Enhancing Research Through the Use of the Genotype-Tissue Expression (GTEx) Database

A comprehensive analysis of Transcribed Ultra Conserved Regions uncovers important regulatory functions of novel non-coding transcripts in gliomas

Transcribed Ultra-Conserved Regions (TUCRs) represent a severely understudied class of putative non-coding RNAs (ncRNAs) that are 100% conserved across multiple species. We performed the first-ever analysis of TUCRs in glioblastoma (GBM) and low-grade gliomas (LGG). We leveraged large human datasets to identify the genomic locations, chromatin accessibility, transcription, differential expression, correlation with survival, and predicted functions of all 481 TUCRs, and identified TUCRs that are relevant to glioma biology. Of these, we investigated the expression, function, and mechanism of action of the most highly upregulated intergenic TUCR, uc.110, identifying it as a new tumor enhancer. Uc.110 was highly overexpressed in GBM and LGG, where it promoted malignancy and tumor growth. Uc.110 activated the WNT pathway by upregulating the expression of membrane frizzled-related protein (MFRP), by sponging the tumor suppressor microRNA miR-544. This pioneering study shows important roles for TUCRs in gliomas and provides an extensive database and novel methods for future TUCR research.

Identification of dimethyl 2,2'-((methylenebis(2-(2H-benzo[d][1,2,3]triazol-2-yl)-4-(2,4,4-trimethylpentan-2-yl)-6,1phenylene))bis(oxy))diacetate (TAJ4) as antagonist of NEK-Family: a future for potential drug discovery

The purpose of the current study was to analyze and validate the existing gap in knowledge, by conducting a differential expression analysis and validation of NEK6, NEK7, and NEK9 in breast, cervical, and glioblastoma cancer and targeting these proteins through development of novel site specific inhibitor with favorable pharmacokinetic and safety profile, using open-source databases. The analysis revealed that the targeted kinases were overexpressed in all three types of cancer. Their expression was significantly linked to overall survival rates, which suggests that they play a major role in the development and progression of these cancers. After, having the prognostic importance of These findings provided a rationale for synthesizing novel compound i.e., dimethyl 2,2'-((methylenebis(2-(2H-benzo[d][1,2,3]triazol-2-yl)-4-(2,4,4-trimethylpentan-2-yl)-6,1phenylene))bis(oxy))diacetate (TAJ4)), capable of effectively targeting these proteins using in-vitro cytotoxicity assays and comprehensive computational approaches. Then the inhibitory potential of TAJ4 was evaluated against cell lines of the respective cancers (HeLa cells, MCF-7 cells, and Vero cells). The growth inhibitory values (GI50) suggested that TAJ4 exhibited strong inhibitory potential towards MCF-7 cells (GI50 = 3.18 ± 0.11 µM) in comparison to the HeLa cell line (GI50 = 8.12 ± 0.43 µM), surpassing that of standard drugs. Furthermore, in-silico investigations, including density functional theory (DFT) calculations and molecular docking studies, revealed a substantial reactivity profile of TAJ4, with promising molecular interactions against NEK7, NEK9, TP53, NF-KAPPA-B, and caspase-3 proteins. Further investigation using in-vitro and in-vivo approaches is recommended to fully establish the therapeutic efficacy and safety profile of TAJ4.

HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning

The process of developing new drugs is both time-consuming and costly, often taking over a decade and billions of dollars to obtain regulatory approval. Additionally, the complexity of patent protection for novel compounds presents challenges for pharmaceutical innovation. Drug repositioning offers an alternative strategy to uncover new therapeutic uses for existing medicines. Previous repositioning models have been limited by their reliance on homogeneous data sources, failing to leverage the rich information available in heterogeneous biomedical knowledge graphs. We propose HeteroKGRep, a novel drug repositioning model that utilizes heterogeneous graphs to address these limitations. HeteroKGRep is a multi-step framework that first generates a similarity graph from hierarchical concept relations. It then applies SMOTE over-sampling to address class imbalance before generating node sequences using a heterogeneous graph neural network. Drug and disease embeddings are extracted from the network and used for prediction. We evaluated HeteroKGRep on a graph containing biomedical concepts and relations from ontologies, pathways and literature. It achieved state-of-the-art performance with 99% accuracy, 95% AUC ROC and 94% average precision on predicting repurposing opportunities. Compared to existing homogeneous approaches, HeteroKGRep leverages diverse knowledge sources to enrich representation learning. Based on heterogeneous graphs, HeteroKGRep can discover new drug-desease associations, leveraging de novo drug development. This work establishes a promising new paradigm for knowledge-guided drug repositioning using multimodal biomedical data.

