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.

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.

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.

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.

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.

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.

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.

Genomic epidemiology and emergence of SARS-CoV-2 variants of concern in the United Arab Emirates

Since the declaration of SARS-CoV-2 outbreak as a pandemic, the United Arab Emirates (UAE) public health authorities have adopted strict measures to reduce transmission as early as March 2020. As a result of these measures, flight suspension, nationwide RT-PCR and surveillance of viral sequences were extensively implemented. This study aims to characterize the epidemiology, transmission pattern, and emergence of variants of concerns (VOCs) and variants of interests (VOIs) of SARS-CoV-2 in the UAE, followed by the investigation of mutations associated with hospitalized cases. A total of 1274 samples were collected and sequenced from all seven emirates between the period of 25 April 2020 to 15 February 2021. Phylogenetic analysis demonstrated multiple introductions of SARS-CoV-2 into the UAE in the early pandemic, followed by a local spread of root clades (A, B, B.1 and B.1.1). As the international flight resumed, the frequencies of VOCs surged indicating the January peak of positive cases. We observed that the hospitalized cases were significantly associated with the presence of B.1.1.7 (p < 0.001), B.1.351 (p < 0.001) and A.23.1 (p = 0.009). Deceased cases are more likely to occur in the presence of B.1.351 (p < 0.001) and A.23.1 (p = 0.022). Logistic and ridge regression showed that 51 mutations are significantly associated with hospitalized cases with the highest proportion originated from S and ORF1a genes (31% and 29% respectively). Our study provides an epidemiological insight of the emergence of VOCs and VOIs following the borders reopening and worldwide travels. It provides reassurance that hospitalization is markedly more associated with the presence of VOCs. This study can contribute to understand the global transmission of SARS-CoV-2 variants.

Can We Convert Genotype Sequences Into Images for Cases/Controls Classification?

Converting genotype sequences into images offers advantages, such as genotype data visualization, classification, and comparison of genotype sequences. This study converted genotype sequences into images, applied two-dimensional convolutional neural networks for case/control classification, and compared the results with the one-dimensional convolutional neural network. Surprisingly, the average accuracy of multiple runs of 2DCNN was 0.86, and that of 1DCNN was 0.89, yielding a difference of 0.03, which suggests that even the 2DCNN algorithm works on genotype sequences. Moreover, the results generated by the 2DCNN exhibited less variation than those generated by the 1DCNN, thereby offering greater stability. The purpose of this study is to draw the research community’s attention to explore encoding schemes for genotype data and machine learning algorithms that can be used on genotype data by changing the representation of the genotype data for case/control classification.

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.

The gut microbiome can be an indicator of various diseases due to its interaction with the human system. Our main objective is to improve on the current state of the art in microbiome classification for diagnostic purposes. A rich body of literature evidences the clinical value of microbiome predictive models. Here, we propose the Multimodal Variational Information Bottleneck (MVIB), a novel deep learning model for microbiome-based disease prediction. MVIB learns a joint stochastic encoding of different input data modalities to predict the output class. We use MVIB to predict whether human hosts are affected by a certain disease by jointly analysing gut microbial species-relative abundance and strain-level marker profiles. Both of these gut microbial features showed diagnostic potential when tested separately in previous studies; however, no research has combined them in a single predictive tool. We evaluate MVIB on various human gut metagenomic samples from 11 publicly available disease cohorts. MVIB achieves competitive performance compared to state-of-the-art methods. Additionally, we evaluate our model by adding metabolomic data as a third input modality and we show that MVIB is scalable with respect to input feature modalities. Further, we adopt a saliency technique to interpret the output of MVIB and identify the most relevant microbial species and strain-level markers to our model predictions.

