The HLA compatibility continues to be the main limitation when finding compatible donors, especially if an identical match is not found within the patient's family group. The creation of bone marrow ...registries allowed a therapeutic option by identifying 10/10 compatible unrelated donors (URD). However, the availability and frequency of haplotypes and HLA alleles are different among ethnic groups and geographical areas, increasing the difficulty of finding identical matches in international registries. In this study, the HLA-A, -B, -C, -DRB1, and -DQB1 loci of 1763 donors registered in the Colombian Bone Marrow Registry were typed by next-generation sequencing. A total of 52 HLA-A, 111 HLA-B, 41 HLA-C, 47 HLA-DRB1, and 20 HLA-DQB1 alleles were identified. The 3 most frequent alleles for each loci were A*24:02g (20,8%), A*02:01g (16,1%), A*01:01g (7.06%); B*35:43g (7.69%), B*40:02g (7.18%), B*44:03g (6.07%); C*04:01g (15.40%), C*01:02g (10.49%), C*07:02g (10.44%); DRB1*04:07g (11.03%), DRB1*07:01g (9.78%), DRB1*08:02g (6.72%); DQB1*03:02g (20.96%), DQB1*03:01g (17.78%) and DQB1*02:01g (16.05%). A total of 497 HLA-A-C-B-DRB1-DQB1 haplotypes were observed with a frequency greater than or equal to 0.05% (> 0.05%); the haplotypes with the highest frequency were A*24:02g~B*35:43g~C*01:02g~DQB1*03:02g~DRB1*04:07g (3.34%), A*29:02g~B*44:03g~C*16:01g~DQB1*02:01g~DRB1*07:01g (2.04%), and A*01:01g~B*08:01g~C*07:01g~DQB1*02:01g~DRB1*03:01g (1.83%). This data will allow the new Colombian Bone Marrow Donor Registry to assess the genetic heterogeneity of the Colombian population and serve as a tool of interest for future searches of unrelated donors in the country.
INTRODUCTION
We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early‐onset Alzheimer's disease (EOAD).
METHODS
We included baseline data from 77 ...cognitively normal (CN), 230 EOAD, and 70 EO non‐Alzheimer's disease (EOnonAD) participants from the Longitudinal Early‐Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification. Voxel‐wise multiple linear regressions yielded statistical brain maps of gray matter density, amyloid, and tau PET burden.
RESULTS
EOAD females had greater amyloid and tau PET burdens than males. EOAD female APOE ε4 non‐carriers had greater amyloid PET burdens and greater gray matter atrophy than female ε4 carriers. EOnonAD female ε4 non‐carriers also had greater gray matter atrophy than female ε4 carriers.
DISCUSSION
The effects of sex and APOE ε4 must be considered when studying these populations.
HIGHLIGHTS
Novel analysis examining the effects of biological sex and apolipoprotein E ε4 (APOE ε4) carrier status on neuroimaging biomarkers among early‐onset Alzheimer's disease (EOAD), early‐onset non‐AD (EOnonAD), and cognitively normal (CN) participants.
Female sex is associated with greater pathology burden in the EOAD cohort compared to male sex.
The effect of APOE ε4 carrier status on pathology burden was the most impactful in females across all cohorts.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Multiomic data analysis has been extensively developed in recent years. Particularly, microarray data can be used as biomarkers of disease of progression specially in pathologies as ...cancer, autoimmunity and others. (Dahinden et al., 2010; Soleimani Zakeri et al., 2020). This modality of data analysis can also be employed in other multifactorial diseases as Alzheimer’s disease including the progression from normal cognition to mild cognitive impairment and finally to dementia. These genes work in intricate relationship with each other. The establishment of computational gene interaction networks shows the intricacy of the biological systems but increases the complexity of the analysis, for this reason, the removal of redundant edges leaves a network backbone revealing important driver nodes that are essential for the observed interactions, reducing the number of genes of interest and narrowing potential diagnostic or therapeutic targets. (Gates et al., 2021)
Methods
We used data from the ImaGENE study and the Weighted gene co‐expression network analysis (WGCNA) pipeline to analyze gene‐gene interactions of mRNA transcripts obtaining hierarchical clustering of genes and eigengenes in a sample of 160 individuals (Table 1). The backbones were obtained using the shortest path computation. (Gates et al., 2021; Simas et al., 2021). Gene ontology was used to classify processes associated with these genes.
Results
We identified 42 hubs of differentially expressed genes and we analyzed the top 5 groups with the strongest association with amnestic mild cognitive impairment (MCI) phenotype (pfdr<0.0001). This cluster consisting of 46 genes was further classified according to function and interactions with other nodes in the backbone identifying processes like calcium signaling, nucleotide excision repair, fatty acid metabolism among others.
Conclusion
There is an identifiable difference in gene expression in individuals with MCI compared to normal controls. The analysis of the backbone of the gene hubs allowed to reduce the number of genes of interest. This approach can help to elucidate important biological interactions, potential therapeutic targets and could eventually be used as risk factors markers for the development of MCI and AD.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background
Multiomic data analysis has been extensively developed in recent years. Particularly, microarray data can be used as biomarkers of disease of progression specially in pathologies as ...cancer, autoimmunity and others. (Dahinden et al., 2010; Soleimani Zakeri et al., 2020). This modality of data analysis can also be employed in other multifactorial diseases as Alzheimer’s disease including the progression from normal cognition to mild cognitive impairment and finally to dementia. These genes work in intricate relationship with each other. The establishment of computational gene interaction networks shows the intricacy of the biological systems but increases the complexity of the analysis, for this reason, the removal of redundant edges leaves a network backbone revealing important driver nodes that are essential for the observed interactions, reducing the number of genes of interest and narrowing potential diagnostic or therapeutic targets. (Gates et al., 2021).
Method
We used data from the ImaGENE study and the Weighted gene co‐expression network analysis (WGCNA) pipeline to analyze gene‐gene interactions of mRNA transcripts obtaining hierarchical clustering of genes and eigengenes in a sample of 160 individuals (Table 1). The backbones were obtained using the shortest path computation. (Gates et al., 2021; Simas et al., 2021). Gene ontology was used to classify processes associated with these genes.
Result
We identified 42 hubs of differentially expressed genes and we analyzed the top 5 groups with the strongest association with amnestic mild cognitive impairment (MCI) phenotype (pfdr<0.0001). This cluster consisting of 46 genes was further classified according to function and interactions with other nodes in the backbone identifying processes like calcium signaling, nucleotide excision repair, fatty acid metabolism among others.
Conclusion
There is an identifiable difference in gene expression in individuals with MCI compared to normal controls. The analysis of the backbone of the gene hubs allowed to reduce the number of genes of interest. This approach can help to elucidate important biological interactions, potential therapeutic targets and could eventually be used as risk factors markers for the development of MCI and AD.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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