Loading [MathJax]/jax/output/SVG/jax.js
Research article Topical Sections

Computational analysis reveals the therapeutic potential of Asiatic acid against the miRNA correlated differentially expressed genes of bipolar disorder

  • Received: 08 October 2023 Revised: 06 February 2024 Accepted: 28 February 2024 Published: 18 March 2024
  • Bipolar disorder is a psychiatric condition that consists of recurring episodes of severe mood swings between depression and manic episodes. The diagnosis is generally based on clinical interviews and observations, but is often misdiagnosed as unipolar depression, leading to significant delays in treatment. However, the disorder's heterogeneous nature and overlap with other psychiatric conditions, such as schizophrenia, present challenges in its diagnosis and treatment. To address these challenges, this study aims to explore the gene targets of differentially expressed miRNA associated with differentially expressed genes and to find a suitable phytochemical through molecular docking studies. The altered expression level of miRNAs (either increased or decreased) and genes had been observed to play a crucial role in different psychiatric disorders, thus suggesting their potential as biomarkers. The data of patients with bipolar disorder was retrieved from the Gene expression omnibus and Sequence read archive. The differentially expressed genes and miRNAs were identified through DESeq2 post processing. The gene targets of the downregulated miRNA and the upregulated genes were compared to identify the main targets of bipolar disorder. Furthermore, the phytochemicals with neuro-protective properties were identified through a literature study. The drug likeness property of each phytochemical was evaluated on the basis of Lipinski's rule of 5, followed by a toxicity evaluation. Molecular docking studies were carried out using AutoDock to determine the best drug against bipolar disorder. Therefore, the present study targets key proteins overexpressed in patients with bipolar disorder to facilitate a multi-faceted treatment approach.

    Citation: Harshita Maheshwari, Maitreyi Pathak, Prekshi Garg, Prachi Srivastava. Computational analysis reveals the therapeutic potential of Asiatic acid against the miRNA correlated differentially expressed genes of bipolar disorder[J]. AIMS Molecular Science, 2024, 11(2): 99-115. doi: 10.3934/molsci.2024007

    Related Papers:

    [1] Meraj Ali Khan, Ali H. Alkhaldi, Mohd. Aquib . Estimation of eigenvalues for the $ \alpha $-Laplace operator on pseudo-slant submanifolds of generalized Sasakian space forms. AIMS Mathematics, 2022, 7(9): 16054-16066. doi: 10.3934/math.2022879
    [2] Aliya Naaz Siddiqui, Meraj Ali Khan, Amira Ishan . Contact CR $ \delta $-invariant: an optimal estimate for Sasakian statistical manifolds. AIMS Mathematics, 2024, 9(10): 29220-29234. doi: 10.3934/math.20241416
    [3] Aliya Naaz Siddiqui, Mohammad Hasan Shahid, Jae Won Lee . On Ricci curvature of submanifolds in statistical manifolds of constant (quasi-constant) curvature. AIMS Mathematics, 2020, 5(4): 3495-3509. doi: 10.3934/math.2020227
    [4] Ayman Elsharkawy, Hoda Elsayied, Abdelrhman Tawfiq, Fatimah Alghamdi . Geometric analysis of the pseudo-projective curvature tensor in doubly and twisted warped product manifolds. AIMS Mathematics, 2025, 10(1): 56-71. doi: 10.3934/math.2025004
    [5] Rabia Cakan Akpınar, Esen Kemer Kansu . Metallic deformation on para-Sasaki-like para-Norden manifold. AIMS Mathematics, 2024, 9(7): 19125-19136. doi: 10.3934/math.2024932
    [6] Mohammad Aamir Qayyoom, Rawan Bossly, Mobin Ahmad . On CR-lightlike submanifolds in a golden semi-Riemannian manifold. AIMS Mathematics, 2024, 9(5): 13043-13057. doi: 10.3934/math.2024636
    [7] Nülifer Özdemir, Şirin Aktay, Mehmet Solgun . Some results on almost paracontact paracomplex Riemannian manifolds. AIMS Mathematics, 2025, 10(5): 10764-10786. doi: 10.3934/math.2025489
    [8] Amira Ishan . On concurrent vector fields on Riemannian manifolds. AIMS Mathematics, 2023, 8(10): 25097-25103. doi: 10.3934/math.20231281
    [9] Fatimah Alghamdi, Fatemah Mofarreh, Akram Ali, Mohamed Lemine Bouleryah . Some rigidity theorems for totally real submanifolds in complex space forms. AIMS Mathematics, 2025, 10(4): 8191-8202. doi: 10.3934/math.2025376
    [10] Carlo Morpurgo, Liuyu Qin . Moser-Trudinger inequalities on 2-dimensional Hadamard manifolds. AIMS Mathematics, 2024, 9(7): 19670-19676. doi: 10.3934/math.2024959
  • Bipolar disorder is a psychiatric condition that consists of recurring episodes of severe mood swings between depression and manic episodes. The diagnosis is generally based on clinical interviews and observations, but is often misdiagnosed as unipolar depression, leading to significant delays in treatment. However, the disorder's heterogeneous nature and overlap with other psychiatric conditions, such as schizophrenia, present challenges in its diagnosis and treatment. To address these challenges, this study aims to explore the gene targets of differentially expressed miRNA associated with differentially expressed genes and to find a suitable phytochemical through molecular docking studies. The altered expression level of miRNAs (either increased or decreased) and genes had been observed to play a crucial role in different psychiatric disorders, thus suggesting their potential as biomarkers. The data of patients with bipolar disorder was retrieved from the Gene expression omnibus and Sequence read archive. The differentially expressed genes and miRNAs were identified through DESeq2 post processing. The gene targets of the downregulated miRNA and the upregulated genes were compared to identify the main targets of bipolar disorder. Furthermore, the phytochemicals with neuro-protective properties were identified through a literature study. The drug likeness property of each phytochemical was evaluated on the basis of Lipinski's rule of 5, followed by a toxicity evaluation. Molecular docking studies were carried out using AutoDock to determine the best drug against bipolar disorder. Therefore, the present study targets key proteins overexpressed in patients with bipolar disorder to facilitate a multi-faceted treatment approach.



