BACKGROUND: It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation.
METHODS: The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria.
RESULTS: The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation.
CONCLUSIONS: The implemented workflow could be used for other multifactorial diseases.

Recent decades have brought enormous progress in both genetics and genomics, as well as in information technology (IT). The sequence of the human genome is now known, and although our knowledge is far from complete, great progress has been made in understanding how the genome works. With the developments in storage capacity, artificial intelligence, and learning algorithms, we are now able to learn and interpret complex systems such as the human genome in a very short time. Perhaps the most important goal of learning about the human genome is to understand diseases better: how they develop; how their processes can be prevented or slowed down; and after diseases have developed, how they can be cured or their symptoms alleviated. The vast majority of diseases have a genetic background, i.e., genes, sequence variations, and gene-gene interactions play a role in most diseases to a greater or lesser extent. Accordingly, the first step is to discover which genes, or genomic variants, cause or contribute to the development of a particular disease in a given patient. Given that an individual\'s genome remains virtually unchanged throughout their life (with one or two exceptions, such as in the case of cancer, which is caused by somatic mutations), it might be considered advantageous to sequence the genome of every person at birth. In this paper, we set out to show the possible benefits of sequencing the entire genome of every human being at birth, while also discussing the main arguments against it.

Designing and discovering compounds for dual-target inhibitors is challenging to synthesize new, safer, and more efficient drugs than single-target drugs, especially to treat multifactorial diseases such as cancer. The simultaneous regulation of multiple targets might represent an alternative synthetic approach to optimize patient compliance and tolerance, minimizing the risk of target-based drug resistance due to the modulation of a few targets. To this end, we conceived for the first time the design and synthesis of dual-ligands σR/HDACi to evaluate possible employment as innovative candidates to address this complex disease. Among all synthesized compounds screened for several tumoral cell lines, compound 6 (Kiσ1R = 38 ± 3.7; Kiσ2R = 2917 ± 769 and HDACs IC50 = 0.59 µM) is the most promising candidate as an antiproliferative agent with an IC50 of 0.9 µM on the HCT116 cell line and no significant toxicity to normal cells. Studies of molecular docking, which confirmed the affinity over σ1R and a pan-HDACs inhibitory behavior, support a possible balanced affinity and activity between both targets.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

UNASSIGNED: Periodontitis affects up to one billion people worldwide, and has been proven to be associated with several systemic inflammatory conditions. This study investigates the specific relationship between two multifactorial diseases: Inflammatory bowel disease (IBD) and periodontitis. To thoroughly explore this issue, we investigated separately whether IBD patients have a higher chance of developing periodontitis, and equally, whether patients with periodontitis have a higher chance of developing IBD.
UNASSIGNED: The systematic search was performed in three databases: MEDLINE, Cochrane Trials, and Embase, up to 26 October 2021. The protocol was registered in PROSPERO. All eligible studies investigating the association between IBD and periodontitis from either direction were included. The Newcastle-Ottawa Scale was used to assess the risk of bias. As a primary outcome, we investigated the prevalence of IBD and periodontitis, and calculated the odds ratio (OR). Our secondary outcomes involved comparing the clinical periodontal outcomes of IBD patients to those of IBD-free patients.
UNASSIGNED: The systematic search resulted in 1,715 records, 14 of which were eligible for qualitative synthesis and 8 for quantitative synthesis. On the basis of the results of the primary outcome, IBD diagnosis was associated with significantly higher odds of periodontitis: OR = 2.65 (CI: 2.09-3.36, I 2 = 0 (CI: 0-0.75)). For subgroup analysis, we investigated separately the odds in Crohn\'s disease (CD) patients: OR = 2.22 (CI: 1.49-3.31, I 2 = 0.05 (CI: 0-0.76)) and in ulcerative colitis (UC) patients: OR = 3.52 (CI: 2.56 to 4.83, I 2 = 0 (CI: 0-0.75)); the odds were significantly higher in all cases. Two studies investigated whether patients with periodontitis were more susceptible to IBD, and both found that periodontitis was significantly associated with the risk of subsequent UC, but not with subsequent CD. However, more studies are needed to prove an association.
UNASSIGNED: Our analysis confirmed that IBD patients have a higher chance of developing periodontitis, and are a higher risk population in dentistry. Both dentists and gastroenterologists should be aware of this relationship and should emphasize the importance of prevention even more than in the healthy population.
UNASSIGNED: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42021286161].

