Unique Signature Research Articles

Hyperspectral Tensor Completion Using Low-Rank Modeling and Convex Functional Analysis.

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor.

Identifying Internet of Things Devices through Unique Digital Signa-tures and Advanced Machine Learning Techniques

Identifying Internet of Things Devices through Unique Digital Signa-tures and Advanced Machine Learning Techniques

Refining molecular subtypes and risk stratification of ovarian cancer through multi-omics consensus portfolio and machine learning.

Ovarian cancer (OC), known for its pronounced heterogeneity, has long evaded a unified classification system despite extensive research efforts. This study integrated five distinct multi-omics datasets from eight multicentric cohorts, applying a combination of ten clustering algorithms and ninety-nine machine learning models. This methodology has enabled us to refine the molecular subtyping of OC, leading to the development of a novel Consensus Machine Learning-driven Signature (CMLS). Our analysis delineated two prognostically significant cancer subtypes (CS), each marked by unique genetic and immunological signatures. Notably, CS1 is associated with an adverse prognosis. Leveraging a subtype classifier, we identified five key genes (CTHRC1, SPEF1, SCGB3A1, FOXJ1, and C1orf194) instrumental in constructing the CMLS. Patients classified within the high CMLS group exhibited a poorer prognosis and were characterized by a "cold tumor" phenotype, indicative of an immunosuppressive microenvironment rich in MDSCs, CAFs, and Tregs. Intriguingly, this group also presented higher levels of tumor mutation burden (TMB) and tumor neoantigen burden (TNB), factors that correlated with a more favorable response to immunotherapy compared to their low CMLS counterparts. In contrast, the low CMLS group, despite also displaying a "cold tumor" phenotype, showed a favorable prognosis and a heightened responsiveness to chemotherapy. This study's findings underscore the potential of targeting immune-suppressive cells, particularly in patients with high CMLS, as a strategic approach to enhance OC prognosis. Furthermore, the redefined molecular subtypes and risk stratification, achieved through sophisticated multi-omics analysis, provide a framework for the selection of therapeutic agents.

Spectroscopic and Theoretical Identifications of Two Structural Motifs of (H2O)10 Cluster.

Precise characterization of archetypal systems of aqueous hydrogen-bonding networks is essential for developing accurate potential functions and universal models of water. The structures of water clusters (H2O)n (n = 2-9) have been verified recently through size-specific infrared spectroscopy with a vacuum ultraviolet free electron laser (VUV-FEL) and quantum chemical studies. For (H2O)10, the pentagonal prism and butterfly motifs were proposed to be important building blocks and were observed in previous experiments. Here we report the size-specific infrared spectra of (H2O)10 via a joint experimental and theoretical study. Well-resolved spectra provide a unique signature for the coexistence of pentagonal prism and butterfly motifs. These (H2O)10 motifs develop from the dominant structures of (H2O)n (n = 8, 9) clusters. This work provides an intriguing prelude to the diverse structure of liquid water and opens avenues for size-dependent measurement of larger systems to understand the stepwise formation mechanism of hydrogen-bonding networks.

Sodium-glucose cotransporter 2 inhibitors influence skeletal muscle pathology in patients with heart failure and reduced ejection fraction.