    In 1827, C. F. Gauss proved that if two smooth surfaces are isometric, then these surfaces have the same Gaussian curvature at corresponding points. Then the notion of curvature is one of the most attractive and significant topic in differential geometry. For a Riemannian submanifold, the main extrinsic curvature invariant is the squared mean curvature and the main intrinsic curvature invariants include classical curvature invariants namely the sectional curvature which corresponds to the Gaussian curvature in the Euclidean space, the Ricci curvature and the scalar curvature.

    In pseudo-Riemannian settings, there exist null plane sections which the sectional curvature map can not be defined as classically on these sections. Therefore, S. Harris [16] introduced the null sectional curvature of a null plane. Later, A. L. Albujer and S. Haesen [1] showed that the null sectional curvature is proportional to the difference in length of the two spacelike closing geodesics which is a generalization of the interpretation of the sectional curvature in the Riemannian case as was observed by T. Levi-Civita [20]. Furthermore, there exist very qualified papers dealing the notion of null sectional curvatures on pseudo-Riemannian manifolds and their submanifolds (cf. [5,9,10,11,13,17,18]).

    Another curvature invariant for pseudo-Riemannian manifolds and their submanifolds is the qualar curvature. Inspired by the crude mixture of terms quasi and scalar, M. Nardmann [22] introduced the notion of qualar curvature by decomposing scalar curvature. He also give some relations involving the qualar curvature and the scalar curvature for solving the Riemannian prescribed scalar curvature problem in a psedo-Riemannian manifold. Since the qualar curvature is a sum of sectional curvatures of some plane sections, one can consider that this curvature is a intrinsic invariant for a pseudo-Riemannian submanifold as Ricci curvature and scalar curvature.

    Motivated by these facts, we investigate to qualar curvatures of pseudo Riemannian manifolds and pseudo-Riemannian submanifolds. Furthermore, we present some relations involving qualar curvatures and null sectional curvatures for these manifolds and their submanifolds. In Section 2, some basic facts related to pseudo-Riemannian manifolds are mentioned. In Section 3, some relations involving the qualar and null curvatures of a pseudo Riemnnian manifolds are obtained. In Section 4, with the help of Gauss formula, some results dealing qualar curvatures of pseudo-Riemannian submanifolds are given.

    Let (˜M,˜g) be an m-dimensional pseudo-Riemannian manifold with the indefinite metric ˜g of constant index q. The inner product of ˜g is denoted by , throughout this paper. If q=1 then (˜M,˜g) is called a Lorentzian manifold. A pseudo-Riemannian manifold has constant curvature c is called a pseudo-Riemannian space form and it is usually denoted by ˜M(c). For any pseudo-Riemannian space form ˜M(c), there exists the following relation for any X,Y and Z vector fields in the tangent bundle T˜M:

    ˜R(X,Y)Z=c{Z,XYZ,YX}. (2.1)

    Let {e1,,eq,eq+1,,em} be an orthonormal basis of T˜M. Suppose that e1,,eq are timelike and eq+1,,em are spacelike vectors. Then there exists two orthogonal distribution ˜V=Span{e1,,eq} and ˜H=Span{eq+1,,em} of (˜M,˜g) such that we have the following ˜g-orthogonal decomposition:

    T˜M=˜V˜H. (2.2)

    Here, ˜V and ˜H are called as the maximally timelike and maximally spacelike distributions of (˜M,˜g) respectively. Note that every maximally timelike and spacelike distributions are isomorphic as smooth vector bundles over on ˜M [4]. Also, using the decomposition given in (2.2), one can consider this case as a special case of a pseudo-Riemannian almost product structure which is firstly defined by A. Gray [14] and studied by various geometers in [2,3,6,8,12,15,19,24].

    Let Π=Span{ei,ej} be a non-degenerate plane section in T˜M. The sectional curvature ˜K(Π) of Π is defined by

    ˜K(Π)˜K(ei,ej)=εiεj˜R(ei,ej)ej,ei, (2.3)

    where εi=ei,ei=1 for ij{1,,m}. In the case of Π is a degenerate plane section spanned by a null vector ξ and a unit vector ei, the sectional curvature so called null sectional curvature is defined by

    ˜K(Π)˜K(ei,ξ)=εi˜R(ei,ξ)ξ,ei. (2.4)

    For a fixed i{1,,m}, the Ricci curvature of ei is defined by

    ~Ric(ei)=mj=1˜K(ei,ej). (2.5)

    The manifold is called an Einstein manifold if the Ricci curvature (tensor) is constant for all vector fields on T˜M. The scalar curvature at a point p˜M is given by

    ˜τ(p)=mi<j˜K(ei,ej). (2.6)

    Furthermore, the qualar curvature at a point p˜M, denoted by ~qual(p), is defined by

    ~qual(p)=2qi=1ms=q+1˜K(ei,es). (2.7)

    We note that the qualar curvature is equal to the twice mixed scalar curvature of a pseudo Riemannian almost product manifold, for example, see the equation (3.49) in [24].

