HDL levels modulate the impact of type 2 diabetes susceptibility alleles in older adults.

BACKGROUND: Type 2 Diabetes (T2D) is influenced by genetic, environmental, and ageing factors. Ageing pathways exacerbate metabolic diseases. This study aimed to examine both clinical and genetic factors of T2D in older adults. METHODS: A total of 2,909 genotyped patients were enrolled in this study. Genome Wide Association Study was conducted, comparing T2D patients to non-diabetic older adults aged ≥ 60, ≥ 65, or ≥ 70 years, respectively. Binomial logistic regressions were applied to examine the association between T2D and various risk factors. Stepwise logistic regression was conducted to explore the impact of low HDL (HDL < 40 mg/dl) on the relationship between the genetic variants and T2D. A further validation step using data from the UK Biobank with 53,779 subjects was performed. RESULTS: The association of T2D with both low HDL and family history of T2D increased with the age of control groups. T2D susceptibility variants (rs7756992, rs4712523 and rs10946403) were associated with T2D, more significantly with increased age of the control group. These variants had stronger effects on T2D risk when combined with low HDL cholesterol levels, especially in older control groups. CONCLUSIONS: The findings highlight a critical role of age, genetic predisposition, and HDL levels in T2D risk. The findings suggest that individuals over 70 years who have high HDL levels without the T2D susceptibility alleles may be at the lowest risk of developing T2D. These insights can inform tailored preventive strategies for older adults, enhancing personalized T2D risk assessments and interventions.

Critical assessment of on-premise approaches to scalable genome analysis.

BACKGROUND: Plummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype-phenotype predictions in complex diseases. METHODS: In order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability. RESULTS: Tools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database. CONCLUSION: The assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.

Microbiome-based disease prediction with multimodal variational information bottlenecks.

Scientific research is shedding light on the interaction of the gut microbiome with the human host and on its role in human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. Most of them leverage shotgun metagenomic sequencing to extract gut microbial species-relative abundances or strain-level markers. Each of these gut microbial profiling modalities showed diagnostic potential when tested separately; however, no existing approach combines them in a single predictive framework. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model capable of learning a joint representation of multiple heterogeneous data modalities. MVIB achieves competitive classification performance while being faster than existing methods. Additionally, MVIB offers interpretable results. Our model adopts an information theoretic interpretation of deep neural networks and computes a joint stochastic encoding of different input data modalities. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundances and strain-level markers. MVIB is evaluated on human gut metagenomic samples from 11 publicly available disease cohorts covering 6 different diseases. We achieve high performance (0.80 < ROC AUC < 0.95) on 5 cohorts and at least medium performance on the remaining ones. We adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to the model's predictions. We also perform cross-study generalisation experiments, where we train and test MVIB on different cohorts of the same disease, and overall we achieve comparable results to the baseline approach, i.e. the Random Forest. Further, we evaluate our model by adding metabolomic data derived from mass spectrometry as a third input modality. Our method is scalable with respect to input data modalities and has an average training time of < 1.4 seconds. The source code and the datasets used in this work are publicly available.

Transfer learning for genotype-phenotype prediction using deep learning models.

BACKGROUND: For some understudied populations, genotype data is minimal for genotype-phenotype prediction. However, we can use the data of some other large populations to learn about the disease-causing SNPs and use that knowledge for the genotype-phenotype prediction of small populations. This manuscript illustrated that transfer learning is applicable for genotype data and genotype-phenotype prediction. RESULTS: Using HAPGEN2 and PhenotypeSimulator, we generated eight phenotypes for 500 cases/500 controls (CEU, large population) and 100 cases/100 controls (YRI, small populations). We considered 5 (4 phenotypes) and 10 (4 phenotypes) different risk SNPs for each phenotype to evaluate the proposed method. The improved accuracy with transfer learning for eight different phenotypes was between 2 and 14.2 percent. The two-tailed p-value between the classification accuracies for all phenotypes without transfer learning and with transfer learning was 0.0306 for five risk SNPs phenotypes and 0.0478 for ten risk SNPs phenotypes. CONCLUSION: The proposed pipeline is used to transfer knowledge for the case/control classification of the small population. In addition, we argue that this method can also be used in the realm of endangered species and personalized medicine. If the large population data is extensive compared to small population data, expect transfer learning results to improve significantly. We show that Transfer learning is capable to create powerful models for genotype-phenotype predictions in large, well-studied populations and fine-tune these models to populations were data is sparse.

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.

BACKGROUND: Genotype-phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. RESULTS: The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. CONCLUSION: Genotype-phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

HLA class I allele lineages and haplotype frequencies in Arabs of the United Arab Emirates.