Gut Microbiota Interplay With COVID-19 Reveals Links to Host Lipid Metabolism Among Middle Eastern Populations

The interplay between the compositional changes in the gastrointestinal microbiome, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility and severity, and host functions is complex and yet to be fully understood. This study performed 16S rRNA gene-based microbial profiling of 143 subjects. We observed structural and compositional alterations in the gut microbiota of the SARS-CoV-2-infected group in comparison to non-infected controls. The gut microbiota composition of the SARS-CoV-2-infected individuals showed an increase in anti-inflammatory bacteria such as Faecalibacterium (p-value = 1.72 × 10^-6) and Bacteroides (p-value = 5.67 × 10^-8). We also revealed a higher relative abundance of the highly beneficial butyrate producers such as Anaerostipes (p-value = 1.75 × 10^-230), Lachnospiraceae (p-value = 7.14 × 10^-65), and Blautia (p-value = 9.22 × 10^-18) in the SARS-CoV-2-infected group in comparison to the control group. Moreover, phylogenetic investigation of communities by reconstructing unobserved state (PICRUSt) functional prediction analysis of the 16S rRNA gene abundance data showed substantial differences in the enrichment of metabolic pathways such as lipid, amino acid, carbohydrate, and xenobiotic metabolism, in comparison between both groups. We discovered an enrichment of linoleic acid, ether lipid, glycerolipid, and glycerophospholipid metabolism in the SARS-CoV-2-infected group, suggesting a link to SARS-CoV-2 entry and replication in host cells. We estimate the major contributing genera to the four pathways to be Parabacteroides, Streptococcus, Dorea, and Blautia, respectively. The identified differences provide a new insight to enrich our understanding of SARS-CoV-2-related changes in gut microbiota, their metabolic capabilities, and potential screening biomarkers linked to COVID-19 disease severity.

Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Peanut-induced anaphylaxis in children and adolescents: Data from the European Anaphylaxis Registry

BACKGROUND

Peanut allergy has a rising prevalence in high-income countries, affecting 0.5%-1.4% of children. This study aimed to better understand peanut anaphylaxis in comparison to anaphylaxis to other food triggers in European children and adolescents.

METHODS

Data was sourced from the European Anaphylaxis Registry via an online questionnaire, after in-depth review of food-induced anaphylaxis cases in a tertiary paediatric allergy centre.

RESULTS

3514 cases of food anaphylaxis were reported between July 2007 - March 2018, 56% in patients younger than 18 years. Peanut anaphylaxis was recorded in 459 children and adolescents (85% of all peanut anaphylaxis cases). Previous reactions (42% vs. 38%; p = .001), asthma comorbidity (47% vs. 35%; p < .001), relevant cofactors (29% vs. 22%; p = .004) and biphasic reactions (10% vs. 4%; p = .001) were more commonly reported in peanut anaphylaxis. Most cases were labelled as severe anaphylaxis (Ring&Messmer grade III 65% vs. 56% and grade IV 1.1% vs. 0.9%; p = .001). Self-administration of intramuscular adrenaline was low (17% vs. 15%), professional adrenaline administration was higher in non-peanut food anaphylaxis (34% vs. 26%; p = .003). Hospitalization was higher for peanut anaphylaxis (67% vs. 54%; p = .004).

CONCLUSIONS

The European Anaphylaxis Registry data confirmed peanut as one of the major causes of severe, potentially life-threatening allergic reactions in European children, with some characteristic features e.g., presence of asthma comorbidity and increased rate of biphasic reactions. Usage of intramuscular adrenaline as first-line treatment is low and needs to be improved. The Registry, designed as the largest database on anaphylaxis, allows continuous assessment of this condition.

A Population-Specific Major Allele Reference Genome From The United Arab Emirates Population

The ethnic composition of the population of a country contributes to the uniqueness of each national DNA sequencing project and, ideally, individual reference genomes are required to reduce the confounding nature of ethnic bias. This work represents a representative Whole Genome Sequencing effort of an understudied population. Specifically, high coverage consensus sequences from 120 whole genomes and 33 whole exomes were used to construct the first ever population specific major allele reference genome for the United Arab Emirates (UAE). When this was applied and compared to the archetype hg19 reference, assembly of local Emirati genomes was reduced by ∼19% (i.e., some 1 million fewer calls). In compiling the United Arab Emirates Reference Genome (UAERG), sets of annotated 23,038,090 short (novel: 1,790,171) and 137,713 structural (novel: 8,462) variants; their allele frequencies (AFs) and distribution across the genome were identified. Population-specific genetic characteristics including loss-of-function variants, admixture, and ancestral haplogroup distribution were identified and reported here. We also detect a strong correlation between F ST and admixture components in the UAE. This baseline study was conceived to establish a high-quality reference genome and a genetic variations resource to enable the development of regional population specific initiatives and thus inform the application of population studies and precision medicine in the UAE.

