Alzheimer’s disease (AD) is a complex, genetically heterogeneous disorder. The diverse phenotypes associated with AD result from interactions between genetic and environmental factors, influencing multiple biological pathways throughout disease progression. Network-based approaches offer a way to assess phenotype-specific states. In this study, we calculated key network metrics to characterize the network transcriptional structure and organization in LOAD, focusing on genes and pathways implicated in AD pathology within the dorsolateral prefrontal cortex (DLPFC). Our findings revealed disease-specific coexpression markers associated with diverse metabolic functions. Additionally, significant differences were observed at both the mesoscopic and local levels between AD and control networks, along with a restructuring of gene coexpression and biological functions into distinct transcriptional modules. These results show the molecular reorganization of the transcriptional program occurring in LOAD, highlighting specific adaptations that may contribute to or result from cellular responses to pathological stressors. Our findings may support the development of a unified model for the causal mechanisms of AD, suggesting that its diverse manifestations arise from multiple pathways working together to produce the disease’s complex clinical patho-phenotype.

Alzheimer’s disease (AD) is a complex, genetically heterogeneous disorder. The diverse phenotypes associated with AD result from intricate interactions between genetic and environmental factors, influencing multiple biological pathways throughout disease progression. Network-based approaches offer a comprehensive way to assess phenotype-specific states. In this study, we calculated key network metrics to characterize the network transcriptional structure and organization in LOAD, focusing on genes and pathways implicated in AD pathology within the dorsolateral prefrontal cortex (DLPFC). Our findings revealed disease-specific coexpression markers associated with diverse metabolic functions. Additionally, significant differences were observed at both the mesoscopic and local levels between AD and control networks, along with a restructuring of gene coexpression and biological functions into distinct transcriptional modules. These results show the molecular reorganization of the transcriptional program occurring in LOAD, highlighting specific adaptations that may contribute to or result from cellular responses to pathological stressors. Our findings may support the development of a unified model for the causal mechanisms of AD, suggesting that its diverse manifestations arise from multiple pathways working together to produce the disease’s complex clinical patho-phenotype.

Indigenous people from the American continent, or Native Americans, are underrepresented in the collective genomic knowledge. A minimal percentage of individuals in international databases belong to these important minority groups. Yet, the study of native American genomics is a growing field. In this work, we reviewed 56 scientific publications where ancient or contemporary DNA of Native Americans across the continent was studied by array, whole-exome, or whole-genome technologies. In total, 13,706 native Americans have been studied with genomic technologies, of which 1292 provided whole genome samples. Data availability is lacking, with barely 3.6% of the contemporary samples clearly accessible for further studies; in striking contrast, 96.3% of the ancient samples are publicly available. We compiled census data on the home countries and found that 607 indigenous groups are still missing representation in genomic datasets. By analyzing authorship of the published works, we found that there is a need for more involvement of the home countries as leads in indigenous genomic studies. We provide this review to aid in the design of future studies that aim to reduce the missing diversity of indigenous Americans.

The Comcáac (also known as Seri) are an indigenous community from the central coast of the Sonoran Desert in Mexico. Genetic studies conducted on this population since the late 1990s have revealed marked genetic differentiation resulting from pronounced genetic drift caused by a historical bottleneck, which is consistent with anthropological and linguistic records. Research has examined allele frequencies and genetic variants associated with cirrhosis risk, pathogen adaptation, forensic markers, vitamin and sugar metabolism, body mass index, immune responses, cytochrome genes, genetic distances with other groups, and migration patterns. While early investigations relied on classical genetic approaches, more recent studies have employed omics technologies, including whole-genome sequencing. Analyses of the Comcáac genome provide valuable insights into the phenotypic traits and medical predispositions of this community.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by the simultaneous disruption of interconnected molecular pathways, yet the structural mechanisms underlying this transcriptional disintegration remain poorly characterized. To address this, we constructed condition-specific gene co-expression networks from DLPFC bulk RNA-seq data, using a mutual information framework with Infomap community partitioning. Functional enrichment of network communities via Ingenuity Pathway Analysis (IPA) identified GABAergic signaling, SNARE complex assembly, Synaptogenesis, and Neurexins and Neuroligins interactions as significantly overrepresented pathways. Integration of node degree with condition-specific average expression revealed coordinated topological centralization of key synaptic genes — including NRXN2, LRRTM1, DLGAP3, and SHANK1 — alongside a widespread transcriptional downregulation in GABAergic and Synaptogenesis modules. Shortest path analysis further demonstrated a consistent expansion of intra-pathway distances across all evaluated canonical pathways in AD, quantifying a progressive loss of local communication efficiency. These findings reframe LOAD as an active structural rewiring process, in which the transcriptional network consolidates remaining resources around essential synaptic components as a molecular signature of the pathological state.

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by the simultaneous disruption of interconnected molecular pathways, yet the structural mechanisms underlying this transcriptional disintegration remain poorly characterized. To address this, we constructed condition-specific gene co-expression networks from DLPFC bulk RNA-seq data, using a mutual-information (MI) framework with infomap community partitioning. Functional enrichment of network communities via Ingenuity Pathway Analysis (IPA) identified GABAergic signaling, SNARE complex assembly, Synaptogenesis, and neurexin and neuroligin interactions as significantly overrepresented pathways. Integration of node degree with condition-specific average expression revealed coordinated topological centralization of key synaptic genes—including NRXN2, LRRTM1, DLGAP3, and SHANK1—alongside a widespread transcriptional downregulation in GABAergic and Synaptogenesis modules. A shortest-path analysis revealed a consistent expansion of intra-pathway distances across all evaluated canonical pathways in AD, a pattern statistically consistent with reduced local co-expression cohesion. These findings reframe Late-Onset Alzheimer’s Disease (LOAD) as an active structural-rewiring process, in which the observed topological centralization pattern seems to be consistent with a consolidation of co-expression around synaptic components, though we cannot exclude that shifts in cellular composition contribute to this signal in bulk RNA-seq data.