Genome-wide meta-analysis conducted in three large biobanks expands the genetic landscape of lumbar disc herniations

Given that lumbar disc herniation (LDH) is a prevalent spinal condition that causes significant individual suffering and societal costs, the genetic basis of LDH has received relatively little research. Our aim is to increase understanding of the genetic factors influencing LDH. We perform a genome-wide association analysis (GWAS) of LDH in the FinnGen project and in Estonian and UK biobanks, followed by a genome-wide meta-analysis to combine the results. In the meta-analysis, we identify 41 loci that have not been associated with LDH in prior studies on top of the 23 known risk loci. We detect LDH-associated loci in the vicinity of genes related to inflammation, disc-related structures, and synaptic transmission. Overall, our research contributes to a deeper understanding of the genetic factors behind LDH, potentially paving the way for the development of new therapeutics, prevention methods, and treatments for symptomatic LDH in the future.

Scoulerine: A natural isoquinoline alkaloid targeting SLC6A3 to treat RCC

Scoulerine, an isoquinoline alkaloid derived from the corydalis plant, exhibits diverse therapeutic properties against tumors, Alzheimer's disease, and inflammation. This research delves into the pharmacological impact and underlying mechanism of scoulerine on renal cell carcinoma (RCC). Our findings suggest that Scoulerine displays promise as a potential therapeutic agent for RCC, demonstrating notable inhibitory effects in both in vivo and in vitro models. In addition, scoulerine inhibited the viability of 769-P and 786-O cell lines in a time-dependent and dose-dependent manner, and promoted the level of apoptosis associated with B-cell lymphoma-2 associated X protein (Bax). Moreover, the administration of scoulerine resulted in a significant suppression of the mitogen activated protein kinase (MAPK) signaling pathway. Subsequently, utilizing bioinformatics and spatial transcriptomic databases, we identified solute carrier family 6 Member 3 (SLC6A3) as the most promising target of scoulerine. Through experimental validation, we confirmed the functional and therapeutic relevance of SLC6A3 in scoulerine-mediated treatment of RCC. The results of our study indicate a significant affinity between scoulerine and SLC6A3, with competitive inhibition of this interaction leading to a reduction in the inhibitory impact of scoulerine on RCC cell viability. In conclusion, our findings suggest that scoulerine may induce apoptosis in RCC by targeting SLC6A3 and inhibiting the activation of the MAPK signaling pathway, thereby positioning it as a promising natural compound for potential future RCC treatment.

Genetic Variants at PRKCG Splice and UTR Sites Promote Cancer Susceptibility by Disrupting Epigenetic and miRNA Regulatory Network

The changes in the protein kinase C gamma gene (PRKCG) expression are associated with both coding and non-coding variants. No studies have specifically established the association between PRKCG 3'UTR, 5'UTR, donor and acceptor splice variants with post-transcriptional changes through utilizing in-silico tools. The current study intends to uncover this linkage. In total, 419 3' and 5'UTR variants were retrieved. 325 of these variant IDs were annotated as functionally significant. 18 variants impacted the transcription factors binding and therefore influenced the post-transcriptional regulatory activity while 7 variants affected regulatory mechanisms through histone modifications. 2 rsIDs (rs373228, rs446795) potentially impacted the interactions with RNA binding proteins. In addition to that, PRKCG showed high expression in brain cells and had variable expression in TCGA tumors, respectively. Furthermore, 5 3' UTR variants were identified to be targeted by miRNAs. In total, 5 of these miRNAs (hsa-miR-663a, hsa-miR-324-5p, hsa-miR-646, hsa-miR-1205 and hsa-miR-4270) that targeted 3'UTRs (rs57483118, rs181418157 and rs60891969) showed differential expressions in distinct cancer types. The presence of 3'UTR variants likely altered the secondary structure of mRNA. The 7 rsIDs at 3' UTR site caused the loss of function of authentic splice site at 10 positions was noted; at 1 position, gain of function was observed while at 2 positions no effect was identified. Moreover, the loss of donor and acceptor splice site was evident. Our results highlight the importance of non-coding regions that might boost our research capacity to predict and construct targeted therapeutic approaches.

