A growing body of evidence suggests that only a few amino acids (“hot-spots”) at the interface contribute most of the binding energy in transient protein-protein interactions. However, experimental protocols to identify these hot-spots are highly labor-intensive and expensive. Computational methods, including evolutionary couplings, have been proposed to predict the hot-spots, but they generally fail to provide details of the interacting amino acids. Here we showed that unbiased evolutionary methods followed by biased molecular dynamic simulations could achieve this goal and reveal critical elements of protein complexes. We applied the methodology to selected G-protein coupled receptors (GPCRs), known for their therapeutic properties. We used the structure-prior-assisted direct coupling analysis (SP-DCA) to predict the binding interfaces of A2aR/D2R, CB1R/D2R, A2aR/CB1R, 5HT2AR/D2R, and 5-HT2AR/mGluR2 receptor heterodimers, which all agreed with published data. In order to highlight details of the interactions, we performed molecular dynamic (MD) simulations using the newly developed AWSEM energy model. We found that these receptors interact primarily through critical residues at the C and N terminal domains and the third intracellular loop (ICL3). The MD simulations showed that these residues are energetically necessary for dimerization and revealed their native conformational state. We subsequently applied the methodology to the 5-HT2AR/5-HTR4R heterodimer, given its implication in drug addiction and neurodegenerative pathologies such as Alzheimer’s disease (AD). Further, the SP-DCA analysis showed that 5-HT2AR and 5-HTR4R heterodimerize through the C-terminal domain of 5-HT2AR and ICL3 of 5-HT4R. However, elucidating the details of GPCR interactions would accelerate the discovery of druggable sites and improve our knowledge of the etiology of common diseases, including AD.

Alzheimer’s disease and related dementias (ADRD) have been associated with alterations in both oral and gut microbiomes. While extensive research has focused on the role of gut dysbiosis in ADRD, the contribution of the oral microbiome remains relatively understudied. Furthermore, the potential synergistic interactions between oral and gut microbiomes in ADRD pathology are largely unexplored. This study aims to evaluate distinct patterns and potential synergistic effects of oral and gut microbiomes in a cohort of predominantly Hispanic individuals with cognitive impairment (CI) and without cognitive impairment (NC). We conducted 16S rRNA gene sequencing on stool and saliva samples from 32 participants (17 CI, 15 NC; 62.5% female, mean age = 70.4 ± 6.2 years) recruited in San Antonio, Texas, USA. Correlation analysis through MaAslin2 assessed the relationship between participants’ clinical measurements (e.g., fasting glucose and blood cholesterol) and their gut and saliva microbial contents. Differential abundance analysis evaluated taxa with significant differences between CI and NC groups, and alpha and beta diversity metrics assessed within-sample and group compositional differences. Our analyses revealed no significant differences between NC and CI groups in fasting glucose or blood cholesterol levels. However, a clear association was observed between gut microbiome composition and levels of fasting glucose and blood cholesterol. While alpha and beta diversity metrics showed no significant differences between CI and NC groups, differential abundance analysis revealed an increased presence of oral genera such as Dialister , Fretibacterium , and Mycoplasma in CI participants. Conversely, CI individuals exhibited a decreased abundance of gut genera, including Shuttleworthia , Holdemania , and Subdoligranulum , which are known for their anti-inflammatory properties. No evidence was found for synergistic contributions between oral and gut microbiomes in the context of ADRD. Our findings suggest that similar to the gut microbiome, the oral microbiome undergoes significant modifications as individuals transition from NC to CI. Notably, the identified oral microbes have been previously associated with periodontal diseases and gingivitis. These results underscore the necessity for further investigations with larger sample sizes to validate our findings and elucidate the complex interplay between oral and gut microbiomes in ADRD pathogenesis.

