chore: pull latest news + research spotlight articles from reactome.org verbatim

Three articles existed on production reactome.org but were missing from
the migrated content tree:

- about/news/291-v96-released
- content/reactome-research-spotlight/290-patient-stratification-...
- content/reactome-research-spotlight/292-a-workflow-for-human-health-hazard-evaluation-...

Pulled via the joomla -> MDX pattern used elsewhere in the repo: extract
<h2 itemprop="name"> as title, <time datetime="..."> as date, and
<div itemprop="articleBody"> as body content. Body is run through
html2text with link/image hrefs preserved verbatim (relative paths
wrapped in <...> so MDX doesn't misparse # fragments). The hero image
referenced by the v96 article (/images/R-HSA-9975921_medres.png) is
copied into public/images so beta serves it same-origin.

Author: adamjohnwright

projects/website-angular/content/about/news/291-v96-released.mdx

@@ -0,0 +1,37 @@
+---
+title: "V96 Released"
+category: "about"
+date: "2026-04-01T20:56:55-04:00"
+tags: ["about", "news", "291-v96-released"]
+---
+
+## V96 Released
+
+![R HSA 9975921 medres](</images/R-HSA-9975921_medres.png>)
+
+[Assembly of the 9+0 primary cilium](</PathwayBrowser/#9975921>)
+
+New and Updated Topics and Pathways. Topics with new or revised pathways in this release include Cell-cell communication ([Activation of STAT3 by cadherin engagement](</PathwayBrowser/#9958825>)), Chromatin organization ([CHD chromatin remodelers](</PathwayBrowser/#9937848>)), Developmental biology ([Differentiation of naive CD4+ T cells to T helper 2 cells (Th2 cells)](</PathwayBrowser/#9976102>)), Disease ([Defects of coagulation cascade](</PathwayBrowser/#9769726>)) and [Defects of contact activation system](</PathwayBrowser/#9946127>)), DNA repair ([HDR through Single Strand Annealing (SSA)](</PathwayBrowser/#5685938>)), Gene expression ([PRC2 methylates histones and DNA](</PathwayBrowser/#212300>)), Hemostasis ([Coagulation pathway](</PathwayBrowser/#9769740>)), Immune system ([FXII activates plasma kallikrein-kinin system](</PathwayBrowser/#9970672>) and [FXIIa, PKa-dependent activation of coagulation pathway](</PathwayBrowser/#9935598>), [Regulation of Complement cascade](</PathwayBrowser/#977606>)), Metabolism ([choline metabolism](</PathwayBrowser/#6798163>), [Inositol phosphate metabolism](</PathwayBrowser/#1483249>) and [Thyroxine metabolism and iodide transport](</PathwayBrowser/#209968>)), Muscle Contraction ([Cardiac Conduction](</PathwayBrowser/#5576891>)), Signal Transduction ([Formation of the beta-catenin:TCF transactivating complex](</PathwayBrowser/#201722>) and [MTOR signalling](</PathwayBrowser/#165159>)), and Transport of small molecules ([Organic anion transport by SLC22 transporters](</PathwayBrowser/#561048>)).
+
+New and Updated Illustrations. New or revised illustrations with embedded navigation features have been created for [Cilium assembly](</PathwayBrowser/#5617833>), [Assembly of the 9+0 primary cilium](</PathwayBrowser/#9975921>), [ATP-dependent chromatin remodelers](</PathwayBrowser/#9932444>), [Defects of coagulation cascade](</PathwayBrowser/#9769726>), [Defects of contact activation system and kallikrein-kinin system](</PathwayBrowser/#9946127>), [Differentiation of T cells](</PathwayBrowser/#9945266>), [Diseases of hemostasis](</PathwayBrowser/#9671793>), [Diseases of immune system](</PathwayBrowser/#5260271>), [Hemostasis](</PathwayBrowser/#109582>), and [Innate immune system](</PathwayBrowser/#168249>).
+
+Thanks to our Contributors. [Hanad Adan](<https://orcid.org/0000-0002-9476-134X>), [Juliet Daniel](<https://orcid.org/0000-0002-3497-0772>), [Anthony J Gesino](<https://orcid.org/0009-0000-7729-3373>), [Leda Raptis](<https://orcid.org/0000-0002-6517-4429>), and [Arielle Vaglio](<https://orcid.org/0009-0008-5095-5936>) are our external authors and [Isabella Barbutti](<https://orcid.org/0000-0001-5478-9834>), [David P Hill](<https://orcid.org/0000-0001-7476-6306>), [Lin Huang](<https://orcid.org/0000-0001-8254-7664>), [Graham M Lord](<https://orcid.org/0000-0003-2069-4743>), are [Alvin H Schmaier](<https://orcid.org/0000-0002-3884-6234>) our external reviewers.