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.

Whole Genome Sequencing of Four Representatives From the Admixed Population of the United Arab Emirates

Whole genome sequences (WGS) of four nationals of the United Arab Emirates (UAE) at an average coverage of 33X have been completed and described. The selection of suitable subpopulation representatives was informed by a preceding comprehensive population structure analysis. Representatives were chosen based on their central location within the subpopulation on a principal component analysis (PCA) and the degree to which they were admixed. Novel genomic variations among the different subgroups of the UAE population are reported here. Specifically, the WGS analysis identified 4,161,067-4,798,806 variants in the four individual samples, where approximately 80% were single nucleotide polymorphisms (SNPs) and 20% were insertions or deletions (indels). An average of 2.75% was found to be novel variants according to dbSNP (build 151). This is the first report of structural variants (SV) from WGS data from UAE nationals. There were 15,677-20,339 called SVs, of which around 13.5% were novel. The four samples shared 1,399,178 variants, each with distinct variants as follows: 1,085,524 (for the individual denoted as UAE S011), 1,228,559 (UAE S012), 791,072 (UAE S013), and 906,818 (UAE S014). These results show a previously unappreciated population diversity in the region. The synergy of WGS and genotype array data was demonstrated through variant annotation of the former using 2.3 million allele frequencies for the local population derived from the latter technology platform. This novel approach of combining breadth and depth of array and WGS technologies has guided the choice of population genetic representatives and provides complementary, regionalized allele frequency annotation to new genomes comprising millions of loci.

Genetic Diversity and Low Stratification of the Population of the United Arab Emirates

With high consanguinity rates on the Arabian Peninsula, it would not have been unexpected if the population of the United Arab Emirates (UAE) was shown to be relatively homogenous. However, this study of 1000 UAE nationals provided a contrasting perspective, one of a relatively heterogeneous population. Located at the apex of Europe, Asia, and Africa, the observed diversity could be explained by a plethora of migration patterns since the first Out-of-Africa movement. A strategy to explore the extent of genetic variation of the population of the UAE is presented. The first step involved a comprehensive population stratification study that was instructive for subsequent whole genome sequencing (WGS) of suitable representatives (which is described elsewhere). When these UAE data were compared to previous smaller studies from the region, the findings were consistent with a population that is a diverse and admixed group of people. However, rather than sharp and distinctive clusters, cluster analysis reveals low levels of stratification throughout the population. UAE emirates exhibit high within-Emirate-distance/among-Emirate distance ratios. Supervised admixture analysis showed a continuous gradient of ancestral populations, suggesting that admixture on the south eastern tip of the Arabian Peninsula occurred gradually. When visualized using a unique technique that combined admixture ratios and principal component analysis (PCA), unappreciated diversity was revealed while mitigating projection bias of conventional PCA. We observe low population stratification in the UAE in terms of homozygosity versus separation cluster coefficients. This holds for the UAE in a global context as well as for isolated cluster analysis of the Emirati birthplaces. However, the subtle clustering observed in the Emirates reflects geographic proximity and historic migration events. The analytical strategy used here highlights the complementary nature of data from genotype array and WGS for anthropological studies. Specifically, genotype array data were instructive to select representative subjects for WGS. Furthermore, from the 2.3 million allele frequencies obtained from genotype arrays, we identified 46,481 loci with allele frequencies that were significantly different with respect to other world populations. This comparison of allele frequencies facilitates variant prioritization in common diseases. In addition, these loci bear great potential as biomarkers in anthropological and forensic studies.

