Cell-type-specific transcriptome architecture underlying the establishment and exacerbation of systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease involving multiple immune cells. A major hurdle to the elucidation of SLE pathogenesis is our limited understanding of dysregulated gene expression linked to various clinical statuses with a high cellular resolution. Here, we conducted a large-scale transcriptome study with 6,386 RNA sequencing data covering 27 immune cell types from 159 SLE and 89 healthy donors. We first profiled two distinct cell-type-specific transcriptomic signatures: disease-state and disease-activity signatures, reflecting disease establishment and exacerbation, respectively. We next identified candidate biological processes unique to each signature. This study suggested the clinical value of disease-activity signatures, which were associated with organ involvement and responses to therapeutic agents such as belimumab. However, disease-activity signatures were less enriched around SLE risk variants than disease-state signatures, suggesting that the genetic studies to date may not well capture clinically vital biology in SLE. Together, we identified comprehensive gene signatures of SLE, which will provide essential foundations for future genomic, genetic, and clinical studies.

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

AN ATTEMPT TO UNDERSTAND THE CLINICAL APPROACH OF ADRAVYABHUTA CHIKITSA

Chikitsa (Treatment protocols) is Pravrutti (effort) made by Gunayukta Chikitsa Chatuspada (Physician, nurse, medications and patient) to bring back Dhatu Samyata (Healthy state) whenever there is Dhatu Vaikruti (Disease state). In Ayurvedic text, we got an explanation of many types of Chikitsa for different Vyadhis (Disease) or in different Avastha (Stages) of the Vyadhi. Among all these Chikitsa, we use Dravyabhuta Chikitsa (Dimensional things) like all medication including shodhana (Purification) and shamana (Pacification) broadly but the use of the Adravyabhuta Chikitsa (Non-dimensional) is very limited. Adravyabhuta is a type of Chikitsa that is classified based on the swarupa (Shape), these Adravyabhutas Chikitsa specially deals with non - dimensional things. This review article is explaining about concept and types of Adravyabhuta Chikitsa with classical examples which our Acharyas explained in Samhitas. Keywords: Chikitsa, Dravyabhuta, Adravyabhuta, Murta Bhava, Amurtha Bhava, Samavayi Karana.

TMOD-15. CHARACTERIZATION OF THE MINIMAL RESIDUAL DISEASE STATE REVEALS DISTINCT EVOLUTIONARY TRAJECTORIES OF HUMAN GLIOBLASTOMA

Recurrence of solid tumors renders patients vulnerable to a distinctly advanced, highly treatment-refractory disease state that has an increased mutational burden and novel oncogenic drivers not detected at initial diagnosis. Improving outcomes for recurrent cancers requires a better understanding of cancer cell populations that expand from the post-therapy, minimal residual disease (MRD) state. We profiled barcoded tumor cell populations through therapy at tumor initiation/engraftment, MRD and recurrence in our therapy-adapted, patient-derived xenograft models of glioblastoma (GBM). Tumors showed distinct patterns of recurrence in which clonal populations exhibited either a priori, pre-existing fitness, or equipotent fitness acquired through therapy. Characterization of the MRD state by single-cell and bulk RNA sequencing revealed a tumor-intrinsic immunomodulatory signature with strong prognostic significance at the transcriptomic level and in proteomic analysis of cerebrospinal fluid (CSF) collected from GBM patients at all stages of disease. Our results provide insight into the innate and therapy-driven dynamics of human glioblastoma, and the prognostic value of the interrogating of the MRD state in solid cancers.

Differential Vulnerability of Anterior Cingulate Cortex Cell-Types to Diseases and Drugs

In psychiatric disorders, mismatches between disease-states and therapeutic strategies are highly pronounced, largely because of unanswered questions regarding specific vulnerabilities of different cell-types and therapeutic responses. Which cellular events (housekeeping or salient) are most affected? Which cell-types succumb first to challenges, and which exhibit the strongest response to drugs? Are these events coordinated between cell-types? How does the disease-state and drug affect this coordination? To address these questions, we analyzed single-nucleus-RNAseq (sn-RNAseq) data from the human anterior cingulate cortex- a region involved in many psychiatric disorders. Density index, a metric for quantifying similarities and dissimilarities across functional profiles, was employed to identify common (housekeeping) or salient functional themes across all cell-types. Cell-specific signatures were integrated with existing disease and drug-specific signatures to determine cell-type-specific vulnerabilities, druggabilities, and responsiveness. Clustering of functional profiles revealed cell-types jointly participating in these events. SST and VIP interneurons were found to be most vulnerable, whereas pyramidal neurons were least vulnerable. Overall, the disease-state is superficial layer-centric, largely influences cell-specific salient themes, strongly impacts disinhibitory neurons, and influences astrocyte interaction with a subset of deep-layer pyramidal neurons. Drug activities, on the other hand, are deep layer-centric and involve activating a distinct subset of deep-layer pyramidal neurons to circumvent the disinhibitory circuit malfunctioning in the disease-state. These findings demonstrate a novel application of sn-RNAseq data to explain drug and disease action at a systems level, suggests a targeted drug development and reevaluate various postmortem-based findings.

The Pathogenic R5L Mutation Disrupts Formation of Tau Complexes on the Microtubule by Altering Local N-Terminal Structure

The microtubule-associated protein (MAP) Tau is an intrinsically disordered protein (IDP) primarily expressed in axons, where it functions to regulate microtubule dynamics, modulate motor protein motility, and participate in signaling cascades. Tau misregulation and point mutations are linked to neurodegenerative diseases, including Progressive Supranuclear Palsy (PSP), Pick's Disease and Alzheimer's disease. Many disease-associated mutations in Tau occur in the C-terminal microtubule-binding domain of the protein. Effects of C-terminal mutations in Tau have led to the widely accepted disease-state theory that missense mutations in Tau reduce microtubule-binding affinity or increase Tau propensity to aggregate. Here, we investigate the effect of an N-terminal disease-associated mutation in Tau, R5L, on Tau-microtubule interactions using an in vitro reconstituted system. Contrary to the canonical disease-state theory, we determine the R5L mutation does not reduce Tau affinity for the microtubule using Total Internal Reflection Fluorescence (TIRF) Microscopy. Rather, the R5L mutation decreases the ability of Tau to form larger order complexes, or Tau patches, at high concentrations of Tau. Using Nuclear Magnetic Resonance (NMR), we show that the R5L mutation results in a local structural change that reduces interactions of the projection domain in the presence of microtubules. Altogether, these results challenge both the current paradigm of how mutations in Tau lead to disease and the role of the projection domain in modulating Tau behavior on the microtubule surface.

Concurrent cholecystoduodenal fistula and primary aortoenteric fistula

ABSTRACT Bilioenteric fistulae are a rare complication and can pose a diagnostic challenge owing to non-specific symptomology. When occurring with an aortoenteric fistula, it represents a rare and potentially life-threatening disease state. We present the case of a 77-year-old gentleman initially treated as presumed ascending cholangitis. This was complicated by upper gastrointestinal bleeding secondary to an aortoenteric fistula and cholecystoduodenal fistula.