Neurodegenerative disorders, including Alzheimer's disease and AD‐related dementias (AD/ADRD), pose significant challenges to health care systems globally, particularly in Africa. With the advances in medical technology and research capabilities, especially in next‐generation sequencing and imaging, vast amounts of data have been generated from AD/ADRD research. Given that the greatest increase in AD/ADRD prevalence is expected to occur in Africa, it is critical to establish comprehensive bioinformatics training programs to help African scientists leverage existing data and collect additional information to untangle AD/ADRD heterogeneity in African populations. The South Texas Alzheimer's Disease Research Center, with efforts from the National Institutes of Health and the Global Brain Health Institute, has partnered with the Brain Research Africa Initiative to develop the A frican I nitiative on B ioinformatics O nline Training in N eurodegenerative D isease ( AI‐BOND ). AI‐BOND is a comprehensive and accessible training program, the aim of which is to advance biostatistics and bioinformatics expertise in Africa in studying neurodegenerative diseases. This expertise is essential to enable African scientists to utilize the extensive AD/ADRD data and enhance the continent's ability to contribute to global research efforts in this field. The training addresses the gap in analyzing neurodegenerative disease data by providing skills and knowledge in genetic epidemiology, biostatistics, and bioinformatics to African students and researchers. This innovative online training program will last 6 months and provide training in skill sets R, SAS, and Python programing, genome‐wide association studies, genomics, transcriptomics, proteomics, metabolomics, microbiome analysis, and advanced statistical methods. Additional training will include study design and manuscript and grant writing. The first cohort of the AI‐BOND program will graduate in June 2024. The AI‐BOND program is expected to build research computational capacities in Africa that will improve the ability of graduates to conduct and utilize large‐scale studies, with the goal of curbing the growing incidence of neurodegenerative diseases in Africa. Highlights: Alzheimer's disease (AD) and AD‐related dementias (ADRD) pose significant health challenges globally, particularly in Africa. The most significant AD/ADRD prevalence increase is predicted to occur in Africa. It is crucial to establish a bioinformatics training capacity in Africa to leverage the vast number of multi‐omics and imaging biomarkers of AD/ADRD data being generated. The African Initiative on Bioinformatics Online Training in Neurodegenerative Disease (AI‐BOND) training addresses the gaps in study design, biostatistics, genetic epidemiology, and bioinformatics related to neurodegenerative diseases in Africa. The success of AI‐BOND is anticipated to help build computational research capacities in Africa.

Alterations in both oral and gut microbiomes have been associated with Alzheimer’s disease and related dementia (ADRD). While extensive research has focused on the role of gut dysbiosis in ADRD, the contribution of the oral microbiome remains relatively understudied. This study aims to evaluate distinct patterns and potential synergistic effects of oral and gut microbiomes in a cohort of predominantly Hispanic individuals with cognitive impairment (CI) and without cognitive impairment (NC). We conducted 16S rRNA gene sequencing on stool and saliva samples from 32 participants (17 CI, 15 NC; 62.5% female, mean age = 70.4 ± 6.2 years) recruited in San Antonio, Texas, USA. Differential abundance analysis evaluated taxa with significant differences between both groups. While diversity metrics showed no significant differences between CI and NC groups, differential abundance analysis revealed an increased presence of oral genera such as Dialister, Fretibacterium, and Mycoplasma in CI participants. Conversely, CI individuals exhibited a decreased abundance of gut genera, including Shuttleworthia, Holdemania, and Subdoligranulum, which are known for their anti-inflammatory properties. No evidence was found for synergistic contributions between oral and gut microbiomes in the context of CI. Our findings suggest that like the gut microbiome, the oral microbiome of CI participants undergoes significant modifications. Notably, the identified oral microbes have been previously associated with periodontal diseases and gingivitis. These results underscore the necessity for further investigations with larger sample sizes to validate our findings and elucidate the complex interplay between oral and gut microbiomes in ADRD pathogenesis.

