Biology subjects

Abdalla, A.

Publications and source records attributed to Abdalla, A..

4 recordsLinked to original sources

bioRxiv2026

Evaluation of MeaSeq: comprehensive analysis and reporting of measles virus whole genome sequences.

Although vaccine-preventable, measles virus (MeV) continues to pose a significant public health challenge, with a substantial resurgence of cases worldwide. As whole-genome sequencing (WGS) becomes increasingly affordable and routinely adopted in public health laboratories, reliable and accessible analysis of next-generation sequencing (NGS) data is critical for outbreak investigation and molecular surveillance. Here, we present MeaSeq, a fast, user-friendly, open-source bioinformatics pipeline for MeV analysis using Illumina or Oxford Nanopore Technologies (ONT) NGS data. MeaSeq performs quality control assessments, consensus genome assembly and variant detection, optional genotype-specific reference selection, Distinct Sequence Identifier (DSId) assignment via user-provided databases or hashing, sub-consensus variant visualization, genome quality assessment, and standardized HTML reporting. We compared the performance of MeaSeq on NGS data generated from multiple sequencing platforms and targeted enrichment strategies against gold-standard Sanger data, reference genomes, and publicly available comparative data. This validation demonstrates that MeaSeq provides an accurate, reproducible, and accessible solution for routine MeV WGS analysis, supporting genomic surveillance and outbreak response workflows in public health and research settings. Impact StatementThe recent surge in measles cases worldwide, causing several countries to lose their measles elimination status, underscores the urgent need for effective and accessible genomic surveillance. Our manuscript introduces MeaSeq, a comprehensive and open-source bioinformatics pipeline specifically designed for analyzing MeV NGS data. MeaSeq includes MeV specific analyses such as genotype prediction from sequencing reads with optional genotype-specific reference selection; DSId assignment; quality control checks such as genome rule-of-six divisibility and gene CDS validation; subconsensus nucleotide analysis with mixed-site highlighting; and genomic plotting. By leveraging NGS technology, our pipeline can facilitate the identification of transmission chains and may provide critical insights into the dynamics of MeV outbreaks. This information is essential for public health officials and researchers to implement targeted interventions and optimize vaccine strategies. Additionally, the open-source nature of MeaSeq fosters collaboration and innovation within the scientific measles community along with providing access to a wider range of researchers. Data SummaryThe MeaSeq pipeline code is available on GitHub (https://github.com/phac-nml/measeq). Comparative datasets of publicly available WGS data were accessed through the NCBI Sequence Read Archive under the following BioProjects: PRJNA869081 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA869081) PRJNA480551 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA480551) PRJNA1017431 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1017431) PRJNA1241325 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241325) PRJNA1174053 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1174053) PRJNA1293457 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1293457) PRJNA843031 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA843031) Whole-genome sequences were included in the validation analysis if they consisted of paired-end data (Illumina) and achieved [≥]95% genome completeness following trimming of the 5' and 3' untranslated regions (UTRs). This criterion ensured sufficient genome coverage for robust validation while allowing for limited missing data arising from regions of low sequencing depth or amplicon dropout. A complete list of sequences included in the validation, along with their accession numbers, is provided in Supplementary Table 1.

bioinformatics↗

bioRxiv2024

Heterologous Surface Display Reveals Conserved Complement Inhibition and Functional Diversification of Borrelia burgdorferi Elp Proteins

Lyme disease is a tick-borne spirochetosis with diverse clinical manifestations. Phenotypic variation among Borrelia burgdorferi strains correlates with variable manifestations of Lyme disease in humans; this diversity is attributed in part to variation in surface-exposed lipoproteins, which are targets of the human antibody response and contribute to tissue adhesion, immune evasion, and other host interactions. Many B. burgdorferi lipoproteins are encoded as multi-copy gene families including the OspE/F- like leader peptide (Elp) protein family. To characterize Elp allelic variants, we adapted the Pseudomonas syringae ice nucleation protein (INP) system to present B. burgdorferi lipoproteins on the surface of Escherichia coli. We identified interactions with classical complement proteins and mapped binding regions in the E. coli system. We validated interactions using recombinant proteins and B. burgdorferi surface display. By assessing potential interactions with extracellular matrix components, we identified a novel interaction between Elp proteins and perlecan, a component of mammalian basement membranes, and revealed the bifunctional nature of Elps. Our findings reveal that Elps have undergone functional diversification while maintaining classical complement inhibition mediated by potent and conserved C1s binding and demonstrate that E. coli surface display offers an efficient, cost-effective, and relatively high throughput approach to characterize B. burgdorferi lipoproteins.

microbiology↗

bioRxiv2021

Infection with endosymbiotic Spiroplasma disrupts tsetse (Glossina fuscipes fuscipes) metabolic and reproductive homeostasis

Tsetse flies (Glossina spp.) house a population-dependent assortment of microorganisms that can include pathogenic African trypanosomes and maternally transmitted endosymbiotic bacteria, the latter of which mediate numerous aspects of their hosts metabolic, reproductive, and immune physiologies. One of these endosymbionts, Spiroplasma, was recently discovered to reside within multiple tissues of field captured and laboratory colonized tsetse flies grouped in the Palpalis subgenera. In various arthropods, Spiroplasma induces reproductive abnormalities and pathogen protective phenotypes. In tsetse, Spiroplasma infections also induce a protective phenotype by enhancing the flys resistance to infection with trypanosomes. However, the potential impact of Spiroplasma on tsetses viviparous reproductive physiology remains unknown. Herein we employed high-throughput RNA sequencing and laboratory-based functional assays to better characterize the association between Spiroplasma and the metabolic and reproductive physiologies of G. fuscipes fuscipes (Gff), a prominent vector of human disease. Using field-captured Gff, we discovered that Spiroplasma infection induces changes of sex-biased gene expression in reproductive tissues that may be critical for tsetses reproductive fitness. Using a Gff line composed of individuals heterogeneously infected with Spiroplasma, we observed that the bacterium and tsetse host compete for finite nutrients, which negatively impact female fecundity by increasing the length of intrauterine larval development. Additionally, we found that when males are infected with Spiroplasma, the motility of their sperm is compromised following transfer to the female spermatheca. As such, Spiroplasma infections appear to adversely impact male reproductive fitness by decreasing the competitiveness of their sperm. Finally, we determined that the bacterium is maternally transmitted to intrauterine larva at a high frequency, while paternal transmission was also noted in a small number of matings. Taken together, our findings indicate that Spiroplasma exerts a negative impact on tsetse fecundity, an outcome that could be exploited for reducing tsetse population size and thus disease transmission. Author SummaryEndosymbiotic bacteria regulate numerous aspects of their hosts reproductive physiology. Natural populations of the tsetse fly, Glossina fuscipes fuscipes (Gff), house heterogeneous infections with the bacterium Spiroplasma glossinidia. Infection with the bacterium results in the presentation of several phenotypes in both male and female Gff that would put them at a significant reproductive disadvantage when compared to their counterparts that do not house the bacterium. These Spiroplasma induced phenotypes include changes in sex-biased gene expression in the reproductive organs, a depletion in the availability of metabolically critical lipids in pregnant females that results in delayed larval development, and compromised sperm fitness. These findings indicate that Spiroplasma exerts an overall negative impact on both male and female reproductive fitness and thus likely has a profound effect on fly population structure. This outcome, in conjunction with the fact that Spiroplasma infected tsetse are unusually refractory to infection with pathogenic African trypanosomes, indicates that the bacterium could be experimentally exploited to reduce disease transmission through the fly.

microbiology↗

bioRxiv2019

Augmenting Protein Network Embeddings with Sequence Information

Computational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these hybrid representations, we show that simple machine learning models trained using these hybrid representations outperform existing network-based methods on the task of tissue-specific protein function prediction on 13 out of 13 tissues. Furthermore, these representations outperform existing ones by 14% on average.

bioinformatics↗