Biology subjects

Brandenburg, J.-T.

Publications and source records attributed to Brandenburg, J.-T..

4 recordsLinked to original sources

bioRxiv2023

Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort

BackgroundHypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. MethodsThe study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data was generated using data-independent acquisition (DIA) and processed using Spectronaut 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. ResultsOverall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate]=1.4x10-45), innate immune system (q=1.1x10-32), extracellular matrix (ECM) organisation (q=0.03) and activation of matrix metalloproteinases (q=0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and CKD included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. ConclusionsThe urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension and albuminuria.

biochemistry↗

bioRxiv2022

H3AGWAS : A portable workflow for Genome Wide Association Studies

BackgroundGenome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. ResultsThe H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and postassociation analysis of results. ConclusionsThe workflow is scalable -- laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker on Singularity.

bioinformatics↗

bioRxiv2022

FPGA Acceleration of GWAS Permutation Testing

MotivationGenome-wide association studies (GWASs) analyse genetic variation over the genomes of many individuals in an attempt to identify single nucleotide polymorphisms (SNPs) associated with complex phenotypes. To capture a large amount of genetic variation and increase the chance of detecting associated SNPs, modern GWASs include millions of SNPs sampled from thousands of individuals. The cumulative probability of false associations increases with the number of SNPs included in the analysis. A GWAS, therefore, needs to control the number of false associations. Permutation testing is a straightforward and accurate method of controlling the false positive rate, but it is very computationally expensive (and slow) so there is a need for a permutation testing accelerator that can process modern GWAS datasets in reasonable time. ResultsFPGAs (Field-Programmable Gate Arrays) are reconfigurable integrated circuits which provide a high level of parallelisation that can be harnessed to accelerate GWAS permutation testing. This work presents an accessible FPGA-based tool (designed to run on a cloud-based AWS EC2 FPGA instance) that accelerates GWAS permutation testing for continuous phenotypes. The tool implements two known GWAS permutation testing algorithms: maxT permutation testing and adaptive permutation testing. The speed of the FPGA-based tool was compared to the speed of PLINK (a popular CPU-based tool) running on 40 Intel Xeon 4114 CPU cores using an imputed breast cancer dataset of 13.7 million SNPs sampled from 3652 individuals. For 1000 maxT permutations, the FPGA-based algorithms run time was 22 minutes while PLINKs run time was almost 7 days; for 100 million adaptive permutations, the FPGA-based algorithms run time was 325 minutes and PLINKs run time was about 8.5 days. For 700 million adaptive permutations of the same dataset (an almost unfeasible workload for PLINK running on a 40-core CPU) the run time of the FPGA-accelerated algorithm was 33 hours. AvailabilityAn EC2 AMI (FPGA_perm) in the us-east-1 region is available. Instructions, source code and sample data are available at https://github.com/witseie/fpgaperm.

bioinformatics↗

bioRxiv2020

In silico study of the spike protein from SARS-CoV-2 interaction with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

The spread of COVID-19 caused by the SARS-CoV-2 outbreak has been growing since its first identification in December 2019. The publishing of the first SARS-CoV-2 genome made a valuable source of data to study the details about its phylogeny, evolution, and interaction with the host. Protein-protein binding assays have confirmed that Angiotensin-converting enzyme 2 (ACE2) is more likely to be the cell receptor through which the virus invades the host cell. In the present work, we provide an insight into the interaction of the viral spike Receptor Binding Domain (RBD) from different coronavirus isolates with host ACE2 protein. By calculating the binding energy score between RBD and ACE2, we highlighted the putative jump in the affinity from a progenitor form of SARS-CoV-2 to the current virus responsible for COVID-19 outbreak. Our result was consistent with previously reported phylogenetic analysis and corroborates the opinion that the interface segment of the spike protein RBD might be acquired by SARS-CoV-2 via a complex evolutionary process rather than a progressive accumulation of mutations. We also highlighted the relevance of Q493 and P499 amino acid residues of SARS-CoV-2 RBD for binding to human ACE2 and maintaining the stability of the interface. Moreover, we show from the structural analysis that it is unlikely for the interface residues to be the result of genetic engineering. Finally, we studied the impact of eight different variants located at the interaction surface of ACE2, on the complex formation with SARS-CoV-2 RBD. We found that none of them is likely to disrupt the interaction with the viral RBD of SARS-CoV-2.

bioinformatics↗