Genetic polymorphism in untranslated regions of PRKCZ influences mRNA structure, stability and binding sites

BACKGROUND

Variations in untranslated regions (UTR) alter regulatory pathways impacting phenotype, disease onset, and course of disease. Protein kinase C Zeta (PRKCZ), a serine-threonine kinase, is implicated in cardiovascular, neurological and oncological disorders. Due to limited research on PRKCZ, this study aimed to investigate the impact of UTR genetic variants' on binding sites for transcription factors and miRNA. RNA secondary structure, eQTLs, and variation tolerance analysis were also part of the study.

METHODS

The data related to PRKCZ gene variants was downloaded from the Ensembl genome browser, COSMIC and gnomAD. The RegulomeDB database was used to assess the functional impact of 5' UTR and 3'UTR variants. The analysis of the transcription binding sites (TFBS) was done through the Alibaba tool, and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) was employed to identify pathways associated with PRKCZ. To predict the effect of variants on microRNA binding sites, PolymiRTS was utilized for 3' UTR variants, and the SNPinfo tool was used for 5' UTR variants.

RESULTS

The results obtained indicated that a total of 24 variants present in the 3' UTR and 25 variants present in the 5' UTR were most detrimental. TFBS analysis revealed that 5' UTR variants added YY1, repressor, and Oct1, whereas 3' UTR variants added AP-2alpha, AhR, Da, GR, and USF binding sites. The study predicted TFs that influenced PRKCZ expression. RNA secondary structure analysis showed that eight 5' UTR and six 3' UTR altered the RNA structure by either removal or addition of the stem-loop. The microRNA binding site analysis highlighted that seven 3' UTR and one 5' UTR variant altered the conserved site and also created new binding sites. eQTLs analysis showed that one variant was associated with PRKCZ expression in the lung and thyroid. The variation tolerance analysis revealed that PRKCZ was an intolerant gene.

CONCLUSION

This study laid the groundwork for future studies aimed at targeting PRKCZ as a therapeutic target.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Novel indel variation of LTBP4 gene associates with risk of sudden cardiac death in Chinese populations with coronary artery disease

The objective of this study is to investigate whether common genetic variants of the LTBP4 gene are linked to the susceptibility of sudden cardiac death in individuals who have atherosclerotic coronary artery disease (SCD-CAD) in Chinese populations. A total of 208 SCD-CAD cases and 638 controls were included in the analysis, and logistic regression was employed to assess the association between a 4-bp insertion/deletion polymorphism (rs34005443) within LTBP4 and the susceptibility to SCD-CAD among Chinese individuals. Logistic regression analysis demonstrated a notable association between the insertion allele of rs34005443 and an escalated susceptibility to SCD-CAD [odds ratio (OR) = 1.434; 95 % confidence interval:1.14-1.80; P = 1.79 × 10-3]. Genotype-phenotype correlation analysis was performed using Genotype-Tissue expression (GTEx) database and further validated by human myocardium using qPCR. Correlation analysis revealed that LTBP4 expression level was lower in samples with the insertion allele. Furthermore, the dual-luciferase activity assays indicated that rs34005443 may play a regulatory role. Additionally, we predicted 30 transcription factors that are likely to bind to rs34005443 and its highly linked genetic variants via 3DSNP database. Subsequent GO and KEGG analysis indicated that these transcription factors have a significant function in regulating gene expression. Finally, PPI network analysis suggested a tight connection between LTBP4 proteins and TGFβs, highlighting these genes as potential hub genes in the context of SCD-CAD. In summary, our study revealed that rs34005443 might contribute to SCD-CAD susceptibility by regulating LTBP4 expression. These findings revealed that this indel could be a potentially functional marker for molecular diagnosis and risk stratification of SCD-CAD.