+
+Annotation Statistics. Reactome comprises 16,338 human reactions organized into 2,870 pathways involving 32,399 proteins and modified forms of proteins encoded by 11,452 different human genes, 16,145 complexes, 2,198 small molecules, and 1,102 drugs. These annotations are supported by 42,784 literature references. We have projected these reactions onto 83,074 orthologous proteins, creating 20,616 orthologous pathways in 14 non-human species. Version 96 has annotations for 5752 protein variants (mutated proteins) and their post-translationally modified forms, derived from 400 proteins, which have contributed to the annotation of 2056 disease-specific reactions and 788 pathways. Our Illustration library now includes over 2,500 icons and over 200 high level interactive pathway Illustrations.
+
+Other news: 
+
+Tools and Data. Our services and software tools are designed for biologists, bioinformaticians, and software developers. Pathway data is available to view in our [Pathway Browser](<https://reactome.org/PathwayBrowser/>), to [analyze](<https://reactome.org/PathwayBrowser/#TOOL=AT>) your own dataset, to [download](<https://reactome.org/download-data>), and access programmatically through our [Content](<https://reactome.org/ContentService/>) and [Analysis](<https://reactome.org/AnalysisService/>) Services. The [Reactome FIViz](<https://reactome.org/userguide/reactome-fiviz>) app and [ReactomeGSA](<https://reactome.org/gsa>) package provide tools for multi-omics data analysis. The [idg.reactome.org](<https://idg.reactome.org/>) Web Portal provides a collection of web-based tools to help researchers place understudied proteins in a pathway context. 
+
+Reactome is excited to announce the beta release of our [redesigned Pathway Browser](<https://reactome.org/beta/PathwayBrowser>). This major update delivers a modernized user interface (UI) and user experience (UX), along with new visualization and analysis features. Please provide us with your [feedback](<https://forms.gle/TPBxaWnnVLLZj66p8>).
+
+Documentation and Training. Visit our online [User Guide](<https://reactome.org/userguide>) to access documentation supporting pathway analysis of experimental data. The [Developer's Zone](<https://reactome.org/dev>) provides detailed documentation regarding our software, tools, and web services. Training and learning materials can be found [here](<https://reactome.org/community>).
+
+About the Reactome Project. Reactome is a collaboration between groups at the Ontario Institute for Cancer Research, Oregon Health and Science University, New York University Langone Medical Center, and The EMBL - European Bioinformatics Institute. Reactome is both an [ELIXIR Core Data Resource](<https://elixir-europe.org/platforms/data/core-data-resources>) and a [Global Core Biodata Resource](<https://globalbiodata.org/scientific-activities/global-core-biodata-resources/>) and has been certified as a Trustworthy Data Repository by the [CoreTrustSeal](<https://www.coretrustseal.org/>) Standards and Certification Board.
+
+Reactome annotation files and interaction data derived from Reactome are distributed under a Creative Commons Public Domain (CC0 1.0 Universal) Licence. A Creative Commons Attribution 4.0 International (CC BY 4.0) License applies to all software and code, database data dumps, and Pathway Illustrations (Enhanced High-Level Diagrams), Icon Library, Art, and Branding Materials. A full description of the new and updated content is available on the Reactome [website](<https://reactome.org/what-is-reactome>).
+
+Follow us on [email protected] get frequent updates about new and updated pathways, feature updates, and more!
+
+For more information: If you have a question, want to provide feedback, or are interested in collaborating with us to annotate a topic, please contact us at [[email protected].](<mailto:help@reactome.org.>)
+

projects/website-angular/content/content/reactome-research-spotlight/290-patient-stratification-reveals-the-molecular-basis-of-disease-co-occurrences.mdx