Patients with heart failure and reduced ejection fraction (HFrEF) exhibit skeletal muscle pathology, which contributes to symptoms and decreased quality of life. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) improve clinical outcomes in HFrEF but their mechanism of action remains poorly understood. We aimed, therefore, to determine whether SGLT2i influence skeletal muscle pathology in patients with HFrEF. Muscle biopsies from 28 male patients with HFrEF (New York Heart association class I-III) treated with SGLT2i (>12 months) or without SGLT2i were compared. Comprehensive analyses of muscle structure (immunohistochemistry), transcriptome (RNA sequencing), and metabolome (liquid chromatography-mass spectrometry) were performed, and serum inflammatory profiling (ELISA). Experiments in mice (n = 16) treated with SGLT2i were also performed. Myofiber atrophy was ~20% less in patients taking SGLT2i (p = 0.07). Transcriptomics and follow-up measures identified a unique signature in patients taking SGLT2i related to beneficial effects on atrophy, metabolism, and inflammation. Metabolomics identified influenced tryptophan metabolism in patients taking SGLT2i: kynurenic acid was 24% higher and kynurenine was 32% lower (p < 0.001). Serum profiling identified that SGLT2i treatment was associated with lower (p < 0.05) pro-inflammatory cytokines by 26-64% alongside downstream muscle interleukin (IL)-6-JAK/STAT3 signalling (p = 008 and 0.09). Serum IL-6 and muscle kynurenine were correlated (R = 0.65; p < 0.05). Muscle pathology was lower in mice treated with SGLT2i indicative of a conserved mammalian response to treatment. Treatment with SGLT2i influenced skeletal muscle pathology in patients with HFrEF and was associated with anti-atrophic, anti-inflammatory, and pro-metabolic effects. These changes may be regulated via IL-6-kynurenine signalling. Together, clinical improvements following SGLT2i treatment in patients with HFrEF may be partly explained by their positive effects on skeletal muscle pathology.

Abstract P25: Identification of biological components and novel clinical subsets of NPM1-mutated AML using an integrated, multi-omics approach

Abstract P25: Identification of biological components and novel clinical subsets of <i>NPM1</i>-mutated AML using an integrated, multi-omics approach

Multi-proteomic analyses of 5xFAD mice reveal new molecular signatures of early-stage Alzheimer's disease.

An early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis.

Abstract PR005: Patients with endometrial cancer and benign gynecologic conditions exhibit unique vaginal and rectal microbiomes

Abstract Endometrial cancer (EC) rates are increasing in the United States, with 66,200 women diagnosed this year. The microbiome may be a key target for understanding EC etiology and progression and hold vital information for prevention strategies. With the largest cross-sectional cohort to date, a rich set of patient-related meta-data, and samples from multiple mucosal sites, we explored the gut-vagina axis in EC compared to benign gynecologic conditions. Vaginal and rectal swabs were collected from 192 patients diagnosed with benign conditions (n=108), hyperplasia (n= 18), and EC (n= 66). 16S rRNA amplicon sequencing was conducted on DNA samples, and data analysis was performed with QIIME 2 and R software. The clinical and demographic analysis identified risk factors associated with EC, such as BMI and menopausal status, that differed between disease groups. Global microbial analyses identified a distinct microbial composition between benign and EC, as well as higher diversity in rectal samples and lower diversity in vaginal samples in benign patients compared to cancer patients. Differential abundance analysis, even after adjustment for known risk factors, revealed unique microbial features such as depletion of Lactobacillus and increase of Anaerococcus, Porphyromonas, and Peptoniphilus in vaginal samples when EC is compared to benign conditions. While Rectal samples indicated a depletion of Peptoniphilus and Prevotella and an increase in Buttiaxella in EC compared to benign conditions. Additionally, community networks were different between cancer patients compared to benign, where taxa in Peptoniphilaceae had the most microbial network connections in EC compared to benign conditions. Notably, putative metabolic pathway analysis identified dysregulation of amino acid metabolism, complex carbohydrate metabolism, and hormone metabolism between cancer patients and benign, which provide key insight into the potential pathophysiological mechanisms employed by the microbiome in EC. This study provides key findings on unique singular and community microbial signatures that may directly affect EC development and be important targets for prevention. Citation Format: Nicole R. Jimenez, Chloe Herman, Pawel Laniewski, Emily K. Cope, Jamal Mourad, Nichole Mahnert, Dana M. Chase, J. G. Caporaso, Melissa M. Herbst-Kralovetz. Patients with endometrial cancer and benign gynecologic conditions exhibit unique vaginal and rectal microbiomes [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr PR005.