The high degree of polymorphism of the HLA system provides suitable genetic markers to study the diversity and migration of different world populations and is beneficial for forensic identification, anthropology, transplantation and disease associations. Although the United Arab Emirates (UAE) population of about nine million people is heterogeneous, information is limited for the HLA class I allele and haplotype frequencies of the Bedouin ethnic group. We performed low-resolution PCR-SSP genotyping of three HLA class I loci at HLA-A, -B and -C for 95 unrelated healthy Bedouins from the cities of Al Ain and Abu Dhabi in the UAE. A total of 54 HLA allele lineages were detected; the most frequent low-resolution allele lineages at each HLA locus were A*02 (0.268), B*51 (0.163) and C*07 (0.216). The inferred estimates for the two most frequent HLA-A and HLA-B haplotypes were HLA-A*02 ~ HLA-B*50 (0.070) and HLA-A*02 ~ HLA-B*51 (0.051), and the most frequent 3-locus haplotype was HLA-A*02 ~ HLA-B*50 ~ HLA-C*06 (0.068). The HLA allele lineage frequencies of the UAE Arabs were compared to those previously reported for 70 other world populations, and a strong genetic similarity was detected between the UAE Arabs and the Saudi Arabians from the west with evidence of a limited gene flow between the UAE Arabs and Pakistani across the Gulf from the east, and the UAE Arabs and Omani from the south of the Gulf Peninsula.

Evolution of a Vegetarian Vibrio: Metabolic Specialization of Vibrio breoganii to Macroalgal Substrates.

While most Vibrionaceae are considered generalists that thrive on diverse substrates, including animal-derived material, we show that Vibrio breoganii has specialized for the consumption of marine macroalga-derived substrates. Genomic and physiological comparisons of V. breoganii with other Vibrionaceae isolates revealed the ability to degrade alginate, laminarin, and additional glycans present in algal cell walls. Moreover, the widely conserved ability to hydrolyze animal-derived polymers, including chitin and glycogen, was lost, along with the ability to efficiently grow on a variety of amino acids. Ecological data showing associations with particulate algal material but not zooplankton further support this shift in niche preference, and the loss of motility appears to reflect a sessile macroalga-associated lifestyle. Together, these findings indicate that algal polysaccharides have become a major source of carbon and energy in V. breoganii, and these ecophysiological adaptations may facilitate transient commensal associations with marine invertebrates that feed on algae.IMPORTANCE Vibrios are often considered animal specialists or generalists. Here, we show that Vibrio breoganii has undergone massive genomic changes to become specialized on algal carbohydrates. Accompanying genomic changes include massive gene import and loss. These vibrios may help us better understand how algal biomass is degraded in the environment and may serve as a blueprint on how to optimize the conversion of algae to biofuels.

Taxonomy-aware feature engineering for microbiome classification.

BACKGROUND: What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e.g. Machine Learning approaches. For the latter, the feature space is typically shaped by the relative abundances of operational taxonomic units, as determined by cost-effective phylogenetic marker gene profiles. While a substantial body of microbiome/microbiota research involves unsupervised and supervised Machine Learning, very little attention has been put on feature selection and engineering. RESULTS: We here propose the first algorithm to exploit phylogenetic hierarchy (i.e. an all-encompassing taxonomy) in feature engineering for microbiota classification. The rationale is to exploit the often mono- or oligophyletic distribution of relevant (but hidden) traits by virtue of taxonomic abstraction. The algorithm is embedded in a comprehensive microbiota classification pipeline, which we applied to a diverse range of datasets, distinguishing healthy from diseased microbiota samples. CONCLUSION: We demonstrate substantial improvements over the state-of-the-art microbiota classification tools in terms of classification accuracy, regardless of the actual Machine Learning technique while using drastically reduced feature spaces. Moreover, generalized features bear great explanatory value: they provide a concise description of conditions and thus help to provide pathophysiological insights. Indeed, the automatically and reproducibly derived features are consistent with previously published domain expert analyses.

Visibiome: an efficient microbiome search engine based on a scalable, distributed architecture.

