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Background and aims To validate the applicability of the new histological criteria for autoimmune hepatitis (AIH) proposed by the International AIH Pathology Group (IAIH‐PG) among Chinese patients with AIH and drug‐induced liver injury (DILI). Methods The gold standard for diagnosis relied on clinical response: discontinuing treatment without relapse supported DILI, while relapse or ongoing immunosuppressive treatment confirmed AIH. This two‐centre retrospective cohort study included inpatients with DILI or AIH from January 2002 to March 2023. Cases that underwent liver biopsy were selected according to inclusion and exclusion criteria. The diagnostic performance of the criteria was assessed by an area under the receiver operating characteristic curve (AUROC). Results Out of 69 patients: AIH (41, 59%) and DILI (28, 41%). The accuracy, sensitivity and specificity of the new histological criteria for likely and possible AIH were 70%, 98% and 29%, respectively, with an AUROC of 0.8236 [95% confidence interval (CI): 0.7533–0.8938]. For likely AIH, the accuracy, sensitivity and specificity were 73%, 61% and 89%, respectively, with an AUROC of 0.9177 [95% CI: 0.8757–0.9596]. Moreover, for possible AIH, significant differences were found in serum alanine aminotransferase levels [178.4 (87.0, 435.0) versus 536.5 (206.9, 930.4) U/L] and antinuclear antibody (ANA) ≥1:160 [10 (67%) versus 1 (6%)], as well as in lobular lymphoplasmacytic infiltrate [15 (100%) versus 12 (71%)] and more than mild inflammation [13 (87%)versus 6 (35%)] between AIH and DILI (all P values were <0.05). Conclusion The new histological criteria exhibit good diagnostic efficacy in distinguishing AIH from DILI in China, with high AUROC. Key discriminators include low aminotransferase, ANA ≥1:160, lobular lymphoplasmacytic infiltrate and more than mild inflammation, which may further improve diagnostic accuracy for AIH.

AbstractBackgroundAlzheimer’s disease (AD) missing heritability remains extensive despite numerous genetic risk loci identified by genome‐wide association or sequencing studies. This has been attributed, at least partially, to mechanisms not currently investigated by traditional single‐marker/gene approaches. Polygenic Risk Scores (PRS) aggregate sparse genetic information across the genome to identify individual genetic risk profiles for disease prediction and patient risk stratification. Recent advancements have pivoted on innovative approaches utilizing OMICS data to construct such risk scores.MethodWe employed a random forest algorithm to identify a list of gene candidates from bulk RNA sequencing data in prefrontal cortex from 565 AD brain samples (non‐Hispanic Whites, n = 399; Hispanics, n = 113; African American, n = 12) across six U.S. brain banks. Subsequently, we calculated their effect size on Braak staging using regression models to construct a polytranscriptomic risk score (PTRS). We employed two distinct models: “Ethnicity‐Agnostic” Model (randomly assigning samples to training and testing samples) and “Ethnicity‐Aware” Model (assigning NHW samples to training and Hispanics to testing sample). Analysis of variance and the receiver operating characteristics area under the curve (ROC AUC) was used to evaluate PTRS’s classification performances. We validated findings using the Religious Orders Study/Memory and Aging Project study (ROS/MAP, n = 1,095).ResultWe found a significant difference in PTRS between samples with low vs. high Braak stages (≤4 vs. ≥5, p = 1*E‐04; Figure 1 upper panel). AUC was found to be 79‐81%, consistently in both Ethnicity‐Agnostic and Ethnicity‐Aware models (Figure 1 lower panel). Finally, the PTRS in ROS/MAP yielded a similar classification performance (p = 2*E‐04, AUC = 77%).Conclusioncontrary to prior studies, we developed a PTRS with optimal transferability across ethnicities. This underscores the importance of developing novel tools to stratify and harmonize large brain repositories for AD.