    Let Πk be a k-dimensional plane section on T˜M and {e1,,ek} be an orthonormal basis of Πk. The Ricci curvature of a k-dimensional plane section Πk at a vector field ei is defined to be

    ~RicΠk(ei)=kij=1εiεj˜R(ei,ej)ej,ei=kij=1˜K(ei,ej), (2.8)

    where i{1,,k}. The scalar curvature of Πk at a point p˜M is defined to be

    ˜τΠk(p)=1i<jkεiεj˜R(ei,ej)ej,ei=1i<jk˜K(ei,ej). (2.9)

    Let (˜M,˜g) be an m-dimensional pseudo Riemannian manifold. Then, we can write null vectors using by timelike and spacelike vectors as follows:

    ξsi=12{ei+es},

    where i{1,,q} and s{q+1,,m}. In this case, the plane section spanned by ξsi and es is a non-degenerate plane section and we have

    ˜K(ξsi,es)=˜K(ei,es). (3.1)

    In a similar manner, the plane section spanned by ξsi and ei is a non-degenerate plane section and we also have

    ˜K(ξsi,ei)=˜K(ei,es) (3.2)

    Let {1,,m} and i, s. The plane section spanned by ξsi and e is a degenerate plane section and its null sectional curvature is given by

    ˜K(ξsi,e)=12˜K(ei,e)+12˜K(es,e)+ε˜R(ei,e)e,es. (3.3)

    Considering these facts, we obtain the following lemma:

    Lemma 3.1. Let (˜M,˜g) be an m-dimensional pseudo Riemannian manifold of index q. Then we have

    ~qual(p)=qi=1ms=q+1[˜K(ξsi,ei)˜K(ξsi,es)]. (3.4)

    Proof. Putting (3.1) and (3.2) in (2.7), the proof of lemma is straightforward.

    Taking into consideration of (3.1), (3.2) and (3.3), we obtain the followings:

    Lemma 3.2. Let (˜M,˜g) be an m-dimensional pseudo Riemannian manifold of index q. Then the following relations hold:

    i) For s{q+1,,m}, we have

    qi,=1i˜K(ξsi,e)=˜τ˜V(p)(q1)~Ric˜V(es)+2qi,=1iε˜R(ei,e)e,es. (3.5)

    ii) For i{1,,q}, we have

    ms,=q+1s˜K(ξsi,e)=˜τ˜H(p)(mq1)~Ric˜H(ei)+2ms,=q+1sε˜R(ei,e)e,es. (3.6)

    Corollary 1. If (˜M,˜g) is an m-dimensional indefinite space form, then we have

    qi,=1i˜K(ξti,e)=0 and ms,=q+1s˜K(ξsj,e)=0 (3.7)

    for t{q+1,,m} and j{1,,q}.

    Proof. Under the assumption, we have from the Eq (2.1) that

    ˜R(ei,e)e,es=0. (3.8)

    Taking into account of (2.8), (2.9), (3.8) and Lemma 3.2, the proof is straightforward.

    Corollary 2. If (˜M,˜g) is an m-dimensional Lorentzian manifold, then the following relation holds:

    m1=2˜K(ξs1,e)=τ˜H(p)(m2)~Ric(e1) (3.9)

    for s{q+1,,m}.

    Now we recall the following proposition of S. G. Harris (cf. Proposition 2.3 in [16]):

    Proposition 3.3. [16] A Lorentzian manifold of dimension at least three has constant curvature if and only if it has null sectional curvature everywhere zero.

    As a generalization of the result of Proposition 3.3 of S. G. Harris, we obtain the following:

    Proposition 3.4. Let (˜M,˜g) is an m-dimensional (m>3) Lorentzian manifold. Then we have the following situations:

    i. If (˜M,˜g) is an Einstein manifold, then there exists an orthonormal basis {e1,,eq,eq+1,,em} on T˜M satisfying

    m1=2˜K(ξs1,e)=0. (3.10)

    ii. If (˜M,˜g) has null sectional curvature everywhere zero then it is an Einstein manifold.

    Proof. Suppose that (˜M,˜g) is an Einstein manifold. In this case, we have

    ˜τ˜H(p)=(m2) ~Ric(X) (3.11)

    for any unit timelike vector field XT˜M. Putting (3.11) in (3.9), we obtain (3.10) which implies the statement (i).

    The proof of statement (ii) is straightforward from Proposition 3.3.

    Let (M,g) be n-dimensional pseudo Riemannian submanifold of (˜M,˜g) with the induced metric g of constant index q. The submanifold (M,g) is called as

    ⅰ. a spacelike submanifold if q=0,

    ⅱ. a timelike submanifold if n=q,

    ⅲ. a pseudo-Riemannian submanifold if it is neither spacelike nor timelike.