A first comprehensive analysis of Transcribed Ultra Conserved Regions uncovers important regulatory functions of novel non-coding transcripts in gliomas

Transcribed Ultra-Conserved Regions (TUCRs) represent a severely understudied class of putative non-coding RNAs (ncRNAs) that are 100% conserved across multiple species. We performed the first-ever analysis of TUCRs in glioblastoma (GBM) and low-grade gliomas (LGG). We leveraged large human datasets to identify the genomic locations, chromatin accessibility, transcription, differential expression, correlation with survival, and predicted functions of all 481 TUCRs, and identified TUCRs that are relevant to glioma biology. Of these, we investigated the expression, function, and mechanism of action of the most highly upregulated intergenic TUCR, uc.110, identifying it as a new oncogene. Uc.110 was highly overexpressed in GBM and LGG, where it promoted malignancy and tumor growth. Uc.110 activated the WNT pathway by upregulating the expression of membrane frizzled-related protein (MFRP), by sponging the tumor suppressor microRNA miR-544. This pioneering study shows important roles for TUCRs in gliomas and provides an extensive database and novel methods for future TUCR research.

Genome-wide association studies highlight novel risk loci for septal defects and left-sided congenital heart defects

BACKGROUND

Congenital heart defects (CHD) are structural defects of the heart affecting approximately 1% of newborns. They exhibit low penetrance and non-Mendelian patterns of inheritance as varied and complex traits. While genetic factors are known to play an important role in the development of CHD, the specific genetics remain unknown for the majority of patients. To elucidate the underlying genetic risk, we performed a genome wide association study (GWAS) of CHDs in general and specific CHD subgroups using the FinnGen Release 10 (R10) (N > 393,000), followed by functional fine-mapping through eQTL and co-localization analyses using the GTEx database.

RESULTS

We discovered three genome-wide significant loci associated with general CHD. Two of them were located in chromosome 17: 17q21.32 (rs2316327, intronic: LRRC37A2, Odds ratio (OR) [95% Confidence Interval (CI)] = 1.17[1.12-1.23], p = 1.5 × 10-9) and 17q25.3 (rs1293973611, nearest: BAHCC1, OR[95%CI] = 4.48[2.80-7.17], p = 7.0 × 10-10), respectively, and in addition to general CHD, the rs1293973611 locus was associated with the septal defect subtype. The third locus was in band 1p21.2 (rs35046143, nearest: PALMD, OR[95%CI] = 1.15[1.09-1.21], p = 7.1 × 10-9), and it was associated with general CHD and left-sided lesions. In the subgroup analysis, two additional loci were associated with septal defects (rs75230966 and rs6824295), and one with left-sided lesions (rs1305393195). In the eQTL analysis the variants rs2316327 (general CHD), and rs75230966 (septal defects) both located in 17q21.32 (with a LD r2 of 0.41) were both predicted to significantly associate with the expression of WNT9B in the atrial appendage tissue category. This effect was further confirmed by co-localization analysis, which also implicated WNT3 expression in the atrial appendage. A meta-analysis of general CHD together with the UK Biobank (combined N = 881,678) provided a different genome-wide significant locus in LRRC37A2; rs16941382 (OR[95%CI] = 1.15[1.11-1.20], p = 1.5 × 10-9) which is in significant LD with rs2316327.

CONCLUSIONS

Our results of general CHD and different CHD subcategories identified a complex risk locus on chromosome 17 near BAHCC1 and LRRC37A2, interacting with the genes WNT9B, WNT3 and MYL4, may constitute potential novel CHD risk associated loci, warranting future experimental tests to determine their role.

Genetics of sleep medication purchases suggests causality from sleep problems to psychiatric traits

STUDY OBJECTIVES

Over 10% of the population in Europe and in the United States use sleep medication to manage sleep problems. Our objective was to elucidate genetic risk factors and clinical correlates that contribute to sleep medication purchase and estimate the comorbid impact of sleep problems.

METHODS

We performed epidemiological analysis for psychiatric diagnoses, and genetic association studies of sleep medication purchase in 797 714 individuals from FinnGen Release 7 (N = 311 892) and from the UK Biobank (N = 485 822). Post-association analyses included genetic correlation, co-localization, Mendelian randomization (MR), and polygenic risk estimation.

RESULTS

In a GWAS we identified 27 genetic loci significantly associated with sleep medication, located in genes associated with sleep; AUTS2, CACNA1C, MEIS1, KIRREL3, PAX8, GABRA2, psychiatric traits; CACNA1C, HIST1H2BD, NUDT12. TOPAZ1 and TSNARE1. Co-localization and expression analysis emphasized effects on the KPNA2, GABRA2, and CACNA1C expression in the brain. Sleep medications use was epidemiologically related to psychiatric traits in FinnGen (OR [95% (CI)] = 3.86 [3.78 to 3.94], p < 2 × 10-16), and the association was accentuated by genetic correlation and MR; depression (rg = 0.55 (0.027), p = 2.86 × 10-89, p MR = 4.5 × 10-5), schizophrenia (rg = 0.25 (0.026), p = 2.52 × 10-21, p MR = 2 × 10-4), and anxiety (rg = 0.44 (0.047), p = 2.88 × 10-27, p MR = 8.6 × 10-12).

