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BioRxiv : the Preprint Server For... Jun 2024Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on...
INTRODUCTION
Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia.
METHODS
Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant.
RESULTS
At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences.
CONCLUSIONS
In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.
PubMed: 38948832
DOI: 10.1101/2024.03.26.586768 -
BioRxiv : the Preprint Server For... Jun 2024Polyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine...
Polyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine synthesis with D, L-alpha-difluoromethylornithine (DFMO) causes a G1 cell cycle arrest, dose-dependent decreases in sarcosphere formation from Ewing sarcoma cell lines growing in non-adherent conditions and a decrease in clonogenic growth in soft agar. Further, we utilized our orthotopic implantation/amputation model of Ewing sarcoma metastasis to demonstrate that DFMO slowed primary tumor growth in addition to limiting metastasis. RNA sequencing demonstrated gene expression patterns consistent with induction of ferroptosis caused by polyamine depletion. Induction of ferroptosis was validated in vitro by demonstrating that ferrostatin-1, an inhibitor of ferroptosis, allows sphere formation even in the presence of DFMO. Collectively, these results reveal a novel mechanism by which DFMO prevents metastasis - induction of ferroptosis due to polyamine depletion. Our results provide preclinical justification to test the ability of DFMO to prevent metastatic recurrence in Ewing sarcoma patients at high risk for relapse.
PubMed: 38948823
DOI: 10.1101/2024.06.14.599064 -
BioRxiv : the Preprint Server For... Jun 2024Solid carcinomas are often highly heterogenous cancers, arising from multiple epithelial cells of origin. Yet, how the cell of origin influences the response of the...
Solid carcinomas are often highly heterogenous cancers, arising from multiple epithelial cells of origin. Yet, how the cell of origin influences the response of the tumor microenvironment is poorly understood. Lung adenocarcinoma (LUAD) arises in the distal alveolar epithelium which is populated primarily by alveolar epithelial type I (AT1) and type II (AT2) cells. It has been previously reported that AT1 cells can give rise to a histologically-defined LUAD that is distinct in pathology and transcriptomic identity from that arising from AT2 cells . To determine how cells of origin influence the tumor immune microenvironment (TIME) landscape, we comprehensively characterized transcriptomic, molecular, and cellular states within the TIME of AT1 and AT2-derived LUAD using KRAS oncogenic driver mouse models. Myeloid cells within the AT1-derived LUAD TIME were increased, specifically, immunoreactive monocytes and tumor associated macrophages (TAMs). In contrast, the AT2 LUAD TIME was enriched for Arginase-1 myeloid derived suppressor cells (MDSC) and TAMs expressing profiles suggestive of immunosuppressive function. Validation of immune infiltration was performed using flow cytometry, and intercellular interaction analysis between the cells of origin and major myeloid cell populations indicated that cell-type specific markers SFTPD in AT2 cells and CAV1 in AT1 cells mediated unique interactions with myeloid cells of the differential immunosuppressive states within each cell of origin mouse model. Taken together, AT1-derived LUAD presents with an anti-tumor, immunoreactive TIME, while the TIME of AT2-derived LUAD has hallmarks of immunosuppression. This study suggests that LUAD cell of origin influences the composition and suppression status of the TIME landscape and may hold critical implications for patient response to immunotherapy.
PubMed: 38948812
DOI: 10.1101/2024.06.19.599651 -
BioRxiv : the Preprint Server For... Jun 2024Emerging studies in humans have established the modulatory effects of repetitive transcranial magnetic stimulation (rTMS) over primary somatosensory cortex (S1) on...
BACKGROUND
Emerging studies in humans have established the modulatory effects of repetitive transcranial magnetic stimulation (rTMS) over primary somatosensory cortex (S1) on somatosensory cortex activity and perception. However, to date, research in this area has primarily focused on the hand and fingers, leaving a gap in our understanding of the modulatory effects of rTMS on somatosensory perception of the orofacial system and speech articulators.
OBJECTIVE
The present study aimed to examine the effects of different types of theta-burst stimulation-continuous TBS (cTBS), intermittent TBS (iTBS), or sham-over the tongue representation of left S1 on tactile acuity of the tongue.
METHODS
In a repeated-measures design, fifteen volunteers participated in four separate sessions, where cTBS, iTBS, sham, or no stimulation was applied over the tongue representation of left S1. Effects of TBS were measured on both temporal and spatial perceptual acuity of tongue using a custom vibrotactile stimulator.
RESULTS
CTBS significantly impaired spatial amplitude threshold at the time window of 16-30 minutes after stimulation, while iTBS improved it at the same time window. The effect of iTBS, however, was smaller than cTBS. In contrast, neither cTBS nor iTBS had any effect on the temporal discrimination threshold.
CONCLUSIONS
The current study establishes the validity of using TBS to modulate somatosensory perception of the orofacial system. Directly modifying somatosensation in the orofacial system has the potential to benefit clinical populations with abnormal tactile acuity, improve our understanding of the role of sensory systems in speech production, and enhance speech motor learning and rehabilitation.
HIGHLIGHTS
Theta-burst stimulation (TBS) can modulate somatosensation in the orofacial systemcTBS over S1 impaired spatial acuity of tongueiTBS over S1 improved spatial acuity of tongue.
PubMed: 38948808
DOI: 10.1101/2024.06.17.599457 -
BioRxiv : the Preprint Server For... Jun 2024Glycosylation-deficient Chinese hamster ovary (CHO) cell lines have been instrumental in the discovery of N-glycosylation machinery. Yet, the molecular causes of the...
Glycosylation-deficient Chinese hamster ovary (CHO) cell lines have been instrumental in the discovery of N-glycosylation machinery. Yet, the molecular causes of the glycosylation defects in the Lec5 and Lec9 mutants have been elusive, even though for both cell lines a defect in dolichol formation from polyprenol was previously established. We recently found that dolichol synthesis from polyprenol occurs in three steps consisting of the conversion of polyprenol to polyprenal by DHRSX, the reduction of polyprenal to dolichal by SRD5A3 and the reduction of dolichal to dolichol, again by DHRSX. This led us to investigate defective dolichol synthesis in Lec5 and Lec9 cells. Both cell lines showed increased levels of polyprenol and its derivatives, concomitant with decreased levels of dolichol and derivatives, but no change in polyprenal levels, suggesting DHRSX deficiency. Accordingly, N-glycan synthesis and changes in polyisoprenoid levels were corrected by complementation with human DHRSX but not with SRD5A3. Furthermore, the typical polyprenol dehydrogenase and dolichal reductase activities of DHRSX were absent in membrane preparations derived from Lec5 and Lec9 cells, while the reduction of polyprenal to dolichal, catalyzed by SRD5A3, was unaffected. Long-read whole genome sequencing of Lec5 and Lec9 cells did not reveal mutations in the ORF of , but the genomic region containing was absent. Lastly, we established the sequence of Chinese hamster DHRSX and validated that this protein has similar kinetic properties to the human enzyme. Our work therefore identifies the basis of the dolichol synthesis defect in CHO Lec5 and Lec9 cells.
PubMed: 38948797
DOI: 10.1101/2024.06.18.599300 -
BioRxiv : the Preprint Server For... Jun 2024Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and...
BACKGROUND
Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability.
METHODS
We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs.
RESULTS
Mean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.54 (0.52-0.56) for WT and 0.61 (0.56-0.66) for ILK cKO TEM images. Automated and corresponding manual measured PFP widths differed significantly for both WT (p<0.05) and ILK cKO (p<0.05), while automated and manual GBM widths differed only for ILK cKO (p<0.05) but not WT (p=0.49) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p=0.06) more than PFP (p=0.20) widths.
CONCLUSIONS
These results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis.
KEY POINTS
We leveraged U-Net architecture in an algorithm to measure the widths of glomerular basement membrane and podocyte foot processes.Deep learning-based automated measurement of glomerular filtration barrier morphology has promise in podocytopathy research and diagnosis.
PubMed: 38948787
DOI: 10.1101/2024.06.14.599097 -
BioRxiv : the Preprint Server For... Jun 2024The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated...
The physiological response of a cell to stimulation depends on its proteome configuration. Therefore, the abundance variation of regulatory proteins across unstimulated single cells can be associatively linked with their response to stimulation. Here we developed an approach that leverages this association across individual cells and nuclei to systematically identify potential regulators of biological processes, followed by targeted validation. Specifically, we applied this approach to identify regulators of nucleocytoplasmic protein transport in macrophages stimulated with lipopolysaccharide (LPS). To this end, we quantified the proteomes of 3,412 individual nuclei, sampling the dynamic response to LPS treatment, and linking functional variability to proteomic variability. Minutes after the stimulation, the protein transport in individual nuclei correlated strongly with the abundance of known protein transport regulators, thus revealing the impact of natural protein variability on functional cellular response. We found that simple biophysical constraints, such as the quantity of nuclear pores, partially explain the variability in LPS-induced nucleocytoplasmic transport. Among the many proteins newly identified to be associated with the response, we selected 16 for targeted validation by knockdown. The knockdown phenotypes confirmed the inferences derived from natural protein and functional variation of single nuclei, thus demonstrating the potential of (sub-)single-cell proteomics to infer functional regulation. We expect this approach to generalize to broad applications and enhance the functional interpretability of single-cell omics data.
PubMed: 38948785
DOI: 10.1101/2024.06.17.599449 -
BioRxiv : the Preprint Server For... Jun 2024CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS)...
CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of marker genes, followed by guide RNA (gRNA) sequencing. Analysis of these data presents several statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our experimental design model findings that, when using Waterbear for analysis, high power is maintained even at low cell coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens.
PubMed: 38948774
DOI: 10.1101/2024.06.17.599448 -
Biofilm Jun 2024platforms capable of mimicking the hydrodynamic conditions prevailing in natural aquatic environments have been previously validated and used to predict the fouling...
platforms capable of mimicking the hydrodynamic conditions prevailing in natural aquatic environments have been previously validated and used to predict the fouling behavior on different surfaces. Computational Fluid Dynamics (CFD) has been used to predict the shear forces occurring in these platforms. In general, these predictions are made for the initial stages of biofilm formation, where the amount of biofilm does not affect the flow behavior, enabling the estimation of the shear forces that initial adhering organisms have to withstand. In this work, we go a step further in understanding the flow behavior when a mature biofilm is present in such platforms to better understand the shear rate distribution affecting marine biofilms. Using 3D images obtained by Optical Coherence Tomography, a mesh was produced and used in CFD simulations. Biofilms of two different marine cyanobacteria were developed in agitated microtiter plates incubated at two different shaking frequencies for 7 weeks. The biofilm-flow interactions were characterized in terms of the velocity field and shear rate distribution. Results show that global hydrodynamics imposed by the different shaking frequencies affect biofilm architecture and also that this architecture affects local hydrodynamics, causing a large heterogeneity in the shear rate field. Biofilm cells located in the streamers of the biofilm are subjected to much higher shear values than those located on the bottom of the streamers and this dispersion in shear rate values increases at lower bulk fluid velocities. This heterogeneity in the shear force field may be a contributing factor for the heterogeneous behavior in metabolic activity, growth status, gene expression pattern, and antibiotic resistance often associated with nutrient availability within the biofilm.
PubMed: 38948680
DOI: 10.1016/j.bioflm.2024.100204 -
Frontiers in Endocrinology 2024Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on...
BACKGROUND
Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators.
METHODS
Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits.
RESULTS
Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models.
CONCLUSION
Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.
Topics: Thyroid Nodule; Humans; Machine Learning; Female; Male; China; Cross-Sectional Studies; Middle Aged; Adult; Early Detection of Cancer; Aged; Mass Screening; Ultrasonography
PubMed: 38948526
DOI: 10.3389/fendo.2024.1385167