  Bacterial communities are pivotal to maintaining ecological function and
preserving the rich tapestry of biological diversity. The rapid development of
environmental sequencing technologies, such as metagenomics, has revolutionized
our capacity to probe such diversity. However, despite these advances, a
theoretical understanding connecting empirical data with ecosystem modelling,
in particular in the framework of disordered systems akin to spin glasses, is
still in its infancy. Here, we present a comprehensive framework using theories
of disordered systems to decode microbiome data, which offers insight into the
ecological forces that shape macroecological states. By employing the quenched
disordered generalized Lotka-Volterra model, we analyze species abundance data
in healthy and diseased human gut microbiomes. Results reveal the emergence of
two distinct patterns of species-interaction networks, elucidating the pathways
through which dysbiosis may drive microbiome instability. Interaction patterns
thus provide a window into the systemic shifts accompanying the transition from
health to disease, offering a new perspective on the dynamics of the microbial
community. Our findings suggest the potential of disordered systems theory to
characterize microbiomes by capturing the essence of ecological interactions
and their consequences on stability and functioning, leveraging our
understanding of the linkages of dysbiosis and microbial dynamics.