CONCLUSIONS

These results demonstrate the genetics behind sleep problems and the association between sleep problems and psychiatric traits. Our results highlight the scientific basis for sleep management in treating the impact of psychiatric diseases.

The Association Between Thymidylate Synthase Gene Polymorphisms and the Risk of Ischemic Stroke in Chinese Han Population

Family history of hypertension, smoking, diabetes and alcohol consumption and atherosclerotic plaque were identified as common risk factors in IS. We aimed at investigating the relationship between Thymidylate Synthase (TS) gene polymorphisms and ischemic stroke (IS).This case-control research selected and genotyped three single nucleotide polymorphisms (SNPs)of TS( rs699517, rs2790, and rs151264360) with Sanger sequencing in Chinese Han population. We also adopted logistic regression analysis in genetic models for calculating odds ratios and 95% confidence intervals. Genotype-Tissue Expression(GTEx) database analyzed the tissue-specific expression and TS polymorphisms. The ischemic stroke patients showed higher low-density lipoprotein cholesterol and total homocysteine (tHcy). It was found that patients with the TT genotype of rs699517 and GG genotype of rs2790 had larger degrees of tHcy than those with CC + CT genotypes and AA + AG genotypes, respectively. The genotype distribution of the three SNPs did not deviate from Hardy-Weinberg equilibrium (HWE). Haplotype analysis showed that T-G-del was the major haplotype in IS, and C-A-ins was the major haplotype in controls. GTEx database indicated that the rs699517 and rs2790 increased the expression of TS in healthy human and associated with TS expression level in a single tissue. In conclusion: This study has shown that TS rs699517 and rs2790 were significantly related to ischemic stroke patients.

Application of data science and bioinformatics in RNA therapeutics

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.

Genome-Wide Association Study of Blood Mercury in European Pregnant Women and Children

Mercury has high industrial utility and is present in many products, and environmental contamination and occupational exposure are widespread. There are numerous biological systems involved in the absorption, metabolism, and excretion of Hg, and it is possible that some systems may be impacted by genetic variation. If so, genotype may affect tissue concentrations of Hg and subsequent toxic effects. Genome-wide association testing was performed on blood Hg samples from pregnant women of the Avon Longitudinal Study of Parents and Children (n = 2893) and children of the Human Early Life Exposome (n = 1042). Directly-genotyped single-nucleotide polymorphisms (SNPs) were imputed to the Haplotype Reference Consortium r1.1 panel of whole genotypes and modelled againstlog-transformed Hg. Heritability was estimated using linkage disequilibrium score regression. The heritability of Hg was estimated as 24.0% (95% CI: 16.9% to 46.4%) in pregnant women, but could not be determined in children. There were 16 SNPs associated with Hg in pregnant women above a suggestive p-value threshold (p < 1 × 10-5), and 21 for children. However, no SNP passed this threshold in both studies, and none were genome-wide significant (p < 5 × 10-8). SNP-Hg associations were highly discordant between women and children, and this may reflect differences in metabolism, a gene-age interaction, or dose-response effects. Several suggestive variants had plausible links to Hg metabolism, such as rs146099921 in metal transporter SLC39A14, and two variants (rs28618224, rs7154700) in potassium voltage-gated channel genes. The findings would benefit from external validation, as suggestive results may contain both true associations and false positives.

Analysis of age-dependent gene-expression in human tissues for studying diabetes comorbidities

The study of the relationship between type 2 diabetes mellitus (T2DM) disease and other pathologies (comorbidities), together with patient age variation, poses a challenge for medical research. There is evidence that patients affected by T2DM are more likely to develop comorbidities as they grow older. Variation of gene expression can be correlated to changes in T2DM comorbidities insurgence and progression. Understanding gene expression changes requires the analysis of large heterogeneous data at different scales as well as the integration of different data sources into network medicine models. Hence, we designed a framework to shed light on uncertainties related to age effects and comorbidity by integrating existing data sources with novel algorithms. The framework is based on integrating and analysing existing data sources under the hypothesis that changes in the basal expression of genes may be responsible for the higher prevalence of comorbidities in older patients. Using the proposed framework, we selected genes related to comorbidities from existing databases, and then analysed their expression with age at the tissues level. We found a set of genes that changes significantly in certain specific tissues over time. We also reconstructed the associated protein interaction networks and the related pathways for each tissue. Using this mechanistic framework, we detected interesting pathways related to T2DM whose genes change their expression with age. We also found many pathways related to insulin regulation and brain activities, which can be used to develop specific therapies. To the best of our knowledge, this is the first study that analyses such genes at the tissue level together with age variations.