AbstractBackgroundAlzheimer’s Disease (AD) presents complex molecular heterogeneity, influenced by a variety of factors including heterogeneous phenotypic, genetic, and neuropathologic presentations. Regulation of gene expression mechanisms is a primary interest of investigations aiming to uncover the underlying disease mechanisms and progression.MethodWe generated bulk RNA‐sequencing in prefrontal cortex from 565 AD brain samples (non‐Hispanic Whites, n = 399; Hispanics, n = 113; African American, n = 12) across six U.S. brain banks, and conducted differential gene expression and enrichment analyses. We sought to identify cross‐ancestry and ancestry‐specific differentially expressed genes (DEG) and pathways across Braak stages, adjusting for sex, age at death, and RNA quality metrics. We validated our findings using the Religious Orders Study/Memory and Aging Project study (ROS/MAP, n = 1,095). Lastly, we validated top DEG using publically‐available human single‐nucleus RNA sequencing (snRNAseq) data.ResultAD‐known genes VGF (LFC = ‐0.661, padj = 3.78) and ADAMTS2 (padj = 1.21) were consistently differentially expressed across statistical models, ethnic groups, and replicated in ROS/MAP (Figure 1). Genes from the heat shock protein (HSP) family, e.g. HSPB7 (padj = 3.78), were the top DEG, also replicated in ROS/MAP. Ethnic‐stratified analyses prioritized TNFSF14 and SPOCD1 as top DEG in Hispanic samples. Gene set enrichment analysis highlighted several significantly pathways, including “TYROBP causal network in microglia” (WP3945; padj = 1.68) and “Alzheimer Disease” (WP5124; padj = 4.24). snRNAseq validated several DEG, including VGF downregulated in neurons (padj = 1.1).ConclusionTo our knowledge, this is the largest diverse transcriptome study for AD in post‐mortem tissue. We identified perturbated genes and pathways resulting in cross‐ethnic and ethnic‐specific findings, ultimately highlighting the importance of diversity in AD investigations.

AbstractBackgroundGenetic variations have emerged as crucial players in the etiology of Alzheimer’s disease (AD), and they serve for a better understanding of the disease mechanisms; yet the specific roles of these genetic variants remain uncertain. Animal models with reminiscent disease pathology could uncover previously uncharacterized roles of these genes. Therefore, we generated zebrafish models for AD variants to analyze the in depth molecular and biological functions of these variants.MethodUsing CRISPR/Cas9, we generated a knockout model for abca7, orthologous to human ABCA7. We performed single cell transcriptomics and analyzed the altered genes and molecular pathways in zebrafish. We leveraged data from multiethnic AD cohorts at Mayo Clinic and Columbia University, to perform genetic association studies, co‐expression analyses, in silico interaction mapping, family based variant segregation analyses and epigenetic association studies, and the functional and histological studies in zebrafish.ResultThe abca7± zebrafish reduced astroglial proliferation, synaptic integrity, and microglial response after Aβ42 toxicity. We found that the abca7 loss‐of‐function (LOF) reduced neuropeptide Y (npy) expression as well as Brain‐derived neurotrophic factor (bdnf) and Nerve growth factor receptor (ngfr). Human brain analysis showed reduced NPY in AD, regulatory interaction between NPY and BDNF, genetic variants in NPY associated with AD, and segregation of variants in ABCA7, BDNF and NGFR in families. ABCA7 variants altered the epigenetic codes in NPY, BDNF, and NGFR promoter regions. Human results paralleled with zebrafish findings to indicate an evolutionarily conserved disease mechanism through ABCA7‐NPY signalling axis. NPY administration to zebrafish rescued the phenotypes in abca7 knockout, suggesting a true biological relevance.ConclusionOur results demonstrate a previously unknown link between ABCA7 and NPY in regulation of synaptic integrity and neurogenesis in AD. We propose that ABCA7‐dependent NPY is a resilience factor in vertebrate brains, and this reserve mechanism is impaired in AD.

Abstract Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. Significance: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.