Viral infections leading to inflammation have been implicated in several common diseases, such as Alzheimer’s disease (AD) and type 1 diabetes (T1D). Of note, herpes simplex virus 1 (HSV-1) has been reported to be associated with AD. We sought to identify the transcriptomic changes due to HSV-1 infection and anti-viral drug (acyclovir, ACV) treatment of HSV-1 infection in dissociated cells from human cerebral organoids (dcOrgs) versus stem cell-derived pancreatic islets (sc-islets) to gain potential biological insights into the relevance of HSV-1-induced inflammation in AD and T1D. We observed that differentially expressed genes (DEGs) in HSV-1-infected sc-islets were enriched for genes associated with several autoimmune diseases, most significantly, T1D, but also rheumatoid arthritis, psoriasis, Crohn’s disease, and multiple sclerosis, whereas DEGs in HSV-1-infected dcOrgs were exclusively enriched for genes associated with AD. The ACV treatment of sc-islets was not as effective in rescuing transcript perturbations of autoimmune disease-associated genes. Finally, we identified gene ontology categories that were enriched for DEGs that were in common across, or unique to, viral treatment of dcOrgs and sc-islets, such as categories involved in the transferase complex, mitochondrial, and autophagy function. In addition, we compared transcriptomic signatures from HSV-1-infected sc-islets with sc-islets that were infected with the coxsackie B virus (CVB) that had been associated with T1D pathogenesis. Collectively, this study provides tissue-specific insights into the molecular effects of inflammation in AD and T1D.

Viral infections leading to inflammation have been implicated in several common diseases such as Alzheimer’s disease (AD) and type 1 diabetes (T1D). Of note, herpes simplex virus 1 (HSV-1) has been reported to be associated with AD. We sought to identify the transcriptomic changes due to HSV-1 infection and anti-viral drug (acyclovir, ACV) treatment of HSV-1 infection in dissoci-ated cells from human cerebral organoids (dcOrgs) versus stem cell-derived pancreatic islets (sc-islets) to gain potential biological insights into the relevance of HSV-1-induced inflammation in AD and T1D. We observed that differentially expressed genes (DEGs) in HSV-1-infected sc-islets were enriched for genes associated with several autoimmune diseases, most significantly T1D but also rheumatoid arthritis, psoriasis, Crohn’s disease, and multiple sclerosis, whereas DEGs in HSV-1-infected dcOrgs were exclusively enriched for genes associated with AD. ACV treatment of sc-islets did not rescue transcript expression of autoimmune disease-associated genes. Finally, we identified gene ontology categories that were enriched for DEGs that were in common across, or unique to, viral treatment of dcOrgs and sc-islets, such as categories involved in the transferase complex, mitochondrial and autophagy function. Collectively, this studies pro-vide insight to the molecular effects of inflammation in AD and T1D.

As a diverse family of diseases, cancer is unified by a set of common dysfunctions, such as limitless growth potential and an insensitivity to antigrowth signals. These shared overarching biological processes have been termed the hallmarks of cancer. To better understand the root cause of cellular dysregulation, intense molecular characterization of tumors has utilized DNA, RNA, and protein measurement techniques to produce proteogenomic data. In large cancer cohort studies, genomic and proteogenomic data have frequently identified many cancer hallmarks including cell cycle and cell signaling. However, altered metabolism, a known cancer hallmark, is not as clearly identified in mutation screens or differential expression analyses. Here, we introduce a new computational method to identify changes in cellular regulation by focusing on the mRNA/protein relationship. We create a metric, Δ_corr, to capture when the mRNA/protein correlation changes significantly between tumor and normal tissues and show that it is distinct from differential expression and also not associated with DNA mutation profiles. Our method clearly highlights altered metabolic pathways across multiple tumor types. Δ_corr gives researchers a new perspective on the dysfunction of tumor cells and introduces a novel method for proteogenomic data integration.

Background: Alzheimer's disease (AD) is characterized by complex immune interactions, yet the role of antibody‐mediated responses remains poorly understood. Recent advances in high‐throughput epitope screening enable the identification of disease‐associated epitopes that may play a role in AD pathogenesis. We previously developed a computational pipeline to detect disease‐related epitopes from high‐throughput epitope profiling data, and extend this approach to AD to identify potential antibody responses to pathogenic or autoantigenic epitopes in cerebrospinal fluid (CSF) samples. Method: We performed IgG epitope profiling on CSF samples from 625 individuals, including 359 AD patients, 97 mild cognitive impairment (MCI) controls, and 158 cognitively normal controls. The remaining samples did not fit these categories and were excluded from subgroup analyses. CSF samples were screened using Serimmune's Serum Epitope Repertoire Analysis (SERA) platform, which profiles IgG binding to a random 12‐mer bacterial display library. Sequencing of enriched 12‐mers produced approximately 3 million reads per sample. K‐mers (k=5,6) were extracted, and enrichment scores were calculated based on expected amino acid distributions. Identified k‐mers were mapped to human and viral proteomes using protein tiling and permutation analysis to assess statistical enrichment. Epitopes were identified via peak calling, clustered using complete‐linkage clustering, and scored for disease association using an outlier sum statistic. Multiple hypothesis correction was performed using the Benjamini‐Hochberg method. Result: Comparison of AD patients to cognitively normal controls revealed 12 distinct epitopes from 12 viral proteins, spanning multiple viral families (Table 1). These epitopes originated from 9 viruses, with a notable overrepresentation of herpesviruses (HHV8P, HHV6U, EBVB9, HCMVM). Additionally, we identified 42 CNS‐expressed autoantigens associated with AD (Table 2). These included ion channels (SCN1A_HUMAN, SCN3A_HUMAN, ASIC4_HUMAN), neurotransmitter receptors (NTRK2_HUMAN, GRIA1_HUMAN), synaptic and neurodevelopmental proteins (SV2B_HUMAN, ASTN1_HUMAN), and immune‐regulatory proteins (PD1L1_HUMAN, P2Y10_HUMAN), suggesting a potential autoimmune component in AD pathology. Conclusion: Our computational approach successfully identifies putative AD‐associated epitopes, with a significant overrepresentation of herpesvirus‐derived epitopes and CNS autoantigens in AD cases. These findings support a growing body of evidence implicating viral infections and potential autoimmune mechanisms in AD pathology. Further validation and mechanistic studies are warranted to explore the potential role of these epitopes in AD progression.