Decoding the genetic relationship between Alzheimer's disease and type 2 diabetes: potential risk variants and future direction for North Africa

Introduction

Alzheimer's disease (AD) and Type 2 diabetes (T2D) are both age-associated diseases. Identification of shared genes could help develop early diagnosis and preventive strategies. Although genetic background plays a crucial role in these diseases, we noticed an underrepresentation tendency of North African populations in omics studies.

Materials and methods

First, we conducted a comprehensive review of genes and pathways shared between T2D and AD through PubMed. Then, the function of the identified genes and variants was investigated using annotation tools including PolyPhen2, RegulomeDB, and miRdSNP. Pathways enrichment analyses were performed with g:Profiler and EnrichmentMap. Next, we analyzed variant distributions in 16 worldwide populations using PLINK2, R, and STRUCTURE software. Finally, we performed an inter-ethnic comparison based on the minor allele frequency of T2D-AD common variants.

Results

A total of 59 eligible papers were included in our study. We found 231 variants and 363 genes shared between T2D and AD. Variant annotation revealed six single nucleotide polymorphisms (SNP) with a high pathogenic score, three SNPs with regulatory effects on the brain, and six SNPs with potential effects on miRNA-binding sites. The miRNAs affected were implicated in T2D, insulin signaling pathways, and AD. Moreover, replicated genes were significantly enriched in pathways related to plasma protein binding, positive regulation of amyloid fibril deposition, microglia activation, and cholesterol metabolism. Multidimensional screening performed based on the 363 shared genes showed that main North African populations are clustered together and are divergent from other worldwide populations. Interestingly, our results showed that 49 SNP associated with T2D and AD were present in North African populations. Among them, 11 variants located in DNM3, CFH, PPARG, ROHA, AGER, CLU, BDNF1, CST9, and PLCG1 genes display significant differences in risk allele frequencies between North African and other populations.

Conclusion

Our study highlighted the complexity and the unique molecular architecture of North African populations regarding T2D-AD shared genes. In conclusion, we emphasize the importance of T2D-AD shared genes and ethnicity-specific investigation studies for a better understanding of the link behind these diseases and to develop accurate diagnoses using personalized genetic biomarkers.

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

The role of ATG16L2 in autophagy and disease

Macroautophagy/autophagy, a fundamental cell process for nutrient recycling and defense against pathogens (termed xenophagy), is crucial to human health. ATG16L2 (autophagy related 16 like 2) is an autophagic protein and a paralog of ATG16L1. Both proteins are implicated in similar diseases such as cancer and other chronic diseases; however, most autophagy studies to date have primarily focused on the function of ATG16L1, with ATG16L2 remaining uncharacterized and understudied. Overexpression of ATG16L2 has been reported in various cancers including colorectal, gastric, and prostate carcinomas, whereas altered methylation of ATG16L2 has been associated with lung cancer formation and poorer response to therapy in leukemia. In addition, ATG16L2 polymorphisms have been implicated in a range of other diseases including inflammatory bowel diseases and neurodegenerative disorders. Despite this likely role in human health, the function of this enigmatic protein in autophagy remains unknown. Here, we review current studies on ATG16L2 and collate evidence that suggests that this protein is a potential modulator of autophagy as well as the implications this has on pathogenesis.Abbreviations: ATG5: autophagy related 5; ATG12: autophagy related 12; ATG16L1: autophagy related 16 like 1; ATG16L2: autophagy related 16 like 2; CD: Crohn disease; IBD: inflammatory bowel diseases; IRGM: immunity related GTPase M; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; PE: phosphatidylethanolamine; RB1CC1: RB1 inducible coiled-coil 1; SLE: systemic lupus erythematosus; WIPI2B: WD repeat domain, phosphoinositide interacting 2B.