BACKGROUND: Given the current influx of 16S rRNA profiles of microbiota samples, it is conceivable that large amounts of them eventually are available for search, comparison and contextualization with respect to novel samples. This process facilitates the identification of similar compositional features in microbiota elsewhere and therefore can help to understand driving factors for microbial community assembly. RESULTS: We present Visibiome, a microbiome search engine that can perform exhaustive, phylogeny based similarity search and contextualization of user-provided samples against a comprehensive dataset of 16S rRNA profiles environments, while tackling several computational challenges. In order to scale to high demands, we developed a distributed system that combines web framework technology, task queueing and scheduling, cloud computing and a dedicated database server. To further ensure speed and efficiency, we have deployed Nearest Neighbor search algorithms, capable of sublinear searches in high-dimensional metric spaces in combination with an optimized Earth Mover Distance based implementation of weighted UniFrac. The search also incorporates pairwise (adaptive) rarefaction and optionally, 16S rRNA copy number correction. The result of a query microbiome sample is the contextualization against a comprehensive database of microbiome samples from a diverse range of environments, visualized through a rich set of interactive figures and diagrams, including barchart-based compositional comparisons and ranking of the closest matches in the database. CONCLUSIONS: Visibiome is a convenient, scalable and efficient framework to search microbiomes against a comprehensive database of environmental samples. The search engine leverages a popular but computationally expensive, phylogeny based distance metric, while providing numerous advantages over the current state of the art tool.

Mycobacteria in Municipal Wastewater Treatment and Reuse: Microbial Diversity for Screening the Occurrence of Clinically and Environmentally Relevant Species in Arid Regions.

With accumulating evidence of pulmonary infection via aerosolized nontuberculous mycobacteria (NTM), it is important to characterize their persistence in wastewater treatment, especially in arid regions where treated municipal wastewater is extensively reused. To achieve this goal, microbial diversity of the genus Mycobacterium was screened for clinically and environmentally relevant species using pyrosequencing. Analysis of the postdisinfected treated wastewater showed the presence of clinically relevant slow growers like M. kansasii, M. szulgai, M. gordonae, and M. asiaticum; however, in these samples, rapid growers like M. mageritense occurred at much higher relative abundance. M. asiaticum and M. mageritense have been isolated in pulmonary samples from NTM-infected patients in the region. Diversity analysis along the treatment train found environmentally relevant organisms like M. poriferae and M. insubricum to increase in relative abundance across the chlorine disinfection step. A comparison to qPCR results across the chlorine disinfection step saw no significant change in slow grower counts at CT disinfection values ≤90 mg·min/L; only an increase to 180 mg·min/L in late May brought slow growers to below detection levels. The study confirms the occurrence of clinically and environmentally relevant mycobacteria in treated municipal wastewater, suggesting the need for vigilant monitoring of treated wastewater quality and disinfection effectiveness prior to reuse.

Comprehensive Meta-analysis of Ontology Annotated 16S rRNA Profiles Identifies Beta Diversity Clusters of Environmental Bacterial Communities.

Comprehensive mapping of environmental microbiomes in terms of their compositional features remains a great challenge in understanding the microbial biosphere of the Earth. It bears promise to identify the driving forces behind the observed community patterns and whether community assembly happens deterministically. Advances in Next Generation Sequencing allow large community profiling studies, exceeding sequencing data output of conventional methods in scale by orders of magnitude. However, appropriate collection systems are still in a nascent state. We here present a database of 20,427 diverse environmental 16S rRNA profiles from 2,426 independent studies, which forms the foundation of our meta-analysis. We conducted a sample size adaptive all-against-all beta diversity comparison while also respecting phylogenetic relationships of Operational Taxonomic Units(OTUs). After conventional hierarchical clustering we systematically test for enrichment of Environmental Ontology terms and their abstractions in all possible clusters. This post-hoc algorithm provides a novel formalism that quantifies to what extend compositional and semantic similarity of microbial community samples coincide. We automatically visualize significantly enriched subclusters on a comprehensive dendrogram of microbial communities. As a result we obtain the hitherto most differentiated and comprehensive view on global patterns of microbial community diversity. We observe strong clusterability of microbial communities in ecosystems such as human/mammal-associated, geothermal, fresh water, plant-associated, soils and rhizosphere microbiomes, whereas hypersaline and anthropogenic samples are less homogeneous. Moreover, saline samples appear less cohesive in terms of compositional properties than previously reported.

Molecular analysis for screening human bacterial pathogens in municipal wastewater treatment and reuse.

Effective and sensitive monitoring of human pathogenic bacteria in municipal wastewater treatment is important not only for managing public health risk related to treated wastewater reuse, but also for ensuring proper functioning of the treatment plant. In this study, three different 16S rRNA gene molecular analysis methodologies were employed to screen bacterial pathogens in samples collected at three different stages of an activated sludge plant. Overall bacterial diversity was analyzed using next generation sequencing (NGS) on the Illumina MiSeq platform, as well as PCR-DGGE followed by band sequencing. In addition, a microdiversity analysis was conducted using PCR-DGGE, targeting Escherichia coli. Bioinformatics analysis was performed using QIIME protocol by clustering sequences against the Human Pathogenic Bacteria Database. NGS data were also clustered against the Greengenes database for a genera-level diversity analysis. NGS proved to be the most effective approach screening the sequences of 21 potential human bacterial pathogens, while the E. coli microdiversity analysis yielded one (O157:H7 str. EDL933) out of the two E. coli strains picked up by NGS. Overall diversity using PCR-DGGE did not yield any pathogenic sequence matches even though a number of sequences matched the NGS results. Overall, sequences of Gram-negative pathogens decreased in relative abundance along the treatment train while those of Gram-positive pathogens increased.