    Let us denote the Riemannian connections with respect to the pseudo Riemannian metric ˜g by ˜ and the induced pseudo Riemannian connection on M by respectively. The Gauss and Weingarten formulas are given by

    ˜XY=XY+σ(X,Y), (4.1)
    ˜XN=ANX+XN (4.2)

    for any tangent vector fields X and Y and N normal to M, where σ denotes the second fundamental form and the normal connection and A the shape operator of M. Also, it is known that the tensors A and σ are related by the following equation:

    ANX,Y=σ(X,Y),N. (4.3)

    Let R denotes the Riemannian curvature tensor of the submanifold. The equation of Gauss is given by

    R(X,Y)Z,W=˜R(X,Y)Z,W+σ(X,W),σ(Y,Z)σ(X,Z),σ(Y,W) (4.4)

    for any X,Y,Z,WTM. Using the Gauss equation, one has

    εiεjR(ej,ei)ei,ej=εiεj˜R(ej,ei)ei,ej+εiεjσ(ei,ei),σ(ej,ej)εiεjσ(ei,ej),σ(ej,ei). (4.5)

    Thus, we have

    τ(p)=˜τTpM(p)+mr,s=n+1˜εr˜εsni,j=1εiεjσriiσsjjmr=n+1˜εrni,j=1εiεj(σrij)2, (4.6)

    where

    σ(ei,ej)=mr=n+1εrσrij. (4.7)

    Here, εr=er,ee for any r{n+1,,m}. The mean curvature vector (p) at pM is defined by

    (p)=1ntrace(AN)=1nnj=1εjσ(ej,ej). (4.8)

    Now we can write

    σ(X,Y)=σ˜V(X,Y)+σ˜H(X,Y),X,YTM,(p)=|˜V(p)+|˜H(p), (4.9)

    where σ˜V(X,Y), |˜V(p)˜V belong to the vertical distribution ˜V and σ˜H(X,Y), |˜H(p) belong to the horizontal distribution ˜H. Note that these decompositions are unique.

    The submanifold M is called totally geodesic if σ=0, and it is called minimal if =0. If σ(X,Y)=X,Y for all X,YTM, then M is called totally umbilical. Also, M is called quasi-minimal if 0 and (p),(p)=0 at each point pM. For more details, we refer to [7,8,23].

    Let {e1,,eq,eq+1,,en} be an orthonormal basis on TM, where e1,,eq are timelike and eq+1,,en are spacelike vectors. Since (M,g) is a pseudo Riemannian submanifold, we can write TM as orhogonal direct sum of its maximally spacelike distribution H and maximally timelike distribution V as follows:

    TM=VH, (4.10)

    where V=Span{e1,,eq} and H=Span{eq+1,,en}.

    More specifically, M is called timelike V-geodesic if σ˜V|V=0, timelike H-geodesic if σ˜V|H=0, timelike mixed geodesic if σ˜V|V×H=0, timelike geodesic if σ˜V=0, spacelike V-geodesic if σ˜H|V=0, spacelike H-geodesic if σ˜H|H=0, spacelike mixed geodesic if σ˜H|V×H=0, spacelike geodesic if σ˜H=0, mixed geodesic if σ|V×H=0 [25].

    Proposition 4.1. Let M be an n-dimensional pseudo Riemannian submanifold of (˜M,˜g) and pM. Then we have

    qual(p)=~qualTpM(p)+2ntraceVσ,2|traceVσ|22|σV×H|2, (4.11)

    where traceV denotes the trace with respect to the maximally timelike distribution V of M.

    Proof. From (4.5), we get

    R(ei,es)es,ei=˜R(ei,es)es,eiσ(es,es),σ(ei,ei)+σ(ei,es),σ(ei,es)

    for i{1,,q} and s{q+1,,n}. Therefore, we can write

    qi=1ns=q+1R(ei,es)es,ei=qi=1ns=q+1˜R(ei,es)es,ei+qi=1ns=1σ(es,es),σ(ei,ei)qi,s=1σ(es,es),σ(ei,ei)qi=1ns=q+1σ(ei,es),σ(ei,es). (4.12)

    Using (2.7) in (4.12), we obtain

    qual(p)=~qualTpM(p)+2qi=1ns=1σ(es,es),σ(ei,ei)2qi,s=1σ(es,es),σ(ei,ei)2qi=1ns=q+1σ(ei,es),σ(ei,es),

    which implies (4.11).

    For the special case q=1 of Lemma 4.1, we get the following corollary:

    Corollary 3. Let M be an n-dimensional pseudo Riemannian submanifold with index 1 of (˜M,˜g). For any unit timelike vector X in TpM, the following relation satisfies:

    qual(p)=~qualTpM(p)+2nσ(X,X),2|σ(X,X)|22|σ{X}×H|2. (4.13)

    For the special case q=˜q=1 of Lemma 4.1, we get the following corollary:

    Corollary 4. Let M be an n-dimensional timelike submanifold of a Lorentzian manifold ˜M. For any unit timelike vector X in TpM, the following relation satisfies:

    qual(p)=~qualTpM(p)+2nσ(X,X),2σ(X,X)22σ{X}×H2. (4.14)

    Proof. Since the indexes of both submanifold and ambient manifold are equal to each other, we see that the second fundamental form which lies in the normal space becomes a spacelike vector. Using this fact, the proof of corollary is straightforward from Corollary 3.

    Theorem 4.2. Let M be a timelike submanifold of a Lorentzian manifold. For any unit timelike vector X in TpM, the following relation satisfies:

    qual(p)~qualTpM(p)+2nσ(X,X),. (4.15)

    The equality case of (4.15) holds for all unit vectors X in TpM if and only if M is timelike V-geodesic and mixed geodesic.

    Proof. Using (4.14), we obtain the Eq (4.15). The equality case of (4.15) holds for all unit vectors X in TpM if and only if

    σ(X,X)=0,   and   σ{X}×H=0 (4.16)

    which imply that M is timelike V-geodesic and mixed geodesic.

    Corollary 5. Let M be a timelike submanifold of an n-dimensional Minkowski space. For any unit timelike vector X in TM, the following inequality satisfies:

    qual(p)2nσ(X,X),. (4.17)

    The equality case of (4.17) holds for all unit vectors X in TM if and only if M is timelike V-geodesic and mixed geodesic.

    Corollary 6. Let M be a minimal timelike submanifold of a Minkowski space. Then we have

    qual(p)0. (4.18)

    The equality case of (4.18) holds for all pM if and only if M is timelike V-geodesic and mixed geodesic.

    Corollary 7. Every minimal timelike submanifold of a Minkowski space is of constant curvature if and only if it is timelike V-geodesic and mixed geodesic.

    Theorem 4.3. Let M be a totally umbilical timelike submanifold of a Lorentzian manifold. Then we have

    2n(p)2<qual(p)~qualTpM(p). (4.19)

    Proof. Since M is totally umbilical, we have from (4.14) that

    2n(p)2qual(p)~qualTpM(p) (4.20)

    with the equality if and only if M is timelike V-geodesic and mixed geodesic. Therefore, M becomes totally geodesic which contradicts that the submanifold is totally umbilical.

    Corollary 8. Let M be a totally umbilical timelike submanifold of n-dimensional Minkowski space. Then we have the following inequality for all pM:

    2n(p)2<qual(p). (4.21)

    Now, we shall recall the following Lemma of B. Y. Chen (cf. Lemma 3.2 in [8]).

    Lemma 4.4. Let φ:MRm˜q(c) be an isometric immersion of a pseudo Riemannian manifold M into an indefinite real space form Rm˜q(c). If M is totally umbilical, then

    i) is a parallel normal vector field, i.e., ˜=0;

    ii) , is constant;

    iii) ϕ is a parallel immersion, i.e., ˜σ=0 identically on M;

    iv) M is of constant curvature c+,;

    v) AH=,I, where I denotes the identity transformation;

    vi) M is a parallel submanifold.

    From the iv) statement of Lemma 4.4, Corollary 8, we see a contradiction for totally umbilical timelike submanifold in a Minkowski space. Thus, we get the following result:

    Corollary 9. There exist no totally umbilical timelike submanifold in a Minkowski space.

    Now, we shall recall the following theorem of J. Li [21]

    Theorem 4.5. If M is a totally umbilical hypersurface of a Minkowski space, then either M is a Riemannian space form or a locally Minkowski space.

    Remark 1. We note that Corollary 9 is an another proof way of Theorem 4.5 of J. Li [21].

    The author thanks to referees for very important recommendations and warnings which improved the paper.

    The author declares that there is no competing interest.


    Acknowledgments



    We would like to acknowledge the support provided by the Bioinformatics tools, software & databases in the completion of the work. We would also like to thank Amity University Uttar Pradesh, Lucknow Campus, where all the benchwork of the present study was conducted.

    Conflict of interest



    The authors declare no conflict of interest.