EGFR signaling and pharmacology in oncology revealed with innovative BRET-based biosensors

Mutations of receptor tyrosine kinases (RTKs) are associated with the development of many cancers by modifying receptor signaling and contributing to drug resistance in clinical settings. We present enhanced bystander bioluminescence resonance energy transfer-based biosensors providing new insights into RTK biology and pharmacology critical for the development of more effective RTK-targeting drugs. Distinct SH2-specific effector biosensors allow for real-time and spatiotemporal monitoring of signal transduction pathways engaged upon RTK activation. Using EGFR as a model, we demonstrate the capacity of these biosensors to differentiate unique signaling signatures, with EGF and Epiregulin ligands displaying differences in efficacy, potency, and responses within different cellular compartments. We further demonstrate that EGFR single point mutations found in Glioblastoma or non-small cell lung cancer, impact the constitutive activity of EGFR and response to tyrosine kinase inhibitor. The BRET-based biosensors are compatible with microscopy, and more importantly characterize the next generation of therapeutics directed against RTKs.

Abstract PR004: CD73 is a novel repressor of mutant β-catenin oncogenic activity in endometrial cancer

Abstract β-catenin is an important oncogene commonly (20-30%) activated by somatic missense exon 3 CTNNB1 mutations in low grade, early stage endometrioid-type endometrial cancer (EEC). Although exon 3 CTNNB1 mutation is associated with increased risk of recurrence in many (~50%) patients with low grade, early-stage EEC, an equal number of patients with β-catenin mutant tumors will not recur. It remains unclear why patient outcomes are so variable. The variability suggests there are unknown determinants of β-catenin mutant tumor aggressiveness. The purpose of this study was to evaluate CD73, a cell surface 5’-nucleotidase, as a critical factor controlling mutant β-catenin oncogenic activity in EEC. We previously identified CD73 downregulation in exon 3 CTNNB1 mutant EEC predicts recurrence and reported, using a highly homologous Xenopus exon 3 β-catenin mutant, that CD73 restrains mutant β-catenin to the membrane. For this study, we interrogated patient-relevant exon 3 CTNNB1 mutation frequencies in 5 publicly available databases and developed 7 (D32N, S33F, S33Y, G34R, S37C, S37F, and S45F) myc-tagged β-catenin mutant lentiviral vectors for expression in HEC-1-A and Ishikawa cells in which we applied CRISPR-Cas9 deletion or the re-expression of CD73. Reporter assays showed all patient-relevant β-catenin mutants at baseline induce transcriptional activity compared to endogenous levels. With CD73 loss, transcriptional activity for all mutants increased significantly in HEC-1-A cells (normally CD73+/+). With re-expression of CD73 in Ishikawa cells (normally CD73−/−), transcriptional activity of several but not all β-catenin mutants decreased, which provides evidence for the first time that β-catenin mutants are differentially controlled in endometrial cancer. Cell viability assays also demonstrated mutant-specific control by CD73. Using RNA-seq data from TCGA, we assessed whether β-catenin mutants in EECs lacking CD73 exhibited unique tumorigenic transcriptomes. Contrary to current paradigms for exon 3 mutant β-catenin, a dependency on expression of canonical Wnt/β-catenin gene targets was not observed in CD73-deficient β-catenin mutant tumors. In contrast, unique gene signatures (e.g., neuron biology and alcohol metabolic process) that have not previously been linked to β-catenin mutant EEC were identified. Specifically, loss of NAAA and SRD5A3 (encoding N-acylethanolamine-hydrolyzing acid amidase and steroid 5-alpha reductase 3, respectively) were found in β-catenin mutant CD73 low EECs. Loss of these genes are associated with poor outcomes in EEC. Ongoing computational studies of pooled EEC datasets are assessing the unique transcriptomes of individual patient-specific β-catenin mutants in the context of CD73 loss. Our results describe CD73 as a novel determinant controlling mutant β-catenin in EEC. Furthermore, we provide mechanistic rationale to explain the variability in outcomes in endometrial cancer patients with exon 3 β-catenin mutant tumors. Citation Format: Rebecca M. Hirsch, Xingyuan Zhang, Lilly F. Chiou, Sunthoshini Premsankar, Hannah N. Lee, Katherine C. Kurnit, Russell R. Broaddus, Cyrus Vaziri, Jessica L. Bowser. CD73 is a novel repressor of mutant β-catenin oncogenic activity in endometrial cancer [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr PR004.

Assigning the unassigned: A signature-based classification of rDNA metabarcodes reveals new deep-sea diversity.

Environmental DNA metabarcoding reveals a vast genetic diversity of marine eukaryotes. Yet, most of the metabarcoding data remain unassigned due to the paucity of reference databases. This is particularly true for the deep-sea meiofauna and eukaryotic microbiota, whose hidden diversity is largely unexplored. Here, we tackle this issue by using unique DNA signatures to classify unknown metabarcodes assigned to deep-sea foraminifera. We analyzed metabarcoding data obtained from 311 deep-sea sediment samples collected in the Clarion-Clipperton Fracture Zone, an area of potential polymetallic nodule exploitation in the Eastern Pacific Ocean. Using the signatures designed in the 37F hypervariable region of the 18S rRNA gene, we were able to classify 802 unassigned metabarcodes into 61 novel lineages, which have been placed in 27 phylogenetic clades. The comparison of new lineages with other foraminiferal datasets shows that most novel lineages are widely distributed in the deep sea. Five lineages are also present in the shallow-water datasets; however, phylogenetic analysis of these lineages separates deep-sea and shallow-water metabarcodes except in one case. While the signature-based classification does not solve the problem of gaps in reference databases, this taxonomy-free approach provides insight into the distribution and ecology of deep-sea species represented by unassigned metabarcodes, which could be useful in future applications of metabarcoding for environmental monitoring.

Transformer Core Fault Diagnosis via Current Signal Analysis with Pearson Correlation Feature Selection

The role of transformers in power distribution is crucial, as their reliable operation is essential for maintaining the electrical grid’s stability. Single-phase transformers are highly versatile, making them suitable for various applications requiring precise voltage control and isolation. In this study, we investigated the fault diagnosis of a 1 kVA single-phase transformer core subjected to induced faults. Our diagnostic approach involved using a combination of advanced signal processing techniques, such as the fast Fourier transform (FFT) and Hilbert transform (HT), to analyze the current signals. Our analysis aimed to differentiate and characterize the unique signatures associated with each fault type, utilizing statistical feature selection based on the Pearson correlation and a machine learning classifier. Our results showed significant improvements in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, with 83.89% accuracy and a computational cost of 0.2963 s. For future studies, our focus will be on using deep learning models to improve the effectiveness of the proposed method.

Spatiotemporal Insights into Glioma Oncostream Dynamics: Unraveling Formation, Stability, and Disassembly Pathways.

Glioblastoma (GBM) remains a challenge in Neuro-oncology, with a poor prognosis showing only a 5% survival rate beyond two years. This is primarily due to its aggressiveness and intra-tumoral heterogeneity, which limits complete surgical resection and reduces the efficacy of existing treatments. The existence of oncostreams-neuropathological structures comprising aligned spindle-like cells from both tumor and non-tumor origins- is discovered earlier. Oncostreams are closely linked to glioma aggressiveness and facilitate the spread into adjacent healthy brain tissue. A unique molecular signature intrinsic to oncostreams, with overexpression of key genes (i.e., COL1A1, ACTA2) that drive the tumor's mesenchymal transition and malignancy is also identified. Pre-clinical studies on genetically engineered mouse models demonstrated that COL1A1 inhibition disrupts oncostreams, modifies TME, reduces mesenchymal gene expression, and extends survival. An in vitro model using GFP+ NPA cells to investigate how various treatments affect oncostream dynamics is developed. Analysis showed that factors such as cell density, morphology, neurotransmitter agonists, calcium chelators, and cytoskeleton-targeting drugs influence oncostream formation. This data illuminate the patterns of glioma migration and suggest anti-invasion strategies that can improve GBM patient outcomes when combined with traditional therapies. This work highlights the potential of targeting oncostreams to control glioma invasion and enhance treatment efficacy.

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis.

Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited. To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK (natural killer) cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL-1B (interleukin-1B) or C1Q expression, 2 TREM2-positive foam cells (1 expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing cellular indexing of transcriptomes and epitopes by sequencing data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic versus asymptomatic patients. Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types and the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.

Can the Gravitational Wave Background Feel Wiggles in Spacetime?

Recently the international pulsar timing array collaboration has announced the first strong evidence for an isotropic gravitational-wave background (GWB). We propose that rapid small oscillations (wiggles) in the Hubble parameter would trigger a resonance with the propagating gravitational waves, leaving unique signatures in the GWB spectrum as sharp resonance peaks/troughs. The proposed signal can appear at all frequency ranges and is common to GWBs with arbitrary origin. The resonant signal can appear as a trough only when the GWB is primordial, and its amplitude will also be larger by one perturbation order than in the nonprimordial case. These properties serve as a smoking gun for the primordial origin of the observed GWB. We showcased the viability of the signal to near future observations using the recent NANOGrav 15 yr data.

Parkinson's disease-derived α-synuclein assemblies combined with chronic-type inflammatory cues promote a neurotoxic microglial phenotype.

Parkinson's disease (PD) is a common age-related neurodegenerative disorder characterized by the aggregation of α-Synuclein (αSYN) building up intraneuronal inclusions termed Lewy pathology. Mounting evidence suggests that neuron-released αSYN aggregates could be central to microglial activation, which in turn mounts and orchestrates neuroinflammatory processes potentially harmful to neurons. Therefore, understanding the mechanisms that drive microglial cell activation, polarization and function in PD might have important therapeutic implications. Here, using primary microglia, we investigated the inflammatory potential of pure αSYN fibrils derived from PD patients. We further explored and characterized microglial cell responses to a chronic-type inflammatory stimulation combining PD patient-derived αSYN fibrils (FPD), Tumor necrosis factor-α (TNFα) and prostaglandin E2 (PGE2) (TPFPD). We showed that FPD hold stronger inflammatory potency than pure αSYN fibrils generated de novo. When combined with TNFα and PGE2, FPD polarizes microglia toward a particular functional phenotype departing from FPD-treated cells and featuring lower inflammatory cytokine and higher glutamate release. Whereas metabolomic studies showed that TPFPD-exposed microglia were closely related to classically activated M1 proinflammatory cells, notably with similar tricarboxylic acid cycle disruption, transcriptomic analysis revealed that TPFPD-activated microglia assume a unique molecular signature highlighting upregulation of genes involved in glutathione and iron metabolisms. In particular, TPFPD-specific upregulation of Slc7a11 (which encodes the cystine-glutamate antiporter xCT) was consistent with the increased glutamate response and cytotoxic activity of these cells toward midbrain dopaminergic neurons in vitro. Together, these data further extend the structure-pathological relationship of αSYN fibrillar polymorphs to their innate immune properties and demonstrate that PD-derived αSYN fibrils, TNFα and PGE2 act in concert to drive microglial cell activation toward a specific and highly neurotoxic chronic-type inflammatory phenotype characterized by robust glutamate release and iron retention.

Discovery of plasma biomarkers for Parkinson's disease diagnoses based on metabolomics and lipidomics

Parkinson's disease (PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates. The current gold standard for diagnosing PD is clinical evaluation, which is often challenging and inaccurate. Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics. Here, we used comprehensive untargeted metabolomics and lipidomics methodologies based on liquid chromatography-mass spectrometry to evaluate metabolic abnormalities linked with PD. Two well-characterized cohorts of 288 plasma samples (143 PD patients and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers. Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures, based on which the discriminant models for PD were established by integrating multiple machine learning algorithms. A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort, and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6% both in the discovery cohort and validation cohort. The results indicated that PC (40:7), eicosatrienoic acid were negatively correlated with severity of PD, and pentalenic acid, PC (40:6p) and aspartic acid were positively correlated with severity of PD. In summary, we developed a multi-metabolite predictive model which can diagnose PD with over 81.6% accuracy based on this unique metabolic signature. Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.

Transcriptomic evaluation of tau and TDP-43 synergism shows tauopathy predominance and reveals potential modulating targets

Alzheimer's disease (AD), the most common aging-associated neurodegenerative dementia disorder, is defined by the presence of amyloid beta (Aβ) and tau aggregates in the brain. However, more than half of patients also exhibit aggregates of the protein TDP-43 as a secondary pathology. The presence of TDP-43 pathology in AD is associated with increased tau neuropathology and worsened clinical outcomes in AD patients. Using C. elegans models of mixed pathology in AD, we have previously shown that TDP-43 specifically synergizes with tau but not Aβ, resulting in enhanced neuronal dysfunction, selective neurodegeneration, and increased accumulation of pathological tau. However, cellular responses to co-morbid tau and TDP-43 preceding neurodegeneration have not been characterized. In this study, we evaluate transcriptomic changes at time-points preceding frank neuronal loss using a C. elegans model of tau and TDP-43 co-expression (tau-TDP-43 Tg). We find significant differential expression and exon usage in genes enriched in multiple pathways including lipid metabolism and lysosomal degradation. We note that early changes in tau-TDP-43 Tg resemble changes with tau alone, but a unique expression signature emerges during aging. We test loss-of-function mutations in a subset of tau and TDP-43 responsive genes, identifying new modifiers of neurotoxicity. Characterizing early cellular responses to tau and TDP-43 co-pathology is critical for understanding protective and pathogenic responses to mixed proteinopathies, and an important step in developing therapeutic strategies protecting against pathological tau and TDP-43 in AD.

Lymphocytic colitis can be transcriptionally divided into channelopathic and inflammatory lymphocytic colitis.

The pathobiology of the non-destructive inflammatory bowel disease (IBD) lymphocytic colitis (LC) is poorly understood. We aimed to define an LC-specific mucosal transcriptome to gain insight into LC pathology, identify unique genomic signatures, and uncover potentially druggable disease pathways. We performed bulk RNA-sequencing of LC and collagenous colitis (CC) colonic mucosa from patients with active disease, and healthy controls (n=4-10 per cohort). Differential gene expression was analyzed by gene-set enrichment and deconvolution analyses to identify pathologically relevant pathways and cells, respectively, altered in LC. Key findings were validated using reverse transcription quantitative PCR and/or immunohistochemistry. Finally, we compared our data with a previous cohort of ulcerative colitis and Crohn's disease patients (n=4 per group) to distinguish non-destructive from classic IBD. LC can be subdivided into channelopathic LC, which is governed by organic acid and ion transport dysregulation, and inflammatory LC, which is driven by microbial immune responses. Inflammatory LC displays an innate and adaptive immunity that is limited compared to CC and classic IBD. Conversely, we noted a distinct induction of regulatory non-coding RNA species in inflammatory LC samples. Moreover, compared with CC, water channel and cell adhesion molecule gene expression decreased in channelopathic LC, whereas it was accentuated in inflammatory LC and associated with reduced intestinal epithelial cell proliferation. We conclude that LC can be subdivided into channelopathic LC and inflammatory LC that could be pathomechanistically distinct subtypes despite their shared clinical presentation. Inflammatory LC exhibits a dampened immune response compared to CC and classic IBDs. Our results point to regulatory micro-RNAs as a potential disease-specific feature that may be amenable to therapeutic intervention.

Crohn's Patient Serum Proteomics Reveals Response Signature for Infliximab but not Vedolizumab.

Crohn's disease is a chronic inflammatory bowel disease that affects the gastrointestinal tract. Common biologic families used to treat Crohn's are tumor necrosis factor (TNF)-α blockers (infliximab and adalimumab) and immune cell adhesion blockers (vedolizumab). Given their differing mechanisms of action, the ability to monitor response and predict treatment efficacy via easy-to-obtain blood draws remains an unmet need. To investigate these gaps in knowledge, we leveraged 2 prospective cohorts (LOVE-CD, TAILORIX) and profiled their serum using high-dimensional isobaric-labeled proteomics before treatment and 6 weeks after treatment initiation with either vedolizumab or infliximab. The proportion of patients endoscopically responding to treatment was comparable among infliximab and vedolizumab cohorts; however, the impact of vedolizumab on patient sera was negligible. In contrast, infliximab treatment induced a robust response including increased blood-gas regulatory response proteins, and concomitant decreases in inflammation-related proteins. Further analysis comparing infliximab responders and nonresponders revealed a lingering innate immune enrichments in nonresponders and a unique protease regulation signature related to clotting cascades in responders. Lastly, using samples prior to infliximab treatment, we highlight serum protein biomarkers that potentially predict a positive response to infliximab treatment. These results will positively impact the determination of appropriate patient treatment and inform the selection of clinical trial outcome metrics.