorylation of LKB1, Cell Metab., 11 (2010), 113–124. doi: 10.1016/j.cmet.2009.12.010 doi: 10.1016/j.cmet.2009.12.010
    [58] C. C. Bastie, H. H. Zong, J. Xu, S. Judex, I. J. Kurland, J. E. Pessin, Fyn kinase deficiency increases peripheral tissue insulin sensitivity by improving fatty acid oxidation and lipolysis, Diabetes, 56 (2007), A60.
    [59] J. Rodrigues-Krause, M. Krause, C. O'Hagan, G. De Vito, C. Boreham, C. Murphy, et al., Divergence of intracellular and extracellular HSP72 in type 2 diabetes: Does fat matter?, Cell Stress Chaperones, 17 (2012), 293–302. doi: 10.1007/s12192-011-0319-x doi: 10.1007/s12192-011-0319-x
    [60] P. L. Hooper, G. Balogh, E. Rivas, K. Kavanagh, L. Vigh, The importance of the cellular stress response in the pathogenesis and treatment of type 2 diabetes, Cell Stress Chaperones, 19 (2014), 447–464. doi: 10.1007/s12192-014-0493-8 doi: 10.1007/s12192-014-0493-8
    [61] E. Chang, M. Varghese, K. Singer, Gender and sex differences in adipose tissue, Curr. Diab. Rep., 18 (2018), 69. doi: 10.1007/s11892-018-1031-3 doi: 10.1007/s11892-018-1031-3
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3844) PDF downloads(189) Cited by(13)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog