Biology subjects

Kaler, L.

Publications and source records attributed to Kaler, L..

7 recordsLinked to original sources

bioRxiv2025

Female sex hormones enhance gonococcal colonization at the endocervix by modifying cervical mucus

Neisseria gonorrhoeae is a human-exclusive pathogen that causes gonorrhea. Gonococci (GC) initiate female infections by colonizing the cervix, which can remain asymptomatic, cause cervicitis, or ascend to the upper female reproductive tract (FRT), leading to severe tissue damage. The FRT undergoes sex hormone-mediated changes during the menstrual cycle, which have long been implicated in the vulnerability to GC infection. One of the major changes is the increase and decrease in the production of the gel-forming mucin MUC5B by the endocervix in response to the level of estradiol (E2). This study examined the impact of sex hormones on GC infection of the human cervix, utilizing a human cervical tissue explant model. Tissue explants were treated without and with E2 alone or in combination with progesterone (E2+P4) to mimic various menstrual cycle phases. Treatment of E2 or E2+P4 enhanced GC colonization at the endocervix exclusively, but did not affect epithelial transmigration. While both treatments increased the number of GC microcolonies, E2+P4 increased GC colony size on the endocervical epithelium. These increases were independent of GC host receptors, carcinoembryonic antigen-related cell adhesion molecules. GC effectively diffused through cervical mucus to interact with the cervical epithelium under all hormone conditions and through mucin hydrogels with different MUC5B and MUC5AC compositions. Mucus gels collected from cervical explants and animal mucin mixtures enhanced GC aggregation in vitro. GC diffusion through mucin-hydrogels and aggregation in the presence of cervical mucus or animal mucins decreased as the MUC5B concentration increased. Our results suggest that female sex hormones promote GC colonization at the human endocervix by modulating the cervical mucus production, regulating womens susceptibility to GC infection, and further reveal the ability of GC to evade the mucus defense barrier for infection. Author SummaryNeisseria gonorrhoeae is a bacterial pathogen that primarily infects the human genital and female reproductive tracts, causing gonorrhea. While this bacterium can infect both men and women, the infection can lead to severe and permanent damage to womens reproductive systems. Currently, the relationship of gonococcal infection with the menstrual cycle is unknown. Here, we utilize a human cervical tissue explant model that mimics gonococcal infection in women to examine the impact of female sex hormones that drive the menstrual cycle on gonococcal infection. We found that estrogen alone or in combination with progesterone enhanced gonococcal colonization, increasing both the number and size of bacterial microcolonies on the cervical luminal surface, through regulating mucus production. Gonococci effectively penetrate through mucus layers to reach cervical epithelial cells and also prefer to aggregate with each other in the presence of mucus. Our results reveal that hormone-regulated mucus production changes the vulnerability of women to gonococcal infection, and that gonococci convert the mucus defense barrier into a colonization facilitator.

microbiology↗

bioRxiv2024

A Self-Supervised Learning Approach for High Throughput and High Content Cell Segmentation

In principle, AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large datasets, human input, and computational expertise, along with issues of limited generalizability and the necessity for specialized training are notable drawbacks of nominally "automated" segmentation tools. To overcome this roadblock, we introduce an innovative, user-friendly self-supervised learning method (SSL) for pixel classification that requires no dataset-specific modifications or curated labelled data sets, thus providing a more streamlined cell segmentation approach for high-throughput and high-content research. We demonstrate that our algorithm meets the criteria of being fully automated with versatility across various magnifications, optical modalities and cell types. Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. Our SSL technique displayed consistent F1 scores across segmented images, with scores ranging from 0.831 to 0.876, outperforming the popular Cellpose algorithm, which showed greater variance in F1 scores from 0.645 to 0.8815, mainly due to errors in segmentation. On average, our SSL method achieved an F1 score of 0.852 {+/-}0.017, exceeding Cellposes average of 0.804 {+/-}0.08. This novel SSL method not only advances segmentation accuracy but also minimizes the need for extensive computational expertise and data security concerns, making it easier for biological researchers to incorporate automated segmentation into their studies.

bioengineering↗

bioRxiv2023

Mucus physically restricts influenza A viral particle access to the epithelium

Prior work suggests influenza A virus (IAV) crosses the airway mucus barrier in a sialic acid-dependent manner through the actions of the viral envelope glycoproteins, hemagglutinin and neuraminidase. However, host and viral factors that influence how efficiently mucus traps IAV remain poorly defined. In this work, we assessed how the physicochemical properties of mucus influence its ability to effectively capture IAV using fluorescence video microscopy and multiple particle tracking. We found an airway mucus gel layer must be produced with virus-sized pores to physically constrain IAV. While sialic acid binding by IAV may improve mucus trapping efficiency, sialic acid binding preference was found to have little impact on IAV mobility and the fraction of viral particles expected to penetrate the mucus barrier. Further, we demonstrate synthetic polymeric hydrogels engineered with mucus-like architecture are similarly protective against IAV infection despite their lack of sialic acid decoy receptors. Together, this work provides new insights on mucus barrier function toward IAV with important implications on innate host defense and interspecies transmission.

microbiology↗

bioRxiv2022

MUC5B mobilizes and MUC5AC spatially aligns mucociliary transport on human airway epithelium

