The gradient of the function $f(y)$ is the vector $g$ which satisfies
$$df = g^Tdy$$
and the hessian is the matrix $H$ which satisfies
$$dg = H\,dy$$
and the laplacian is the trace of the hessian $\,\,L={\rm Tr}(H)$.
Suppose a new independent vector $x$ is introduced with $\,y=Ax$.
It really helps to consider the case where $A$ is rectangular, so that $x$ and $y$ have different dimensions. Then when you start to wonder why $A$ was transposed or why it was pre-multiplied or post-multiplied, you'll see that it's the only way to make the dimensions come out correctly.
To find the gradient and hessian wrt $x$, one wants modifie${\rm d^*}$ quantities which satisfy
$$df=g_*^Tdx,\,\,\,\,\,\,dg_*=H_*\,dx,\,\,\,\,\,\,L_*={\rm Tr}(H_*)$$
Working these out is straightforward
$$\eqalign{
df &= g^Tdy = g^TA\,dx = (A^Tg)^Tdx = g_*^Tdx \cr
g_* &= A^Tg \cr\cr
dg_* &= A^Tdg = A^T(H\,(dy)) = A^TH(A\,dx) = H_*\,dx \cr
H_* &= A^THA \cr
L_* &= {\rm Tr}(A^THA) = {\rm Tr}(AA^TH) \cr
}$$
The last expression is the laplacian in terms of the new variable.
Unfortunately, knowing the value of ${\rm Tr}(H)$ is of no help in evaluating ${\rm Tr}(AA^TH)$.