Background: Oral and gut microbiomes have been associated with Alzheimer’s disease and related dementias (ADRD). Although the role of the gut microbiome and gut dysbiosis in ADRD has been extensively studied, research on the oral microbiome is lacking. Moreover, the synergetic contribution of oral and gut microbiomes to ADRD is unexplored. This study aimed to assess the differential patterns of oral and gut microbiomes and their synergetic effects in patients with mild cognitive impairment (MCI) compared to normal cognition (NC).Method: Gut and saliva microbiome abundance and diversity measurements were obtained using 16S rRNA gene sequencing of stool and saliva samples from 27 participants (12 MCI, 15 NC, %F = 66.7, Age = 70.2 ± 6.4) recruited in San Antonio, Texas, USA (Table 1). The indexes Chao1, ACE, Observe, Shannon, and Simpson were computed to assess the 〈‐diversity of samples. However, the ®‐diversity was investigated after carrying out the principal coordinates analysis (PCoA) based on Bray‐Curtis distances to visualize group separation in compositional data. We used linear discriminant effect size and differential abundant analysis to identify gut and saliva features statistically different between MCI and NC (adjusted p‐value < 0.005).Result: No differences in bacteria 〈‐ and ®‐diversity between MCI and NC (Figure 1) were found. However, we found an increased abundance of oral pathogenic genera, including Anaeroglobus, Centipeda, Cardiobacterium, Dialister, Fretibacterium, Leptotrichia, Mycoplasma, Tannerella, and Treponema in patients with MCI (Figure 2). We also found that MCI patients have a decreased abundance of gut genera Butyricicoccus, Defluviitaleaceae, Lachnospira, Paludicola, Shuttleworthia, and Subdoligranulum. These differential abundant gut genera have been shown to harbor anti‐inflammatory properties. We did not find evidence of synergetic contributions of oral and gut genera to MCI.Conclusion: Our results suggest that the saliva microbiome, like the gut microbiome, is disrupted as patients progress from NC to MCI. The pathogenic oral microbes we found were previously linked to periodontal and gingivitis pathogens. Further studies with larger sample sizes are needed to validate these findings.

Background: Emerging evidence indicates a complex interplay between cardiovascular health, gut microbiome composition, and cognitive function. Life's Essential 8 (LE8), created by the American Heart Association, encompasses crucial cardiovascular health metrics. This study aimed to explore the relationship between LE8 adherence, gut microbiota, and cognition. Method: We used stool samples, LE8 metrics, and cognitive assessment measures from a sample of 781 participants (mean age 54.9 years, 57.1% Female) from the Framingham Heart Study (generation3, New Offspring Spouses, and the Omni2 cohorts) at the 3 ^rd examination (2016‐2019). Associations between LE8 adherence, gut microbiome diversity, and cognitive performance were evaluated using multivariable linear regression models, adjusting for potential confounders. Mediation analysis was conducted to explore whether specific bacterial taxa mediated the relationship between LE8 adherence and cognitive performance. Result: Participants with greater adherence to LE8 demonstrated significantly increased gut microbial diversity (α‐diversity: Chao1, p = 0.0014; Shannon, p = 0.0071) and distinct microbial compositions (β‐diversity: PERMANOVA p = 1e‐4). Higher adherence to LE8 was related to an increased abundance of genera Barnesiella and Ruminococcus , while reduced abundance of Clostridium was associated with higher LE8 adherence. Greater gut microbial diversity (α‐diversity: Chao1, p = 0.0012; Shannon, p = 0.0066), and beneficial genera like Oscillospira correlated with better global cognitive scores (GCS). Taxonomic overlap analyses revealed microbial taxa that simultaneously influence LE8 adherence and cognitive outcomes. Mediation analyses indicated that specific taxa, including Barnesiella and Lentisphaerae , mediated the link between higher LE8 adherence and better cognitive performance. These taxa could be key modulators in the gut‐brain axis, connecting cardiovascular and brain health. Conversely, higher Clostridium abundance was associated with poorer cognitive performance. Conclusion: This study highlights the interconnected relationship between cardiovascular health, gut microbiome diversity, and cognitive function. Higher adherence to LE8 was associated with favorable microbial profiles and better cognitive performance, with the gut microbiome serving as a critical mediator. These findings emphasize the importance of integrated lifestyle interventions that address cardiovascular and cognitive health simultaneously. To validate these results and refine therapeutic strategies, future research should prioritize longitudinal studies and randomized controlled trials that explore the causal pathways and clinical applications of these findings.