    [1] Jain A, Mitra P (2023) Bipolar disorder. Available from: https://www.ncbi.nlm.nih.gov/books/NBK558998/.
    [2] Hilty DM, Leamon MH, Lim RF, et al. (2006) A review of bipolar disorder in adults. Psychiatry (Edgmont) 3: 43-55.
    [3] Zhang N, Hu G, Myers TG, et al. (2019) Protocols for the analysis of microRNA expression, biogenesis, and function in immune cells. Curr Protoc Immunol 126: e78. https://doi.org/10.1002/cpim.78
    [4] Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight?. Nat Rev Genet 9: 102-114. https://doi.org/10.1038/nrg229
    [5] Anjum A, Jaggi S, Varghese E, et al. (2016) Identification of differentially expressed genes in RNA-seq data of arabidopsis thaliana: A compound distribution approach. J Comput Biol 23: 239-247. https://doi.org/10.1089/cmb.2015.0205
    [6] Rodriguez-Esteban R, Jiang X (2017) Differential gene expression in disease: a comparison between high-throughput studies and the literature. BMC Med Genomics 10: 59. https://doi.org/10.1186/s12920-017-0293-y
    [7] Pfaffenseller B, da Silva Magalhães PV, De Bastiani MA, et al. (2016) Differential expression of transcriptional regulatory units in the prefrontal cortex of patients with bipolar disorder: potential role of early growth response gene 3. Transl Psychiatry 6: e805. https://doi.org/10.1038/tp.2016.78
    [8] Machado-Vieira R, Manji HK, Zarate CA, et al. (2009) The role of lithium in the treatment of bipolar disorder: convergent evidence for neurotrophic effects as a unifying hypothesis. Bipolar disord 11: 92-109. https://doi.org/10.1111/j.1399-5618.2009.00714.x
    [9] Qureshi NA, Al-Bedah AM (2013) Mood disorders and complementary and alternative medicine: A literature review. Neuropsychiatr Dis Treat 2013: 639-658. https://doi.org/10.2147/NDT.S43419
    [10] Clough E, Barrett T (2016) The gene expression omnibus database. Statistical genomics . New York: Humana Press 93-110. https://doi.org/10.1007/978-1-4939-3578-9_5
    [11] Leinonen R, Sugawara H, Shumway M (2011) International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucleic Acids Res : D19-D21. https://doi.org/10.1093/nar/gkq1019
    [12] FastQC: A quality control tool for high throughput sequence data. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
    [13] Jalili V, Afgan E, Gu Q, et al. (2020) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Res 48: 8205-8207. https://doi.org/10.1093/nar/gkaa554
    [14] Friedländer MR, Mackowiak SD, Li N, et al. (2012) miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40: 37-52. https://doi.org/10.1093/nar/gkr688
    [15] Kim D, Langmead B, Salzberg SL (2015) HISAT: A fast spliced aligner with low memory requirements. Nat Methods 12: 357-360. https://doi.org/10.1038/nmeth.3317
    [16] Mackowiak SD (2011) Identification of novel and known miRNAs in deep-sequencing data with miRDeep2. Curr Protoc Bioinformatics . https://doi.org/10.1002/0471250953.bi1210s36
    [17] Liao Y, Smyth GK, Shi W (2014) featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30: 923-930. https://doi.org/10.1093/bioinformatics/btt656
    [18] Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. https://doi.org/10.1186/s13059-014-0550-8
    [19] Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P (2016) Tools for sequence-based miRNA target prediction: What to choose?. Int J Mol Sci 17: 1987. https://doi.org/10.3390/ijms17121987
    [20] Venny—An interactive tool for comparing lists with Venn's diagrams. Available from: https://bioinfogp.cnb.csic.es/tools/venny/index.html
    [21] Molinspiration cheminformatics. Available from: https://molinspiration.com/
    [22] Banerjee P, Eckert OA, Schrey AK, et al. (2018) ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 46: W257-W263. https://doi.org/10.1093/nar/gky318
    [23] Rizvi SMD, Shakil S, Haneef M (2013) A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. EXCLI J 12: 831-857.
    [24] Pacifico R, Davis RL (2017) Transcriptome sequencing implicates dorsal striatum-specific gene network, immune response and energy metabolism pathways in bipolar disorder. Mol Psychiatry 22: 441-449. https://doi.org/10.1038/mp.2016.94
    [25] Pai S, Li P, Killinger B, et al. (2019) Differential methylation of enhancer at IGF2 is associated with abnormal dopamine synthesis in major psychosis. Nat Commun 10: 2046. https://doi.org/10.1038/s41467-019-09786-7
    [26] Hu J, Xu J, Pang L, et al. (2016) Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis. Oncotarget 7: 71087-71098. https://doi.org/10.18632/oncotarget.12122
    [27] Xu Z, Adilijiang A, Wang W, et al. (2019) Arecoline attenuates memory impairment and demyelination in a cuprizone-induced mouse model of schizophrenia. Neuroreport 30: 134-138. https://doi.org/10.1097/WNR.0000000000001172
    [28] Suresh P, Raju AB (2013) Antidopaminergic effects of leucine and genistein on shizophrenic rat models. Neurosciences 18: 235-241.
    [29] Lin JC, Lee MY, Chan MH, et al. (2016) Betaine enhances antidepressant-like, but blocks psychotomimetic effects of ketamine in mice. Psychopharmacology (Berl) 233: 3223-3235. https://doi.org/10.1007/s00213-016-4359-x
    [30] Ben-Azu B, Aderibigbe AO, Omogbiya IA, et al. (2018) Morin pretreatment attenuates schizophrenia-like behaviors in experimental animal models. Drug Res (Stuttg) 68: 159-167. https://doi.org/10.1055/s-0043-119127
    [31] Kumar G, Patnaik R (2016) Exploring neuroprotective potential of Withania somnifera phytochemicals by inhibition of GluN2B-containing NMDA receptors: An in silico study. Med Hypotheses 92: 35-43. https://doi.org/10.1016/j.mehy.2016.04.034
    [32] Chen W, Qi J, Feng F, et al. (2014) Neuroprotective effect of allicin against traumatic brain injury via Akt/endothelial nitric oxide synthase pathwaymediated anti-inflammatory and anti-oxidative activities. Neurochem Int 68: 28-37. https://doi.org/10.1016/j.neuint.2014.01.015
    [33] Zhu HT, Bian C, Yuan JC, et al. (2014) Curcumin attenuates acute inflammatory injury by inhibiting the TLR4/MyD88/NF-κB signaling pathway in experimental traumatic brain injury. J Neuroinflammation 11: 59. https://doi.org/10.1186/1742-2094-11-59
    [34] Krishnamurthy RG, Senut MC, Zemke D, et al. (2009) Asiatic acid, a pentacyclic triterpene from Centella asiatica, is neuroprotective in a mouse model of focal cerebral ischemia. J Neurosci Res 87: 2541-2550. https://doi.org/10.1002/jnr.22071
    [35] Chandrasekaran K, Mehrabian Z, Spinnewyn B, et al. (2001) Neuroprotective effects of bilobalide, a component of the Ginkgo biloba extract (EGb 761), in gerbil global brain ischemia. Brain Res 922: 282-292. https://doi.org/10.1016/S0006-8993(01)03188-2
    [36] Zhao J, Kobori N, Aronowski J, et al. (2006) Sulforaphane reduces infarct volume following focal cerebral ischemia in rodents. Neurosci Lett 393: 108-112. https://doi.org/10.1016/j.neulet.2005.09.065
    [37] Lakstygal AM, Kolesnikova TO, Khatsko SL, et al. (2019) DARK classics in chemical neuroscience: Atropine, scopolamine, and other anticholinergic deliriant hallucinogens. ACS Chem Neurosci 10: 2144-2159. https://doi.org/10.1021/acschemneuro.8b00615