Analysis of Cancer-associated Mutations of POLB using Machine Learning and Bioinformatics.

DNA damage is a critical factor in the onset and progression of cancer. When DNA is damaged, the number of genetic mutations increases, making it necessary to activate DNA repair mechanisms. A crucial factor in the base excision repair process, which helps maintain the stability of the genome, is an enzyme called DNA polymerase [Formula: see text] (Pol[Formula: see text]) encoded by the POLB gene. It plays a vital role in the repair of damaged DNA. Additionally, variations known as Single Nucleotide Polymorphisms (SNPs) in the POLB gene can potentially affect the ability to repair DNA. This study uses bioinformatics tools that extract important features from SNPs to construct a feature matrix, which is then used in combination with machine learning algorithms to predict the likelihood of developing cancer associated with a specific mutation. Eight different machine learning algorithms were used to investigate the relationship between POLB gene variations and their potential role in cancer onset. This study not only highlights the complex link between POLB gene SNPs and cancer, but also underscores the effectiveness of machine learning approaches in genomic studies, paving the way for advanced predictive models in genetic and cancer research.

Assessment of genetic and clinical factors in T2D susceptibility among patients with hypertension.

AIMS: Hypertension (HTN) and Type 2 Diabetes (T2D) often coexist, therefore understanding the relationship between both diseases is imperative to guide targeted prevention/therapy. This study aims to explore the relationship between HTN and T2D using genome-wide association study (GWAS) analysis and biochemical data to understand the implication of both clinical and genetic factors in these pathologies. METHODS: A total of 2,876 patients were enrolled. Using GWAS and biochemical data, patients with both T2D and HTN were compared to patients with only HTN. Specificity was confirmed by testing the detected genetic variants for associations with HTN development in T2D patients, or with HTN in healthy subjects. Regression models were applied to examine the association of T2D in patients with HTN with cardiovascular risk factors. Replication was performed using UK Biobank dataset with 31,170 subjects. RESULTS: Data showed that females with HTN are at higher risk of developing T2D due to dyslipidemia, while males faced higher risk due to high BMI (body mass index) and family history of T2D. GWAS identified Single Nucleotide Polymorphisms (SNPs) linked to T2D in patients with HTN. Notably, rs7865889, rs7756992, and rs10896290 were positively associated with T2D, whereas rs12737517 yielded negative association. Three SNPs were replicated in the UK Biobank (rs10896290, rs7865889, and rs7756992). CONCLUSION: Incorporating clinical and genetic screening into risk assessment is important for the detection and prevention of T2D in patients with HTN. The detected SNPs (rs7865889, rs12737517, and rs10896290), especially the protective SNP (rs12737517), provide an opportunity for better diagnosis, prevention, and therapy of patients with T2D and HTN.

Coronary artery disease patients with rs7904519 (TCF7L2) are at a persistent risk of type 2 diabetes.

AIMS: Type 2 diabetes (T2D) and coronary artery disease (CAD) often coexist and share genetic factors.This study aimed to investigate the common genetic factors underlying T2D and CAD in patients with CAD. METHODS: A three-step association approach was conducted: a) a discovery step involving 943 CAD patients with T2D and 1,149 CAD patients without T2D; b) an eliminating step to exclude CAD or T2D specific variants; and c) a replication step using the UK Biobank data. RESULTS: Ten genetic loci were associated with T2D in CAD patients. Three variants were specific to either CAD or T2D. Five variants lost significance after adjusting for covariates, while two SNPs remained associated with T2D in CAD patients (rs7904519*G: TCF7L2 and rs17608766*C: GOSR2). The T2D susceptibility rs7904519*G was associated with increased T2D risk, while the CAD susceptibility rs17608766*C was negatively associated with T2D in CAD patients. These associations were replicated in a UK Biobank data, confirming the results. CONCLUSIONS: No significant common T2D and CAD susceptibility genetic association was demonstrated indicating distinct disease pathways. However, CAD patients carrying the T2D susceptibility gene TCF7L2 remain at higher risk for developing T2D emphasizing the need for frequent monitoring in this subgroup.