Airway mucus acts as a protective barrier and vehicle for clearance of pathogens, providing the lungs with a defense mechanism called mucociliary clearance (MCC). Airway mucus is composed of two mucins, mucin 5B (MUC5B) and 5AC (MUC5AC) that form a hydrogel that enables functional clearance in health. However, abnormalities in mucin expression, specifically increases in MUC5AC is observed in chronic respiratory diseases and leading to defective MCC. Our current understanding of MCC impairment focuses on mucin concentration, while the impact of mucin composition remains unclear. Here, we use MUC5AC/B-knock out (KO) human airway epithelial (HAE) tissue cultures to understand the role and contribution of individual secreted mucins on MCC mechanisms. We find that KO cultures result in impaired or discoordinated mucociliary transport demonstrating the importance of each of these mucins to effective MCC and shedding light on a new mechanism of mucin composition-dependent airway clearance.

physiology↗

bioRxiv2022

Machine Learning Informed Predictions of Nanoparticle Mobility and Fate in the Mucus Barrier

Diffusion and transport of nanomaterials through mucus, is of critical importance to many basic and applied areas of research such as drug delivery and infectious disease. However, it is often challenging to interpret the dynamics of nanoparticles within the mucus gel due to its inherently heterogeneous microstructure and biochemistry. In this study, we measured the diffusion of densely PEGylated nanoparticles (NP) in human airway mucus ex vivo using multiple particle tracking and utilized machine learning to classify NP movement as either traditional Brownian motion (BM) or one of two different models of anomalous diffusion, fractional Brownian motion (FBM) and continuous time random walk (CTRW). Specifically, we employed a physics-based neural network model to predict the modes of diffusion experienced by individual NP in human airway mucus. We observed rapidly diffusing NP primarily exhibit BM whereas CTRW and FBM exhibited lower diffusion rates. Given the use of muco-inert nanoparticles, the observed transition from diffusive (BM) to sub-diffusive (CTRW/FBM) motion is likely a result of patient-to-patient variation in mucus network pore size. Using mathematic models that account for the mode of NP diffusion, we predicted the percentage of nanoparticles that would cross the mucus barrier over time in human airway mucus with varied total solids concentration. We also applied this approach to explore the transport modes and predicted fate of influenza A virus within human mucus. These results provide new tools to evaluate the extent of synthetic and viral nanoparticle penetration through mucus in the lung and other tissues.

bioengineering↗

bioRxiv2020

Modeling airway dysfunction in asthma using synthetic mucus biomaterials

As asthma worsens, occlusion of airways with mucus significantly contributes to airflow obstruction and reduced lung function. Recent evidence from clinical studies has shown mucus obtained from adults and children with asthma possesses altered mucin composition. However, how these changes alter the functional properties of the mucus gel is not yet fully understood. To study this, we have engineered a synthetic mucus biomaterial to closely mimic the properties of native mucus in health and disease. We demonstrate this model possesses comparable biophysical and transport properties to native mucus ex vivo collected from human subjects and in vitro isolated from human airway epithelial (HAE) tissue cultures. We found by systematically varying mucin composition that mucus gel viscoelasticity is enhanced when predominantly composed of mucin 5AC (MUC5AC), as is observed in asthma. As a result, asthma-like synthetic mucus gels are more slowly transported on the surface of HAE tissue cultures and at a similar rate to native mucus produced by HAE cultures stimulated with the type 2 cytokine IL-13, known to contribute to airway inflammation and MUC5AC hypersecretion in asthma. We also discovered the barrier function of asthma-like synthetic mucus towards influenza A virus was impaired as evidenced by the increased frequency of infection in MUC5AC-rich hydrogel coated HAE cultures. Together, this work establishes a biomaterial-based approach to understand airway dysfunction in asthma and related muco-obstructive lung diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/342766v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@bed446org.highwire.dtl.DTLVardef@626c28org.highwire.dtl.DTLVardef@1aabb6borg.highwire.dtl.DTLVardef@1f69081_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Influenza A virus diffusion through mucus gel networks

In this study, influenza A virus (IAV) and nanoparticle diffusion in human airway mucus was quantified using fluorescent video microscopy and multiple particle tracking. In previous work, it was determined that mucin-associated sialic acid acts as a decoy receptor for IAV hemagglutinin binding, and that virus passage through the mucus gel layer is facilitated through the sialic-acid cleaving enzyme, neuraminidase (NA), also present on the IAV envelope. However, our data suggests the mobility of IAV in mucus is significantly influenced by the mesh structure of the gel, as measured by nanoparticle probes, and NA activity is not required to facilitate virus passage through mucus gels. Using newly developed analyses, the binding affinity of IAV to the 3D mucus meshwork was estimated for individual virions with dissociation constants in the mM range, indicative of weak and reversible IAV-mucus interactions. We also found IAV diffusion significantly increased in mucus when treated with a mucolytic agent to break mucin-mucin disulfide bonds. In addition, IAV diffusion was significantly limited in a synthetic mucus model as crosslink density was systematically increased and network pore size was reduced. The results of this work provide important insights on how the balance of adhesive and physical barrier properties of mucus influence the dissemination of IAV within the lung microenvironment.

biophysics↗