@@ -0,0 +1,17 @@
+---
+title: "Patient stratification reveals the molecular basis of disease co-occurrences"
+category: "content"
+date: "2026-03-16T03:00:02-04:00"
+tags: ["content", "reactome-research-spotlight", "290-patient-stratification-reveals-the-molecular-basis-of-disease-co-occurrences"]
+---
+
+## Patient stratification reveals the molecular basis of disease co-occurrences
+
+Urda-García et al. present a transcriptomics-based analysis of disease co-occurrence using RNA-seq data from 45 human diseases. [This study ](<https://www.pnas.org/doi/pdf/10.1073/pnas.2421060122>)evaluates whether similarities in gene expression profiles can explain known epidemiological comorbidities more effectively than prior network-based approaches.
+
+The authors derived disease-level expression signatures and used these to construct a Disease Similarity Network, in which statistically significant correlations indicate shared molecular patterns. This network reproduced a substantial fraction of known disease co-occurrences. To address disease heterogeneity, patients were further grouped into expression-defined subtypes (“meta-patients”), which were incorporated into a Stratified Similarity Network. This stratified model increased recall of epidemiological associations to 64% and revealed subtype-specific relationships that were not detectable at the disease level, including associations restricted to specific breast cancer subtypes.
+
+Pathway-level analysis using Reactome showed that diseases linked by epidemiological co-occurrence shared significantly more dysregulated pathways than unrelated disease pairs. [Immune system pathways](<https://reactome.org/PathwayBrowser/#/R-HSA-168256>) were the most consistently shared features, with over 95% of epidemiologically linked disease pairs exhibiting common immune pathway upregulation.
+
+The study demonstrates that patient stratification improves the detection of molecular similarities underlying disease co-occurrence and provides a reproducible framework, supported by a public web resource, for exploring subtype-resolved comorbidity at the transcriptomic level.
+

projects/website-angular/content/content/reactome-research-spotlight/292-a-workflow-for-human-health-hazard-evaluation-using-transcriptomic-data-and-key-characteristics-based-gene-sets.mdx

@@ -0,0 +1,15 @@
+---
+title: "A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets"
+category: "content"
+date: "2026-04-14T00:28:56-04:00"
+tags: ["content", "reactome-research-spotlight", "292-a-workflow-for-human-health-hazard-evaluation-using-transcriptomic-data-and-key-characteristics-based-gene-sets"]
+---
+
+## A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets
+
+In their March 2026 Society of Toxicology paper, “[A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets](<https://academic.oup.com/toxsci/article/205/2/310/8089853>)”, Tsai et al. propose a framework to evaluate transcriptomic data through two paradigms: Key Characteristics (KCs) of chemical compounds - expert-defined properties of chemicals associated with specific human health hazards - and pathway annotation databases. The authors first consolidated 72 individual KCs from seven published hazard-specific sets (covering carcinogens, cardiovascular toxicants, endocrine disruptors, reproductive toxicants, hepatotoxicants, and immunotoxicants) into 34 non-redundant umbrella KC terms. They then systematically mapped Reactome and KEGG pathways to these terms and generated parallel “KC gene sets" derived from Reactome and KEGG for each umbrella KC term by pooling all genes contained in the mapped pathways.
+
+The Reactome- and KEGG-derived KC gene sets showed low gene overlap (most Jaccard scores below 0.1), confirming that the two databases are largely complementary rather than redundant and suggesting that optimal results can be obtained by using both gene sets in parallel. Reactome KC gene sets covered 77% of Reactome's annotated human genes.
+
+The proposed workflow was then validated across four compounds. Enrichment analysis correctly identified immunotoxicity and oxidative stress for benzene, hepatotoxicity and carcinogenicity for TCDD (including strain-specific differences in AHR-driven liver fibrosis), and cardiac and mitochondrial dysfunction for the known cardiotoxicant sunitinib, while the non-cardiotoxic antibiotic amoxicillin showed minimal enrichment, as expected. GSEA and ORA were complementary, with GSEA showing greater sensitivity overall. Overall, the results suggest the proposed workflow may be a useful tool for systematic integration of transcriptomics into chemical hazard assessment.
+