Comparison of qPCR and metagenomic sequencing methods for quantifying antibiotic resistance genes in wastewater.

Surveillance methods of circulating antibiotic resistance genes (ARGs) are of utmost importance in order to tackle what has been described as one of the greatest threats to humanity in the 21st century. In order to be effective, these methods have to be accurate, quickly deployable, and scalable. In this study, we compare metagenomic shotgun sequencing (TruSeq DNA sequencing) of wastewater samples with a state-of-the-art PCR-based method (Resistomap HT-qPCR) on four wastewater samples that were taken from hospital, industrial, urban and rural areas. ARGs that confer resistance to 11 antibiotic classes have been identified in these wastewater samples using both methods, with the most abundant observed classes of ARGs conferring resistance to aminoglycoside, multidrug-resistance (MDR), macrolide-lincosamide-streptogramin B (MLSB), tetracycline and beta-lactams. In comparing the methods, we observed a strong correlation of relative abundance of ARGs obtained by the two tested methods for the majority of antibiotic classes. Finally, we investigated the source of discrepancies in the results obtained by the two methods. This analysis revealed that false negatives were more likely to occur in qPCR due to mutated primer target sites, whereas ARGs with incomplete or low coverage were not detected by the sequencing method due to the parameters set in the bioinformatics pipeline. Indeed, despite the good correlation between the methods, each has its advantages and disadvantages which are also discussed here. By using both methods together, a more robust ARG surveillance program can be established. Overall, the work described here can aid wastewater treatment plants that plan on implementing an ARG surveillance program.

Evaluating Drivers of the Patient Experience Triangle: Stress, Anxiety, and Frustration.

Patient experience is a widely used indicator for assessing the quality-of-care process during a patient's journey in hospital. However, the literature rarely discusses three components: patient stress, anxiety, and frustration. Furthermore, little is known about what drives each component during hospital visits. In order to explore this, we utilized data from a patient experience survey, including patient- and provider-related determinants, that was administered at a local hospital in Abu Dhabi, UAE. A machine-learning-based random forest (RF) algorithm, along with its embedded importance analysis function feature, was used to explore and rank the drivers of patient stress, anxiety, and frustration throughout two stages of the patient journey: registration and consultation. The attribute 'age' was identified as the primary patient-related determinant driving patient stress, anxiety, and frustration throughout the registration and consultation stages. In the registration stage, 'total time taken for registration' was the key driver of patient stress, whereas 'courtesy demonstrated by the registration staff in meeting your needs' was the key driver of anxiety and frustration. In the consultation step, 'waiting time to see the doctor/physician' was the key driver of both patient stress and frustration, whereas 'the doctor/physician was able to explain your symptoms using language that was easy to understand' was the main driver of anxiety. The RF algorithm provided valuable insights, showing the relative importance of factors affecting patient stress, anxiety, and frustration throughout the registration and consultation stages. Healthcare managers can utilize and allocate resources to improve the overall patient experience during hospital visits based on the importance of patient- and provider-related determinants.

Elevated Lp(a) Levels Correlate with Severe and Multiple Coronary Artery Stenotic Lesions.

Backgrounds and Aims: The role of Lipoprotein(a) (Lp(a)) in increasing the risk of cardiovascular diseases is reported in several populations. The aim of this study is to investigate the correlation of high Lp(a) levels with the degree of coronary artery stenosis. Methods: Two hundred and sixty-eight patients were enrolled for this study. Patients who underwent coronary artery angiography and who had Lp(a) measurements available were included in this study. Binomial logistic regressions were applied to investigate the association between Lp(a) and stenosis in the four major coronary arteries. The effect of LDL and HDL Cholesterol on modulating the association of Lp(a) with coronary artery disease (CAD) was also evaluated. Multinomial regression analysis was applied to assess the association of Lp(a) with the different degrees of stenosis in the four major coronary arteries. Results: Our analyses showed that Lp(a) is a risk factor for CAD and this risk is significantly apparent in patients with HDL-cholesterol ≥35 mg/dL and in non-obese patients. A large proportion of the study patients with elevated Lp(a) levels had CAD even when exhibiting high HDL serum levels. Increased HDL with low Lp(a) serum levels were the least correlated with stenosis. A significantly higher levels of Lp(a) were found in patients with >50% stenosis in at least two major coronary vessels arguing for pronounced and multiple stenotic lesions. Finally, the derived variant (rs1084651) of the LPA gene was significantly associated with CAD. Conclusion: Our study highlights the importance of Lp(a) levels as an independent biological marker of severe and multiple coronary artery stenosis.

Protein interactions in 3D: from interface evolution to drug discovery.

Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction.