Using Aspergillus nidulans to study alpha-1,3-glucan synthesis and the resistance mechanism against cell wall targeting drugs

Systemic fungal infection is a life-threatening problem. Anti-fungal drugs are the most effective clinical strategy to cure such infections. However, most current anti-fungal drugs either have high toxicity or have a narrow spectrum of effect. Meanwhile, anti-fungal drugs are losing their clinical efficacy due to emerging drug resistance. To protect us from these deadly pathogenic fungi, scientists need to study new drug targets and to solve problems related to drug resistance. The cell wall is essential for fungal cell survival and is absent from animal cells, so it is a promising reservoir for screening safe and effective drug targets. Alpha-1,3-glucan is one of the major cell wall carbohydrates and is important for the virulence of several pathogenic fungi. In this thesis, molecular biology and microscopy techniques were used to investigate the function and the synthesis process of α-1,3-glucan in the model fungus A. nidulans. My results showed that α-1,3-glucan comprises about 15% of A. nidulans cell wall dry weight, but also that α-1,3-glucan does not have an important role in cell wall formation and cell morphology. Deletion of α-1,3-glucan only affects conidial adhesion and cell sensitivity to calcofluor white. In contast, elevated α-1,3-glucan content can cause severe phenotypic defects. To study the α-1,3-glucan synthesis process, I systematically characterized four proteins, including two α-1,3-glucan synthases (AgsA and AgsB) and two amylase-like proteins (AmyD and AmyG). Results showed AgsA and AgsB are both functional synthases. AgsB is the major synthase due to its constant expression. AgsA mainly functions in conidiation stages. AmyG is a cytoplasmic protein that is critical for α-1,3-glucan synthesis, likely being required for an earlier step in the synthesis process. In contrast to the other three proteins, AmyD has a repressive effect on α-1,3-glucan accumulation. These results shed light on therapeutic strategies that might be developed against α-1,3-glucan. I also developed a strategy to investigate drug resistance mutations. The tractability of A. nidulans and the power of next generation sequencing enabled an easy approach to isolate single mutation strains and to identify the causal mutations from a genome scale efficiently. I suggest this strategy has applications to study the drug resistance mechanisms of current anti-fungal drugs and even possibly future ones

STUDYING LIQUID DYNAMICS WITH OPTICAL KERR EFFECT SPECTROSCOPY

Time-resolved optical Kerr effect (OKE) spectroscopy is an established tech-nique for studying the orientational dynamics of liquids. The reduced spectral density (RSD) obtained from transforming the OKE spectrum into the frequency domain has shown its utility in probing the intermolecular dynamics of liquids. The intermolecular dynamics of benzene and its isotopologues have been inves-tigated using OKE spectroscopy. The observed linear dependence of the collective orientational correlation time on the square root of the moment of inertia leads to the conclusion that there is strong translation-rotation coupling in benzene liquid. By ana-lyzing of the RSDs of benzene and its isotopologues, it is evident that the librational scattering dominates the high-frequency region and plays a major role throughout the RSD. The dynamics of confined liquids have also been studied using OKE spectros-copy. A blue shift of the high-frequency portion of the RSD of confined benzene has been observed. This blue shift is similar to the shift in the RSD of bulk benzene as the temperature is decreased. It is believed that this shift in the high-frequency portion of the RSD reflects the densification of the liquid in confinement. This phenomenon has also been observed in confined pyridine and acetonitrile liquids. OKE spectroscopy has also been employed in studies of the dynamics of nano-confined propionitrile and trimethyl acetonitrile. The results of these studies indicate that propionitrile can form a lipid-bilayer-like structure at the confining surfaces, with the alkyl tails of the sublayers being entangled. However, due to the steric effects im-posed by the tert-butyl group in trimethyl acetonitrile, bilayers are not formed at the confining surfaces for this liquid

Biosensing Technologies for Mycobacterium tuberculosis Detection: Status and New Developments

Biosensing technologies promise to improve Mycobacterium tuberculosis (M. tuberculosis) detection and management in clinical diagnosis, food analysis, bioprocess, and environmental monitoring. A variety of portable, rapid, and sensitive biosensors with immediate “on-the-spot” interpretation have been developed for M. tuberculosis detection based on different biological elements recognition systems and basic signal transducer principles. Here, we present a synopsis of current developments of biosensing technologies for M. tuberculosis detection, which are classified on the basis of basic signal transducer principles, including piezoelectric quartz crystal biosensors, electrochemical biosensors, and magnetoelastic biosensors. Special attention is paid to the methods for improving the framework and analytical parameters of the biosensors, including sensitivity and analysis time as well as automation of analysis procedures. Challenges and perspectives of biosensing technologies development for M. tuberculosis detection are also discussed in the final part of this paper

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged as a potential method for modeling complex network data. Deep models, on the other hand, have low interpretability, which prevents their usage in decision-critical contexts like healthcare. To bridge this gap, we propose an interpretable framework to analyze disorder-specific Regions of Interest (ROIs) and prominent connections. The proposed framework consists of two modules: a brain-network-oriented backbone model for disease prediction and a globally shared explanation generator that highlights disorder-specific biomarkers including salient ROIs and important connections. We conduct experiments on three real-world datasets of brain disorders. The results verify that our framework can obtain outstanding performance and also identify meaningful biomarkers. All code for this work is available at https://github.com/HennyJie/IBGNN.git.Comment: Previous version presented at icml-imlh 2021 (no proceedings, archived at 2107.05097), this version is accepted to miccai 202

Fluorescent Nanoparticle-Based Indirect Immunofluorescence Microscopy for Detection of Mycobacterium tuberculosis

A method of fluorescent nanoparticle-based indirect immunofluorescence microscopy (FNP-IIFM) was developed for the rapid detection of Mycobacterium tuberculosis. An anti-Mycobacterium tuberculosis antibody was used as primary antibody to recognize Mycobacterium tuberculosis, and then an antibody binding protein (Protein A) labeled with Tris(2,2-bipyridyl)dichlororuthenium(II) hexahydrate (RuBpy)-doped silica nanoparticles was used to generate fluorescent signal for microscopic examination. Prior to the detection, Protein A was immobilized on RuBpy-doped silica nanoparticles with a coverage of ∼5.1×102 molecules/nanoparticle. With this method, Mycobacterium tuberculosis in bacterial mixture as well as in spiked sputum was detected. The use of the fluorescent nanoparticles reveals amplified signal intensity and higher photostability than the direct use of conventional fluorescent dye as label. Our preliminary studies have demonstrated the potential application of the FNP-IIFM method for rapid detection of Mycobacterium tuberculosis in clinical samples

Construction of an M2 macrophage-related prognostic model in hepatocellular carcinoma

BackgroundM2 macrophages play a crucial role in promoting tumor angiogenesis and proliferation, as well as contributing to chemotherapy resistance and metastasis. However, their specific role in the tumor progression of hepatocellular carcinoma (HCC) and their impact on the clinical prognosis remain to be further elucidated.Materials and methodsM2 macrophage-related genes were screened using CIBERSORT and weighted gene co-expression network analysis (WGCNA), while subtype identification was performed using unsupervised clustering. Prognostic models were constructed using univariate analysis/least absolute shrinkage selector operator (LASSO) Cox regression. In addition, Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and mutation analysis were used for further analysis. The relationship between the risk score and tumor mutation burden (TMB), microsatellite instability (MSI), the efficacy of transcatheter arterial chemoembolization (TACE), immunotype, and the molecular subtypes were also investigated. Moreover, the potential role of the risk score was explored using the ESTIMATE and TIDE (tumor immune dysfunction and exclusion) algorithms and stemness indices, such as the mRNA expression-based stemness index (mRNAsi) and the DNA methylation-based index (mDNAsi). In addition, the R package “pRRophetic” was used to examine the correlation between the risk score and the chemotherapeutic response. Finally, the role of TMCC1 in HepG2 cells was investigated using various techniques, including Western blotting, RT-PCR and Transwell and wound healing assays.ResultsThis study identified 158 M2 macrophage-related genes enriched in small molecule catabolic processes and fatty acid metabolic processes in HCC. Two M2 macrophage-related subtypes were found and a four-gene prognostic model was developed, revealing a positive correlation between the risk score and advanced stage/grade. The high-risk group exhibited higher proliferation and invasion capacity, MSI, and degree of stemness. The risk score was identified as a promising prognostic marker for TACE response, and the high-risk subgroup showed higher sensitivity to chemotherapeutic drugs (e.g., sorafenib, doxorubicin, cisplatin, and mitomycin) and immune checkpoint inhibitor (ICI) treatments. The expression levels of four genes related to the macrophage-related risk score were investigated, with SLC2A2 and ECM2 showing low expression and SLC16A11 and TMCC1 exhibiting high expression in HCC. In vitro experiments showed that TMCC1 may enhance the migration ability of HepG2 cells by activating the Wnt signaling pathway.ConclusionWe identified 158 HCC-related M2 macrophage genes and constructed an M2 macrophage-related prognostic model. This study advances the understanding of the role of M2 macrophages in HCC and proposes new prognostic markers and therapeutic targets

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume Reconstruction

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging (cMRI) is crucial for improved cardiovascular disease diagnosis and understanding of the heart's motion. However, current cardiac MRI-based reconstruction technology used in clinical settings is 2D with limited through-plane resolution, resulting in low-quality reconstructed cardiac volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks, we propose a morphology-guided diffusion model for 3D cardiac volume reconstruction, DMCVR, that synthesizes high-resolution 2D images and corresponding 3D reconstructed volumes. Our method outperforms previous approaches by conditioning the cardiac morphology on the generative model, eliminating the time-consuming iterative optimization process of the latent code, and improving generation quality. The learned latent spaces provide global semantics, local cardiac morphology and details of each 2D cMRI slice with highly interpretable value to reconstruct 3D cardiac shape. Our experiments show that DMCVR is highly effective in several aspects, such as 2D generation and 3D reconstruction performance. With DMCVR, we can produce high-resolution 3D cardiac MRI reconstructions, surpassing current techniques. Our proposed framework has great potential for improving the accuracy of cardiac disease diagnosis and treatment planning. Code can be accessed at https://github.com/hexiaoxiao-cs/DMCVR.Comment: Accepted in MICCAI 202