Biology subjects

Bachman, W.

Publications and source records attributed to Bachman, W..

3 recordsLinked to original sources

bioRxiv2023

In vivo evolution of a Klebsiella pneumoniae capsule defect promotes complement-mediated opsono-phagocytosis and persistence during recurrent infection

Klebsiella pneumoniae carbapenemase-producing K. pneumoniae (KPC-Kp) bloodstream infections rarely overwhelm the host but are associated with high mortality. The complement system is a key host defense against bloodstream infection. However, there are varying reports of serum resistance among KPC-Kp isolates. We assessed growth of 59 KPC-Kp clinical isolates in human serum and found increased resistance in 16/59 (27%). We identified five genetically-related bloodstream isolates with varying serum resistance profiles collected from a single patient during an extended hospitalization marked by recurrent KPC-Kp bloodstream infections. We noted a loss-of-function mutation in the capsule biosynthesis gene, wcaJ, that emerged during infection was associated with decreased polysaccharide capsule content, and resistance to complement-mediated killing. Surprisingly, disruption of wcaJ increased deposition of complement proteins on the microbial surface compared to the wild-type strain and led to increased complement-mediated opsono-phagocytosis in human whole blood. Disabling opsono-phagocytosis in the airspaces of mice impaired in vivo control of the wcaJ loss-of-function mutant in an acute lung infection model. These findings describe the rise of a capsular mutation that promotes KPC-Kp persistence within the host by enabling co-existence of increased bloodstream fitness and reduced tissue virulence. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/542722v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@155736eorg.highwire.dtl.DTLVardef@10d47c0org.highwire.dtl.DTLVardef@e1ab83org.highwire.dtl.DTLVardef@1c33131_HPS_FORMAT_FIGEXP M_FIG Graphical abstract Created with BioRender.com C_FIG

microbiology↗

bioRxiv2022

A Machine Learning Framework to Predict Subcellular Morphology of Endothelial Cells for Digital Twin Generation

Gaining insight into different cell behaviors is key to better understanding different pathologies. These behaviors may be explained in part through close observation of 3D cell morphology. Therefore, the objective of this research was to develop a machine learning (ML) framework that can predict 3D subcellular morphological variation of endothelial cells (ECs) to generate digital twins. ECs were cultured and their membrane, nucleus, and focal adhesion (FA) sites were stained and imaged with confocal microscopy. The multicellular confocal z stacks were segmented resulting in a total of 60 single-cell stacks. Fifty randomly picked cells were augmented 20-fold to train the ML framework, and the remaining 10 were used for an independent test of prediction accuracy. The ML framework was based on an open-source conditional generative adversarial network (cGAN), which was expanded to make 3D predictions using membrane only as input to predict nucleus and FA morphology. After training the framework, the results on the independent test showed an average prediction accuracy of [~]87% for nucleus and [~]70% for FA sites. The predictions were used to build a digital twin of each EC and compared to their respective ground truth, showing an average [~]79% global accuracy and [~]84% accuracy in FA-Nucleus distribution. The results presented show the effectiveness of the developed ML framework to generate digital twins of Ecs using limited amount of data. These digital twins can be used to couple EC morphology with different behaviors. The ML framework can be potentially expanded to predict morphology of other subcellular structures as well as to study other types of cells.

bioengineering↗

bioRxiv2021

Discrete Protein Metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions

Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited datasets (e.g., cell membrane images). However, calculating the accuracy score of these predictions can be challenging due to the discrete/point pattern like nature of FA sites. In the present work, a new image similarity metric, discrete protein metric (DPM), was developed to calculate FA prediction accuracy. This metric measures differences in distribution (d), shape/size (s), and angle (a) of FA sites between the predicted image and its ground truth image. Performance of the DPM was evaluated by comparing it to three other commonly used image similarity metrics: Pearson correlation coefficient (PCC), feature similarity index (FSIM), and Intersection over Union (IoU). A sensitivity analysis was performed by comparing changes in each metric value due to quantifiable changes in FA site location, number, aspect ratio, area, or orientation. Furthermore, accuracy score of DL-generated predictions was calculated using all four metrics to compare their ability to capture variation across samples. Results showed better sensitivity and range of variation for DPM compared to the other metrics tested. Most importantly, DPM had the ability to determine which FA predictions were quantitatively more accurate and consistent with qualitative assessments. The proposed DPM hence provides a method to validate DL-generated FA predictions and can be extended to evaluating other predicted or segmented discrete structures of biomedical relevance.

bioengineering↗