Introducing the first whole genomes of nationals from the United Arab Emirates

Whole Genome Sequencing (WGS) provides an in depth description of genome variation. In the era of large-scale population genome projects, the assembly of ethnic-specific genomes combined with mapping human reference genomes of underrepresented populations has improved the understanding of human diversity and disease associations. In this study, for the first time, whole genome sequences of two nationals of the United Arab Emirates (UAE) at >27X coverage are reported. The two Emirati individuals were predominantly of Central/South Asian ancestry. An in-house customized pipeline using BWA, Picard followed by the GATK tools to map the raw data from whole genome sequences of both individuals was used. A total of 3,994,521 variants (3,350,574 Single Nucleotide Polymorphisms (SNPs) and 643,947 indels) were identified for the first individual, the UAE S001 sample. A similar number of variants, 4,031,580 (3,373,501 SNPs and 658,079 indels), were identified for UAE S002. Variants that are associated with diabetes, hypertension, increased cholesterol levels, and obesity were also identified in these individuals. These Whole Genome Sequences has provided a starting point for constructing a UAE reference panel which will lead to improvements in the delivery of precision medicine, quality of life for affected individuals and a reduction in healthcare costs. The information compiled will likely lead to the identification of target genes that could potentially lead to the development of novel therapeutic modalities.

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.

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.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2205-3) contains supplementary material, which is available to authorized users.

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.

Comparison of retinal thickness and fundus-related microperimetry with visual acuity in uveitic macular oedema

PURPOSE

Macular oedema is a common complication and vision-limiting factor in uveitis. The aim of this study was to compare retinal thickness as measured by optical coherence tomography and photoreceptor function as measured by fundus-related microperimetry with respect to their correlation with visual acuity.

METHODS

Prospective observational monocentre study. Thirty-one patients (53 eyes) with endogenous uveitis and fluorescein angiographically confirmed macular oedema were evaluated. Foveal thickness was analysed using spectral-domain (Spectralis(TM) ; Heidelberg Engineering, Heidelberg, Germany) OCT and retinal sensitivity was assessed using fundus-related microperimetry (MP1; Nidek Technologies, Padova, Italy). All findings were correlated with best-corrected visual acuity (BCVA).

RESULTS

Foveal thickness was correlated with BCVA [p = 0.005, r = 0.38, 95% confidence interval (CI) 0.12-0.59]. For microperimetry measurements, a negative correlation with logMAR visual acuity was found. Fixation abnormalities were not associated with poor visual acuity, increased foveal thickness or retinal sensitivity. In eyes with cystoid changes in the outer plexiform and inner nuclear layer, foveal thickness was increased (p < 0.0001). Epiretinal membrane formation was present in 70%. In these eyes, foveal thickness was significantly increased (p = 0.003) and visual acuity was worse (p = 0.08).

CONCLUSION

Foveal thickness and fundus-related microperimetry were correlated with visual acuity. Cystoid changes in the outer plexiform and inner nuclear layer and the presence of epiretinal membrane were associated with poor visual acuity. Fixation abnormalities were not associated with poor visual acuity.

Structural modeling of histone methyltransferase complex Set1C from Saccharomyces cerevisiae using constraint-based docking

Set1C is a histone methyltransferase playing an important role in yeast gene regulation. Modeling the structure of this eight-subunit protein complex is an important open problem to further elucidate its functional mechanism. Recently, there has been progress in modeling of larger complexes using constraints to restrict the combinatorial explosion in binary docking of subunits. Here, we model the subunits of Set1C and develop a constraint-based docking approach, which uses high-quality protein interaction as well as functional data to guide and constrain the combinatorial assembly procedure. We obtained 22 final models. The core complex consisting of the subunits Set1, Bre2, Sdc1 and Swd2 is conformationally conserved in over half of the models, thus, giving high confidence. We characterize these high-confidence and the lower confidence interfaces and discuss implications for the function of Set1C.

