Biology subjects

Aerts, J.

Publications and source records attributed to Aerts, J..

8 recordsLinked to original sources

bioRxiv2025

Comparative pangenome and mobilome analysis of clostridial species clusters reveals different levels of host adaptation and novel conserved biosynthetic potential

To thrive in diverse ecological niches, bacteria adopt various lifestyles, that range from living freely in the soil to forming close associations with human and animal hosts. However, the impact of these adaptation processes on their genomes and metabolisms remains largely unexplored beyond the genus level. Investigating these evolutionary dynamics at higher taxonomic levels can enhance our understanding of the relationship between host adaptation and their functional capabilities. Here, we examine the evolutionary trajectories and metabolic capabilities of the Clostridia class, which displays a variety of lifestyles and is of high importance for industry, medicine and microbiome research. First, we uncover that the clostridial orders have significantly different adaptation rates. Second, we show that the Oscillospirales order has undergone extensive genomic and functional specialisation toward a host-associated lifestyle, while the Lachnospirales order tends to be at a lower level of host association, retaining a remarkably high number of free-living trait genes and a high degree of metabolic versatility. Third, we reveal substantial differences in genomic architecture and metabolic versatility between the clostridial orders and link these to the progressing stages of host adaptation. Additionally, we identify widely conserved biosynthetic gene clusters, highlighting untapped biosynthetic potential of evolutionary significance. Hence, the beyond-genus level analyses in this study provide valuable new insights into bacterial adaptation with broad implications for evolutionary biology, microbiome research and biotechnology.

bioinformatics↗

bioRxiv2024

Multi-material volumetric bioprinting and plug-and-play suspension bath biofabrication via bioresin molecular weight tuning and multiwavelength optics

Volumetric Bioprinting (VBP), enables to rapidly build complex, cell-laden hydrogel constructs for tissue engineering and regenerative medicine. Light-based tomographic manufacturing enables spatial-selective polymerization of a bioresin, resulting in higher throughput and resolution than what achieved using traditional techniques. However, methods for multi-material printing are needed for a broad VBP adoption and applicability. Although converging VBP with extrusion bioprinting in support baths offers a novel, promising solution, further knowledge on the engineering of hydrogels as light-responsive, volumetrically printable baths is needed. Therefore, this study investigates the tuning of gelatin macromers, in particular leveraging the effect of molecular weight and degree of modification, to overcome these challenges, creating a library of materials for VBP and Embedded extrusion Volumetric Printing (EmVP). Bioresins with tunable printability and mechanical properties are produced, and a novel subset of gelatins and GelMA exhibiting stable shear-yielding behavior offers a new, single-component, ready-to-use suspension medium for in-bath printing, which is stable over multiple hours without needing temperature control. As proof-of-concept biological application, bioprinted gels are tested with insulin-producing pancreatic cell lines for 21 days of culture. Leveraging a multi-color printer, complex multi-material and multi-cellular geometries are produced, enhancing the accessibility of volumetric printing for advanced tissue models.

bioengineering↗

bioRxiv2020

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

Multiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called hashing. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

genomics↗

bioRxiv2020

Genomic and morphologic characterization of a planktonic Thiovulum (Campylobacterota) dominating the surface waters of the sulfidic Movile Cave, Romania

Life in Movile Cave (Romania) relies entirely on carbon fixation by bacteria. The microbial community in the surface water of Movile Caves hypoxic air bells is dominated by large spherical-ovoid bacteria we identified as Thiovulum sp. (Campylobacterota). These form a separate phylogenetic cluster within the Thiovulaceae, consisting mostly of freshwater cave bacteria. We compared the closed genome of this Thiovulum to that of the marine strain Thiovulum ES, and to a genome we assembled from public data from the sulfidic Frasassi caves. The Movile and Frasassi Thiovulum were very similar, differing greatly from the marine strain. Based on their genomes, cave Thiovulum can switch between aerobic and anaerobic sulfide oxidation using O2 and NO3- as electron acceptors, respectively. NO3-, is likely reduced to NH3 via dissimilatory nitrate reduction to ammonia using periplasmic nitrate reductase (Nap) and hydroxylamine oxidoreductase. Thus, Thiovulum, is likely important to both S and N cycles in sulfidic subterranean aquatic ecosystems. Additionally, we suggest that the short peritrichous flagella-like structures typical of Thiovulum are type IV pili, for which genes were found in all Thiovulum genomes. These pili may play a role in veil formation, connecting adjacent cells and the exceptionally fast swimming of these bacteria.

microbiology↗

bioRxiv2019

Effective Tyrosine Kinase Inhibitors result in the intracellular accumulation of EGFR and allows response prediction in patients

Clinical responses to EGFR tyrosine kinase inhibitors are restricted only to tumors harboring specific activating mutations and even then, not all tyrosine kinase inhibitors provide clinical benefit. We here show that the addition of EGFR-TKIs results in a strong and rapid intracellular accumulation of the protein. However, this accumulation was observed only in the context of a combination of a TKI-sensitive mutation with a clinically effective TKI: TKI-insensitive mutations did not show this accumulation nor did clinically ineffective TKIs induce accumulation. All TKIs effectively inhibited EGFR phosphorylation and downstream pathway activation, irrespective of the mutation present in EGFR. The discrepancy between molecular activity of TKIs and their efficacy in patients therefore is mimicked by the mutation- and TKI-specificity of intracellular accumulation. Using this intracellular accumulation as assay, we were able to predict response to gefitinib in a panel of cell-lines (harboring different EGFR mutations) and predicted clinical benefit to EGFR TKIs on a cohort of unselected pulmonary adenocarcinoma patients (hazard ratio 0.21, P=0.0004). Even in patients harboring rare mutations with unknown TKI-sensitivity, intracellular accumulation was predictive of the clinical response. The intracellular accumulation depended on a continued presence of TKI indicating that TKIs exert a continued effect on the protein even after its dephosphorylation. It is therefore possible that accumulation is caused by conformational changes induced by both the mutation and the TKI and this change induces a block in intracellular trafficking. Interestingly, intracellular accumulation was observed independent of the genetic background of the cell, indicating that accumulation is almost entirely dictated by the combination of mutation and TKI. Our results therefore suggest that TKI-sensitivity is tumor-type independent.

cancer biology↗

bioRxiv2019

HiVA: an integrative wet- and dry-lab platform for haplotype and copy number analysis of single-cell genomes

Haplotyping is imperative for comprehensive analysis of genomes, imputation of genetic variants and interpretation of error-prone single-cell genomic data. Here we present a novel sequencing-based approach for whole-genome SNP typing of single cells, and determine genome-wide haplotypes, the copy number of those haplotypes as well as the parental and segregational origin of chromosomal aberrations from sequencing- and array-based SNP landscapes of single cells. The analytical workflow is made available as an interactive web application HiVA (https://hiva.esat.kuleuven.be).

genomics↗

bioRxiv2019

Predicting disease-causing variant combinations

Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (bi-locus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bi-locus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bi-locus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important new step towards the genetic understanding of rare diseases, paving the way to new clinical knowledge and improved patient care.

bioinformatics↗

bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