Multifactorial diseases exhibit a complex pathophysiology with several factors contributing to their pathogenesis and development. Examples of such disorders are neurodegenerative (e. g. Alzheimer\'s, Parkinson\'s) and cardiovascular diseases (e. g. atherosclerosis, metabolic syndrome, diabetes II). Traditional therapeutic approaches with single-target drugs have been proven, in many cases, unsatisfactory for the treatment of multifactorial diseases such as diabetes II. The well-established by now strategy of multitarget drugs is constantly gaining interest and momentum, as a more effective approach. The development of pharmacomolecules able to simultaneously modulate multiple relevant-to-the-disease targets has already several successful examples in various fields and has, as such, inspired the design of multitarget antidiabetic agents; this review highlights the design aspect and efficacy of this approach for improved antidiabetics by presenting several examples of successful pharmacophore combinations in (multitarget) agents that modulate two or more molecular targets involved in diabetes II, resulting in a superior antihyperglycemic profile.

Multitarget anti-Alzheimer agents are the focus of very intensive research. Through a comprehensive bibliometric analysis of the publications in the period 1990-2020, we have identified trends and potential gaps that might guide future directions. We found that: (i) the number of publications boomed by 2011 and continued ascending in 2020; (ii) the linked-pharmacophore strategy was preferred over design approaches based on fusing or merging pharmacophores or privileged structures; (iii) a significant number of in vivo studies, mainly using the scopolamine-induced amnesia mouse model, have been performed, especially since 2017; (iv) China, Italy and Spain are the countries with the largest total number of publications on this topic, whereas Portugal, Spain and Italy are the countries in whose scientific communities this topic has generated greatest interest; (v) acetylcholinesterase, β-amyloid aggregation, oxidative stress, butyrylcholinesterase, and biometal chelation and the binary combinations thereof have been the most commonly pursued, while combinations based on other key targets, such as tau aggregation, glycogen synthase kinase-3β, NMDA receptors, and more than 70 other targets have been only marginally considered. These results might allow us to spot new design opportunities based on innovative target combinations to expand and diversify the repertoire of multitarget drug candidates and increase the likelihood of finding effective therapies for this devastating disease.

Current findings on multifactorial diseases with a complex pathomechanism confirm that multi-target drugs are more efficient ways in treating them as opposed to single-target drugs. However, to design multi-target ligands, a number of factors and challenges must be taken into account.
In this perspective, we summarize the concept of application of multi-target drugs for the treatment of complex diseases such as neurodegenerative diseases, schizophrenia, diabetes, and cancer. We discuss the aspects of target selection for multifunctional ligands and the application of in silico methods in their design and optimization. Furthermore, we highlight other challenges such as balancing affinities to different targets and drug-likeness of obtained compounds. Finally, we present success stories in the design of multi-target ligands for the treatment of common complex diseases.
Despite numerous challenges resulting from the design of multi-target ligands, these efforts are worth making. Appropriate target selection, activity balancing, and ligand drug-likeness belong to key aspects in the design of ligands acting on multiple targets. It should be emphasized that in silico methods, in particular inverse docking, pharmacophore modeling, machine learning methods and approaches derived from network pharmacology are valuable tools for the design of multi-target drugs.

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Esophageal atresia with or without tracheoesophageal fistula (EA/TEF) is the most common congenital malformation of the upper digestive tract. This study represents the first genome-wide association study (GWAS) to identify risk loci for EA/TEF. We used a European case-control sample comprising 764 EA/TEF patients and 5,778 controls and observed genome-wide significant associations at three loci. On chromosome 10q21 within the gene CTNNA3 (p = 2.11 × 10-8; odds ratio [OR] = 3.94; 95% confidence interval [CI], 3.10-5.00), on chromosome 16q24 next to the FOX gene cluster (p = 2.25 × 10-10; OR = 1.47; 95% CI, 1.38-1.55) and on chromosome 17q12 next to the gene HNF1B (p = 3.35 × 10-16; OR = 1.75; 95% CI, 1.64-1.87). We next carried out an esophageal/tracheal transcriptome profiling in rat embryos at four selected embryonic time points. Based on these data and on already published data, the implicated genes at all three GWAS loci are promising candidates for EA/TEF development. We also analyzed the genetic EA/TEF architecture beyond the single marker level, which revealed an estimated single-nucleotide polymorphism (SNP)-based heritability of around 37% ± 14% standard deviation. In addition, we examined the polygenicity of EA/TEF and found that EA/TEF is less polygenic than other complex genetic diseases. In conclusion, the results of our study contribute to a better understanding on the underlying genetic architecture of ET/TEF with the identification of three risk loci and candidate genes.