Famine Edema and the Mechanism of Its Formation

Famine edema was produced experimentally in 34 normal men who lost a quarter of their body weight while subsisting for 6 months on a European type of semi-starvation diet. The ratio of extracellular water to cellular tissue was roughly doubled. Their clinical state closely resembled that seen in Europe in 1945. There were no signs of renal or cardiac failure. The plasma protein concentration fell only slightly and the A/G ratio remained within normal limits. The venous pressure was roughly 50 per cent below normal. Data from the field lend support to these indications that famine edema is not simply a result of hypoproteinemia or of renal or cardiac failure. It is concluded that there is a dynamic nonequilibrium state of the capillary wall and, accordingly, calculations from equilibrium equations are inadmissible.

Structural fragment clustering reveals novel structural and functional motifs in alpha-helical transmembrane proteins

Background

A large proportion of an organism's genome encodes for membrane proteins. Membrane proteins are important for many cellular processes, and several diseases can be linked to mutations in them. With the tremendous growth of sequence data, there is an increasing need to reliably identify membrane proteins from sequence, to functionally annotate them, and to correctly predict their topology.

Results

We introduce a technique called structural fragment clustering, which learns sequential motifs from 3D structural fragments. From over 500,000 fragments, we obtain 213 statistically significant, non-redundant, and novel motifs that are highly specific to α-helical transmembrane proteins. From these 213 motifs, 58 of them were assigned to function and checked in the scientific literature for a biological assessment. Seventy percent of the motifs are found in co-factor, ligand, and ion binding sites, 30% at protein interaction interfaces, and 12% bind specific lipids such as glycerol or cardiolipins. The vast majority of motifs (94%) appear across evolutionarily unrelated families, highlighting the modularity of functional design in membrane proteins. We describe three novel motifs in detail: (1) a dimer interface motif found in voltage-gated chloride channels, (2) a proton transfer motif found in heme-copper oxidases, and (3) a convergently evolved interface helix motif found in an aspartate symporter, a serine protease, and cytochrome b.

Conclusions

Our findings suggest that functional modules exist in membrane proteins, and that they occur in completely different evolutionary contexts and cover different binding sites. Structural fragment clustering allows us to link sequence motifs to function through clusters of structural fragments. The sequence motifs can be applied to identify and characterize membrane proteins in novel genomes.

MeMotif: a database of linear motifs in alpha-helical transmembrane proteins

Membrane proteins are important for many processes in the cell and used as main drug targets. The increasing number of high-resolution structures available makes for the first time a characterization of local structural and functional motifs in α-helical transmembrane proteins possible. MeMotif (http://projects.biotec.tu-dresden.de/memotif) is a database and wiki which collects more than 2000 known and novel computationally predicted linear motifs in α-helical transmembrane proteins. Motifs are fully described in terms of several structural and functional features and editable. Motifs contained in MeMotif can be used in different biological applications, from the identification of biochemically important functional residues which are candidates for mutagenesis experiments to the improvement of tools for transmembrane protein modeling.

[Optical coherence tomography for macula diagnostics. Review of methods and standardized application concentrating on diagnostic and therapy control of age-related macula degeneration]

BACKGROUND

Optical coherence tomography (OCT) has gained increasing relevance for follow-up after the treatment of macular diseases especially after anti-VEGF therapy. Therefore it seemed reasonable to develop standardized evaluation strategies and OCT examination guidelines for Stratus OCT III.

MATERIALS AND METHODS

Basic guidelines for the Stratus OCT III examination of macular diseases were developed. The first part contains basic advice for the OCT examination with respect to the examiner, patients, image quality, movement artefacts, algorithms, archiving and interpretation of OCT images. The second part consists of the relevance and indications for OCT examination especially in age-related macular degeneration (AMD), subgroups of AMD and follow-up after treatment. The third part demonstrates a brief outlook on future developments, such as the digital integration method (DIM), which provides identical scan localization in follow-up and eliminates any movement artefacts.

CONCLUSION

The application of standardized routine scanning and analysis protocols in Stratus OCT III for macular diseases and follow-up examinations provides an optimized, time-saving and comparable use of OCT. Therefore, the relevance and quality of OCT is increased for routine use in outpatient departments, hospitals and also for clinical studies.