    [38] Huang SS, Tsai MC, Chih CL, et al. (2001) Resveratrol reduction of infarct size in Long-Evans rats subjected to focal cerebral ischemia. Life Sci 69: 1057-1065. https://doi.org/10.1016/S0024-3205(01)01195-X
    [39] Leiderman E, Zylberman I, Zukin SR, et al. (1996) Preliminary investigation of high-dose oral glycine on serum levels and negative symptoms in schizophrenia: An open-label trial. Biol Psychiatry 39: 213-215. https://doi.org/10.1016/0006-3223(95)00585-4
    [40] Hannan MA, Rahman MA, Sohag AAM, et al. (2021) Black cumin (Nigella sativa L.): A comprehensive review on phytochemistry, health benefits, molecular pharmacology, and safety. Nutrients 13: 1784. https://doi.org/10.3390/nu13061784
    [41] Yadav M, Jindal DK, Dhingra MS, et al. (2018) Protective effect of gallic acid in experimental model of ketamine-induced psychosis: Possible behaviour, biochemical, neurochemical and cellular alterations. Inflammopharmacology 26: 413-424. https://doi.org/10.1007/s10787-017-0366-8
    [42] Mukherjee PK, Kumar V, Mal M, et al. (2007) In vitro acetylcholinesterase inhibitory activity of the essential oil from Acorus calamus and its main constituents. Planta Med 73: 283-285. https://doi.org/10.1055/s-2007-967114
    [43] Azimi A, Ghaffari SM, Riazi GH, et al. (2016) α-Cyperone of Cyperus rotundus is an effective candidate for reduction of inflammation by destabilization of microtubule fibers in brain. J Ethnopharmacol 194: 219-227. https://doi.org/10.1016/j.jep.2016.06.058
    [44] Alhebshi AH, Gotoh M, Suzuki I (2013) Thymoquinone protects cultured rat primary neurons against amyloid β-induced neurotoxicity. Biochem Biophys Res Commun 433: 362-367. https://doi.org/10.1016/j.bbrc.2012.11.139
    [45] Fuentes RG, Arai MA, Sadhu SK, et al. (2015) Phenolic compounds from the bark of Oroxylum indicum activate the Ngn2 promoter. J Nat Med 69: 589-594. https://doi.org/10.1007/s11418-015-0919-3
    [46] Rayan NA, Baby N, Pitchai D (2011) Costunolide inhibits proinflammatory cytokines and iNOS in activated murine BV2 microglia. Front Biosci (Elite Ed) 3: 1079-1091. https://doi.org/10.2741/e312
    [47] Khanra R, Dewanjee S, Dua TK, et al. (2015) Abroma augusta L. (Malvaceae) leaf extract attenuates diabetes induced nephropathy and cardiomyopathy via inhibition of oxidative stress and inflammatory response. J Transl Med 13: 6. https://doi.org/10.1186/s12967-014-0364-1
    [48] Gray NE, Magana AA, Lak P, et al. (2018) Centella asiatica: Phytochemistry and mechanisms of neuroprotection and cognitive enhancement. Phytochem Rev 17: 161-194. https://doi.org/10.1007/s11101-017-9528-y
    [49] Sarkar T, Salauddin M, Chakraborty R (2020) In-depth pharmacological and nutritional properties of bael (Aegle marmelos): A critical review. J Agric Food Res 2: 100081. https://doi.org/10.1016/j.jafr.2020.100081
    [50] Okugawa H, Ueda R, Matsumoto K, et al. (1995) Effect of α-santalol and β-santalol from sandalwood on the central nervous system in mice. Phytomedicine 2: 119-126. https://doi.org/10.1016/S0944-7113(11)80056-5
    [51] BIOVIA Discovery Studio. Available from: https://www.3ds.com/products/biovia/discovery-studio
    [52] Judd LL, Akiskal HS (2003) The prevalence and disability of bipolar spectrum disorders in the US population: Re-analysis of the ECA database taking into account subthreshold cases. J Affect Disord 73: 123-131. https://doi.org/10.1016/s0165-0327(02)00332-4
    [53] Osby U, Brandt L, Correia N, et al. (2001) Excess mortality in bipolar and unipolar disorder in Sweden. Arch Gen Psychiatry 58: 844-850. https://doi.org/10.1001/archpsyc.58.9.844
    [54] Taguchi YH, Wang H (2018) Exploring microRNA biomarker for amyotrophic lateral sclerosis. Int J Mol Sci 19: 1318. https://doi.org/10.3390/ijms19051318
    [55] Fame RM, MacDonald JL, Dunwoodie SL, et al. (2016) Cited2 regulates neocortical layer II/III generation and somatosensory callosal projection neuron development and connectivity. J Neurosci 36: 6403-6419. https://doi.org/10.1523/JNEUROSCI.4067-15.2016
    [56] Yang C, Zhang K, Zhang A, et al. (2022) Co-expression network modeling identifies specific inflammation and neurological disease-related genes mRNA modules in mood disorder. Front Genet 13: 865015. https://doi.org/10.3389/fgene.2022.865015
    [57] Ng PH, Kim GD, Chan ER, et al. (2020) CITED2 limits pathogenic inflammatory gene programs in myeloid cells. FASEB J 34: 12100-12113. https://doi.org/10.1096/fj.202000864R
    [58] Adnan G, Rubikaite A, Khan M, et al. (2020) The GTPase Arl8B plays a principle role in the positioning of interstitial axon branches by spatially controlling autophagosome and lysosome location. J Neurosci 40: 8103-8118. https://doi.org/10.1523/JNEUROSCI.1759-19.2020
    [59] Bagshaw RD, Callahan JW, Mahuran DJ (2006) The Arf-family protein, Arl8b, is involved in the spatial distribution of lysosomes. Biochem Biophys Res Commun 344: 1186-1191. https://doi.org/10.1016/j.bbrc.2006.03.221
    [60] Boeddrich A, Haenig C, Neuendorf N, et al. (2023) A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer's disease. Genome Med 15: 50. https://doi.org/10.1186/s13073-023-01206-2
    [61] NUDT4 nudix hydrolase 4 [Homo sapiens (human)] (2024). Available from: https://www.ncbi.nlm.nih.gov/gene/11163#summary
    [62] Hua LV, Green M, Warsh JJ, et al. (2001) Molecular cloning of a novel isoform of diphosphoinositol polyphosphate phosphohydrolase: A potential target of lithium therapy. Neuropsychopharmacology 24: 640-651. https://doi.org/10.1016/S0893-133X(00)00233-5
    [63] Ding L, Liu T, Ma J (2023) Neuroprotective mechanisms of Asiatic acid. Heliyon 9: e15853. https://doi.org/10.1016/j.heliyon.2023.e15853
    [64] Zheng CJ, Qin LP (2007) Chemical components of Centella asiatica and their bioactivities. J Chin Integr Med 5: 348-351.
    [65] Rao KGM, Rao SM, Rao SG (2006) Centella asiatica (L.) leaf extract treatment during the growth spurt period enhances hippocampal CA3 neuronal dendritic arborization in rats. Evid-Based Complement Alternat Med 3: 349-357. https://doi.org/10.1093/ecam/nel024
    [66] Subathra M, Shila S, Devi MA, et al. (2005) Emerging role of Centella asiatica in improving age-related neurological antioxidant status. Exp Gerontol 40: 707-715. https://doi.org/10.1016/j.exger.2005.06.001
    [67] Lu CW, Lin TY, Pan TL, et al. (2021) Asiatic acid prevents cognitive deficits by inhibiting calpain activation and preserving synaptic and mitochondrial function in rats with kainic acid-induced seizure. Biomedicines 9: 284. https://doi.org/10.3390/biomedicines9030284
    [68] Kato T, Kato N (2000) Mitochondrial dysfunction in bipolar disorder. Bipolar Disord 2: 180-190. https://doi.org/10.1034/j.1399-5618.2000.020305.x
    [69] Ding H, Xiong Y, Sun J, et al. (2018) Asiatic acid prevents oxidative stress and apoptosis by inhibiting the translocation of α-synuclein into mitochondria. Front Neurosci 12: 431. https://doi.org/10.3389/fnins.2018.00431
    [70] Krishnamurthy RG, Senut MC, Zemke D, et al. (2009) Asiatic acid, a pentacyclic triterpene from Centella asiatica, is neuroprotective in a mouse model of focal cerebral ischemia. J Neurosci Res 87: 2541-2550. https://doi.org/10.1002/jnr.22071
  • This article has been cited by:

    1. Yanlin Li, Somnath Mondal, Santu Dey, Arindam Bhattacharyya, Akram Ali, A Study of Conformal $$\eta$$-Einstein Solitons on Trans-Sasakian 3-Manifold, 2022, 1776-0852, 10.1007/s44198-022-00088-z
    2. Yanlin Li, Sahar H. Nazra, Rashad A. Abdel-Baky, Singularity Properties of Timelike Sweeping Surface in Minkowski 3-Space, 2022, 14, 2073-8994, 1996, 10.3390/sym14101996
    3. Yanlin Li, Zhizhi Chen, Sahar H. Nazra, Rashad A. Abdel-Baky, Singularities for Timelike Developable Surfaces in Minkowski 3-Space, 2023, 15, 2073-8994, 277, 10.3390/sym15020277
    4. Yanlin Li, Huchchappa A. Kumara, Mallannara Siddalingappa Siddesha, Devaraja Mallesha Naik, Characterization of Ricci Almost Soliton on Lorentzian Manifolds, 2023, 15, 2073-8994, 1175, 10.3390/sym15061175
    5. Yanlin Li, Mahmut Mak, Framed Natural Mates of Framed Curves in Euclidean 3-Space, 2023, 11, 2227-7390, 3571, 10.3390/math11163571
    6. Yanlin Li, Sujit Bhattacharyya, Shahroud Azami, Apurba Saha, Shyamal Kumar Hui, Harnack Estimation for Nonlinear, Weighted, Heat-Type Equation along Geometric Flow and Applications, 2023, 11, 2227-7390, 2516, 10.3390/math11112516
  • Reader Comments
  • © 2024 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(1879) PDF downloads(118) Cited by(1)

Figures and Tables

Figures(5)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog