Biology subjects

Jimenez, G.

Publications and source records attributed to Jimenez, G..

7 recordsLinked to original sources

bioRxiv2026

Mechanofiltration Enables High-Throughput Measurements of Bacterial Cell Mechanics

Bacteria experience diverse mechanical forces throughout their natural environments, yet quantitative measurements of bacterial biomechanics remain challenging because most existing techniques require specialized instrumentation, such as atomic force microscopy (AFM) or microfluidic devices. Here, we introduce mechanofiltration, a simple, high-throughput assay that estimates whole-cell mechanics using standard laboratory equipment. In mechanofiltration, bacterial suspensions are centrifuged through porous membranes in multiwell plates. Pressure generated during centrifugation drives cells toward pores that are smaller than cell width, requiring cells to deform to transit through the membrane. By combining recovery on the opposite side of the filter with measurements of cell size, the assay estimates maximum cell deformation, a size-corrected proxy for whole-cell stiffness. We show that mechanofiltration detects established reductions in Escherichia coli cell stiffness caused by genetic disruption of load-bearing cell envelope components, treatment with outer membrane-destabilizing agents, and sublethal exposure to antibiotics. Fold changes in maximum deformation closely agree with published measurements obtained using AFM, cell-bending assays, and osmotic shock experiments. Mechanofiltration also reproduces previously reported mechanical differences among bacterial species with distinct morphologies and envelope architectures. Together, these results establish mechanofiltration as an accessible, inexpensive, and scalable approach for identifying genetic and environmental determinants of bacterial mechanics and for high-throughput screening of bacterial biomechanical phenotypes.

microbiology↗

bioRxiv2025

Physical mechanisms that enable bacteria to traverse channels half their width

Bacteria colonize surfaces in the environment and can also penetrate tissues and materials by entering micro- and nanoscale cracks and pores. Staphylococcus aureus has been observed within nanoscale channels in bone that are 2-3 times smaller than cell diameter. Once inside bone, bacteria are protected from host immunity and systemic antibiotics, potentially contributing to chronic and recurrent infections. The physical mechanisms that enable bacteria to enter channels smaller than cell width are unclear. It has been proposed that bacteria traverse narrow passages through division - such that daughter cells form within small channels and eventually create a chain of cells extending down the channel length. Here we use microfluidics to test the idea that S. aureus can traverse bone-like nanochannels through growth. We examined 322 individual cells trapped within tapered nanochannels ([~]1.5 to 0.4 mm in width). When cells were deformed below 606 nm (65% original width), growth and division were slowed or completely inhibited. When cell division did occur in nanochannels, daughter cells were more likely to travel towards the wider side of the channel. Hence, it is unlikely that cell division would preferentially enable transit of S. aureus into nanoscale channels. However, the magnitudes of fluid pressure needed to deform S. aureus to widths similar to that seen in bone (1 to 6.5 kPa), were small relative to fluid pressure in bone generated by physical forces in vivo (8-20 kPa). Thus, our findings suggest that colonization of nanoscale channels in bone or other tissues is more likely due to moderate fluid pressure rather than growth. ImportanceBacteria that colonize materials and tissues within the body can be difficult to remove, even with thorough cleaning and application of antibiotics. Recent studies show that bacteria not only colonize the surfaces of tissues in the body but can also squeeze into naturally occurring pores and channels and thereby gain protection from immune cells and antibiotics. Here we ask how physical forces and cell growth might enable bacteria to enter small pores within materials. We use microfluidic devices to study the growth and migration of the human pathogenic bacteria, S. aureus, which is the leading cause of chronic bone infections.

microbiology↗

bioRxiv2024

Guided assembly of multispecies positive biofilms targeting undesirable bacteria

The use of synthetic microbial communities (SynComs) engineered to form positive biofilms that prevent the settlement of harmful bacteria is emerging as a promising strategy in biotechnology, particularly in reducing reliance on chemical antimicrobials. Despite this potential, the rationale for selecting specific strains in SynComs and the mechanisms underlying their antagonistic effects remains insufficiently understood. In this study, we present a bottom-up approach integrating live-cell imaging with high-throughput analysis of multi-strain biofilms across diverse scenarios. Through this method, we identified beneficial strains based on their superior ability to exclude undesirable bacteria and form mixed biofilms. Notably, our findings revealed that competitive strains against undesirable bacteria could also exclude other beneficial strains, emphasising the need for compatibility control in SynComs design. SynComs composed of B. velezensis and Pediococcus spp. demonstrated enhanced pathogen exclusion compared to single strains. Temporal analysis of biofilm interactions, supported by mathematical models, showed that pathogen exclusion was primarily driven by nutritional competition (Jameson effect) with additional specific interference mechanisms (prey-predator Lotka-Volterra model). Furthermore, pre-establishing SynComs to surfaces significantly increased pathogen inhibition, indicating a distinct biofilm-associated exclusion effect. These insights offer a framework for rational SynCom design and deepen our understanding of the mechanisms underpinning positive biofilm applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/618781v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@a8ca36org.highwire.dtl.DTLVardef@173dc16org.highwire.dtl.DTLVardef@1764af2org.highwire.dtl.DTLVardef@1328cfc_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2023

Automated segmentation by deep learning of neuritic plaques and neurofibrillary tangles in brain sections of Alzheimer's Disease Patients

Alzheimers Disease (AD) is a neurodegenerative disorder with complex neuropathological features, such as phosphorylated tau (p-tau) positive neurofibrillary tangles (NFTs) and neuritic plaques (NPs). The quantitative evaluation of p-tau pathology is a key element for the diagnosis of AD and other tauopathies. Assessment of tauopathies relies on semi-quantitative analysis and does not consider lesions heterogeneity (e.g., load and density of NFTs vs NPs). In this study, we developed a deep learning-based workflow for automated annotation and segmentation of NPs and NFTs from AT8-immunostained whole slide images (WSIs) of AD brain sections. Fifteen WSIs of frontal cortex from four biobanks with different tissue quality, staining intensity and scanning formats were used for the present study. We first applied an artificial intelligence (AI-)-driven iterative procedure to improve the generation of pathologist validated training datasets for NPs and NFTs. This procedure increased the annotation quality by more than 50%, especially for NPs when present in high density. Using this procedure, we obtained an expert validated annotation database with 5013 NPs and 5143 NFTs. As a second step, we trained two U-Net convolutional neural networks (CNNs) for accurate detection and segmentation of NPs or NFTs. The workflow achieved a high accuracy and consistency, with a mean Dice similarity coefficient of 0.81 for NPs and 0.77 for NFTs. The workflow also showed good generalization performance across different patients with different staining and tissue quality. Our study demonstrates that artificial intelligence can be used to correct and enhance annotation quality especially for complex objects, even when intermingled and present in high density, in brain tissue. Furthermore, the expert validated databases allowed to generate highly accurate models for segmenting discrete brain lesions using a commercial software. Our annotation database will be publicly available to facilitate human digital pathology applied to AD.

neuroscience↗

bioRxiv2022

Molecular basis of DNA recognition by the HMG-box-C1 module of Capicua

The HMG-box protein Capicua (CIC) is an evolutionarily conserved transcriptional repressor with key functions in development and disease-associated processes. CIC binds DNA using an exclusive mechanism that requires both its HMG-box and a separate domain called C1, but how these domains cooperate to recognize specific DNA sequences is not known. Here we report the crystal structure of the human CIC HMG-box and C1 domains in complex with an 18-base-pair DNA oligomer containing a consensus octameric CIC binding site. We find that both protein domains adopt independent tri-helical structures that pack against opposite sides of the DNA helix. The C1 domain in particular folds into a helix-turn-helix (HTH) structure that resembles the FF phosphoprotein binding domain. It inserts into the major groove of the DNA and plays a direct role in enhancing both the affinity and sequence specificity of CIC DNA binding. Our results reveal a unique bipartite protein module, ensuring highly specific DNA recognition by CIC, and show how this mechanism is disrupted by cancer mutations affecting either the HMG-box or C1 domains.

biochemistry↗

bioRxiv2022

Cross-species analysis of LZTR1 loss-of-function mutants demonstrates dependency to RIT1 orthologs

RAS GTPases are highly conserved proteins involved in the regulation of mitogenic signaling. We have previously described a novel Cullin 3 RING E3 ubiquitin ligase complex formed by the substrate adaptor protein LZTR1 that binds, ubiquitinates, and promotes proteasomal degradation of the RAS GTPase RIT1. In addition, others have described that this complex is also responsible for the ubiquitination of canonical RAS GTPases. Here, we have analyzed the phenotypes of LZTR1 loss-of-function mutants in both fruit flies and mice and have demonstrated biochemical dependency on their RIT1 orthologs. Moreover, we show that LZTR1 is haplosufficient in mice and that embryonic lethality of the homozygous null allele can be rescued by deletion of RIT1.

genetics↗

bioRxiv2020

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

It is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancers sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants. STATEMENT OF SIGNIFICANCEA central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

bioinformatics↗