Rich polymorphism of a rod-like liquid crystal (8CB) confined in two types of unidirectional nanopores

We present a neutron and X-rays scattering study of the phase transitions of 4-n-octyl-4' -cyanobiphenyl (8CB) confined in unidirectional nanopores of porous alumina and porous silicon (PSi) membranes with an average diameter of 30 nm. Spatial confinement reveals a rich polymorphism, with at least four different low temperature phases in addition to the smectic A phase. The structural study as a function of thermal treatments and conditions of spatial confinement allows us to get insights into the formation of these phases and their relative stability. It gives the first description of the complete phase behavior of 8CB confined in PSi and provides a direct comparison with results obtained in bulk conditions and in similar geometric conditions of confinement but with reduced quenched disorder effects using alumina anopore membranes.

Using structural motif descriptors for sequence-based binding site prediction

BACKGROUND

Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites.

RESULTS

We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern.

CONCLUSION

The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.

Preferred orientations and stability of medium length n-alkanes solidified in mesoporous silicon

The n-alkanes C(16)H(34), C(17)H(36), C(19)H(40), and C(25)H(52) have been imbibed and solidified in mesoporous, crystalline silicon with a mean pore diameter of 10 nm. The structures and phase sequences have been determined by x-ray diffractometry. Apart from a reduction and the hysteresis of the melting-freezing transition, we find a set of six discrete orientation states ("domains") of the confined alkane crystals with respect to the lattice of the silicon host. The growth process responsible for the domain selection is interpreted as a nanoscale version of the Bridgman technique known from single-crystal growth. Oxidation of the pore walls leads to extrusion of the hydrocarbons upon crystallization, whereas the solidified n-alkanes investigated in nonoxidized, porous silicon are thermodynamically stable.

The many faces of protein-protein interactions: A compendium of interface geometry

A systematic classification of protein-protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces) for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at http://www.scoppi.org.

SCOPPI: a structural classification of protein-protein interfaces

SCOPPI, the structural classification of protein–protein interfaces, is a comprehensive database that classifies and annotates domain interactions derived from all known protein structures. SCOPPI applies SCOP domain definitions and a distance criterion to determine inter-domain interfaces. Using a novel method based on multiple sequence and structural alignments of SCOP families, SCOPPI presents a comprehensive geometrical classification of domain interfaces. Various interface characteristics such as number, type and position of interacting amino acids, conservation, interface size, and permanent or transient nature of the interaction are further provided. Proteins in SCOPPI are annotated with Gene Ontology terms, and the ontology can be used to quickly browse SCOPPI. Screenshots are available for every interface and its participating domains. Here, we describe contents and features of the web-based user interface as well as the underlying methods used to generate SCOPPI's data. In addition, we present a number of examples where SCOPPI becomes a useful tool to analyze viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues and diversity of interface localizations. SCOPPI is available at .

Equivalent binding sites reveal convergently evolved interaction motifs

MOTIVATION

Much research has been devoted to the characterization of interaction interfaces found in complexes with known structure. In this context, the interactions of non-homologous domains at equivalent binding sites are of particular interest, as they can reveal convergently evolved interface motifs. Such motifs are an important source of information to formulate rules for interaction specificity and to design ligands based on the common features shared among diverse partners.

RESULTS

We develop a novel method to identify non-homologous structural domains which bind at equivalent sites when interacting with a common partner. We systematically apply this method to all pairs of interactions with known structure and derive a comprehensive database for these interactions. Of all non-homologous domains, which bind with a common interaction partner, 4.2% use the same interface of the common interaction partner (excluding immunoglobulins and proteases). This rises to 16% if immunoglobulin and proteases are included. We demonstrate two applications of our database: first, the systematic screening for viral protein interfaces, which can mimic native interfaces and thus interfere; and second, structural motifs in enzymes and its inhibitors. We highlight several cases of virus protein mimicry: viral M3 protein interferes with a chemokine dimer interface. The virus has evolved the motif SVSPLP, which mimics the native SSDTTP motif. A second example is the regulatory factor Nef in HIV which can mimic a kinase when interacting with SH3. Among others the virus has evolved the kinase's PxxP motif. Further, we elucidate motif resemblances in Baculovirus p35 and HIV capsid proteins. Finally, chymotrypsin is subject to scrutiny wrt. its structural similarity to subtilisin and wrt. its inhibitor's similar recognition sites.

SUPPLEMENTARY INFORMATION

A database is online at scoppi.biotec.tu-dresden.de/abac/.

DEQOR: a web-based tool for the design and quality control of siRNAs

RNA interference (RNAi) is a powerful tool for inhibiting the expression of a gene by mediating the degradation of the corresponding mRNA. The basis of this gene-specific inhibition is small, double-stranded RNAs (dsRNAs), also referred to as small interfering RNAs (siRNAs), that correspond in sequence to a part of the exon sequence of a silenced gene. The selection of siRNAs for a target gene is a crucial step in siRNA-mediated gene silencing. According to present knowledge, siRNAs must fulfill certain properties including sequence length, GC-content and nucleotide composition. Furthermore, the cross-silencing capability of dsRNAs for other genes must be evaluated. When designing siRNAs for chemical synthesis, most of these criteria are achievable by simple sequence analysis of target mRNAs, and the specificity can be evaluated by a single BLAST search against the transcriptome of the studied organism. A different method for raising siRNAs has, however, emerged which uses enzymatic digestion to hydrolyze long pieces of dsRNA into shorter molecules. These endoribonuclease-prepared siRNAs (esiRNAs or 'diced' RNAs) are less variable in their silencing capabilities and circumvent the laborious process of sequence selection for RNAi due to a broader range of products. Though powerful, this method might be more susceptible to cross-silencing genes other than the target itself. We have developed a web-based tool that facilitates the design and quality control of siRNAs for RNAi. The program, DEQOR, uses a scoring system based on state-of-the-art parameters for siRNA design to evaluate the inhibitory potency of siRNAs. DEQOR, therefore, can help to predict (i) regions in a gene that show high silencing capacity based on the base pair composition and (ii) siRNAs with high silencing potential for chemical synthesis. In addition, each siRNA arising from the input query is evaluated for possible cross-silencing activities by performing BLAST searches against the transcriptome or genome of a selected organism. DEQOR can therefore predict the probability that an mRNA fragment will cross-react with other genes in the cell and helps researchers to design experiments to test the specificity of esiRNAs or chemically designed siRNAs. DEQOR is freely available at http://cluster-1.mpi-cbg.de/Deqor/deqor.html.

Performance of a flat-panel detector in the detection of artificial erosive changes: comparison with conventional screen-film and storage-phosphor radiography

The purpose of this study was to compare a large-area, direct-readout, flat-panel detector system with a conventional screen-film system, a storage-phosphor system, and a mammography screen-film system with regard to the detection of artificial bone erosions simulating rheumatoid disease, and to assess its diagnostic performance with decreasing exposure dose. Six hundred forty regions were defined in 160 metacarpophalangeal and proximal interphalangeal joint specimens from 20 monkey paws (4 regions per joint). Artificial bone erosions were created in 320 of these 640 regions. Specimens were enclosed in containers filled with water to obtain absorption and scatter radiation conditions similar to those of a human hand. Imaging was performed using a flat-panel system, a speed class 200 screen-film system, a mammography screen-film system, and a storage-phosphor system under exactly matched conditions. Different exposure doses equivalent to speed classes of S=100, 200, 400, 800, 1600, and 3200 were used. In all images the presence or absence of a lesion was assessed by three radiologists using a five-level confidence scale. Receiver operating characteristic (ROC) analysis was performed for a total of 21,120 observations (1920 for each imaging modality and exposure level) and diagnostic performance estimated by the area under the ROC curve (A(z)). The significance of differences in diagnostic performance was tested with analysis of variance. The ROC analysis showed A(z) values of 0.809 (S=200), 0.768 (S=400), 0.737 (S=800), 0.710 (S=1600), and 0.685 (S=3200) for the flat-panel system, 0.770 for the speed class 200 screen-film system, 0.781 (S=200), 0.739 (S=400), 0.724 (S=800), 0.680 (S=1600) for the storage-phosphor system, and 0.798 for the mammography screen-film system. Analysis of variance showed significant differences between different combinations of imaging modalities and exposure doses ( p<0.05). The diagnostic performance of the flat-panel detector system is superior to that of a screen-film system and a storage-phosphor system for the detection of erosive lesions at clinical exposure settings (S=200). Using the flat-panel system the exposure dose can be reduced by 50% to obtain a diagnostic performance comparable to a speed class 200 screen-film system.

[Disseminated ischemic necrosis and livedo racemosa in a chronic dialysis patient with calciphylaxis]

Calciphylaxis occurred in a 40-year old female patient with end-stage renal failure. The patient developed livedo racemosa ("livedo reticularis") with painful skin necrosis and ulcers involving multiple areas of the hip and legs after 22 years of hemodialysis. X-ray-examinations revealed calcinosis of peripheral arteries, especially of the pelvis, thigh and hands, while histological examinations showed a fibrosis and calcinosis of small subcutaneous arteries. A generalized cutaneous microangiopathy could be demonstrated by transcutaneous oxygen pressure measurements. Laboratory data showed a moderate secondary hyperparathyroidism with mild elevation of calcium-phosphate product. In addition to the hemodialysis an attempt was made to improve the microcirculation by vasoactive drugs. The clinical course was characterized by slow healing of the ulcers and occurrence of new areas of cutaneous necrosis. Calciphylaxis is a rare late complication in patients with advanced, often end-stage renal failure. It has characteristic histopathological features and is frequently, but not always, associated with a disturbed calcium and phosphorus metabolism and mildly elevated levels of parathyroid hormone. Calciphylaxis is classified as a special type of metastatic calcinosis.

Porous-coated knee arthroplasty. A case report concerning bone ingrowth

The area of bone-metal interaction in uncemented implants is regarded by many investigators as the key to the success or failure of the implant. The nature of the interaction is poorly understood because the zone is technically difficult to visualize and analyze. In order to test the power of modern imaging, analyzing, and metallurgical methods in this context, a well-functioning porous-coated knee prosthesis (tibial component) removed from the knee of a 65-year-old man was sectioned and examined by light microscopy, roentgenogram, scanning electron microscopy, and radiologic energy dispersive analysis. Independently, these methods demonstrated that the prosthesis was held in situ by collagenous tissue between and below the metal pellets of the prosthesis. Calcified bone did not appear to interact with the prosthesis and is probably not a major factor for prosthesis fixation. The various analytic methods described are suitable and sufficient for further exploration on a larger scale of the zone of bone-metal contact in cementless implants.

Cytoplasmic inclusions in lymphocytes of chronic lymphocytic leukaemia. A report of 10 cases

Peripheral blood from 90 CLL patients was examined by light-and electron-microscopy for the occurrence of crystalline inclusions in lymphocytes. Inclusions were demonstrated in 10 patients (11%). In these patients the inclusions were present in 5-45% of peripheral blood lymphocytes. In the light microscope the inclusions appeared as rectangular, unstained structures in May-Grünewald Giemsa and PAS stains. In the electron microscope the inclusions appeared as intracytoplasmic, completely partially membrane-bound bodies, which were often associated with dilated profiles of rough endoplasmic reticulum. The ultrastructure of the inclusions was granular. In immunofluorescence staining the inclusions were found to contain immunoglobulin of the same type and class as the surface membrane-bound immunoglobulin of the neoplastic lymphocytes, most frequently IgM-lambda. The lymphocytes of one case with kappa light chains at the cell surface membrane contained inclusions of the same ultrastructural morphology as those of the other cases with lambda light chains. The presence of inclusions was not associated with any specific clinical or prognostic features. the inclusions persisted during antileukaemic therapy. Their formation may be related to a dysfunction in the synthesis of surface membrane-bound immunoglobulins.