Abstract Genomic DNA encodes regulatory information that determines where, when, and to what extent genes are expressed. Theoretically, we should be able to identify these transcriptional “instructions” by examining genomic DNA sequence alone, yet this has remained challenging. Here we present the Vertebrate Regulatory MOdule Detector (VRMOD), a method that accurately predicts gene regulatory sequences using only the query genomic sequences. We applied VRMOD to 309 Ensembl genomes, generating a compendium of high-resolution, genome-position-fixed cis-regulatory modules without parameter tuning. We performed extensive computational evaluation and experimental validation of VRMOD predictions. Notably, VRMOD predicted three sub-enhancers within the human hs52 enhancer at the FTO locus from the VISTA database, including one missed by existing methods. Using a chicken embryo system and 3D tissue imaging, we showed that each sub-enhancer exhibits restricted spatiotemporal activity within specific subsets of tissues where the full enhancer is active. We further demonstrated VRMOD’s utility for identifying evolutionarily non-conserved enhancers, annotating regulatory sequences in non-model organisms, and identifying candidate disease-causal variants. Collectively, VRMOD provides a universal coordinate reference system for regulatory sequences across 309 vertebrate genomes and enables genome-wide annotation of non-coding regulatory elements in any vertebrate species using genomic sequence alone.
Tássia Mangetti Gonçalves, Casey L. Stewart, Samantha D. Baxley et al.· NAR Genomics and Bioinformat...· 0 citations
Early-phase studies of deep B cell depletion with anti-CD19 chimeric antigen receptor (CAR) T have produced prolonged, drug-free remission in refractory autoimmune disease. However, CAR T cell therapy requires lymphodepleting chemotherapy, autologous cell manufacturing and specialized infrastructure, limiting reach to a fraction of patients who might benefit. Bispecific T cell engagers (TCEs) offer a potent, off-the-shelf approach to deep B cell depletion; however, controlled clinical evaluation in rheumatic disease remains limited. Here we report results from the intravenous treatment arm of an ongoing, first-in-disease, phase 1 trial of A-319, a next-generation CD3×CD19 TCE, in 12 patients with active systemic lupus erythematosus (SLE) with 52 weeks of follow-up. Patients received A-319 (0.3−1.2 μg kg−1) three times weekly for 3 weeks after 1 week of priming doses (0.05 μg kg−1). The primary endpoint was safety and tolerability. A-319 demonstrated a favorable safety profile, with no treatment-related serious adverse events, deaths, grade 3 or higher cytokine release syndrome (CRS) or neurotoxicity; CRS was predominantly grade 1 (91.6%, 11/12), and hematologic toxicity was minimal. Secondary endpoints (pharmacokinetics, pharmacodynamics and immunogenicity) demonstrated linear pharmacokinetics and dose-dependent B cell depletion, with complete peripheral depletion in higher-dose cohorts. Among exploratory efficacy endpoints, 80% (8/10) of patients achieved Lupus Low Disease Activity State (LLDAS), and 60% (6/10) achieved Definition of Remission in SLE (DORIS) at 12 months, accompanied by sustained reductions in Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) scores, autoantibody titers and proteinuria. Exploratory serial single-cell RNA sequencing revealed broad immune reprogramming mechanistically similar to that after CD19 CAR T cell therapy in SLE, including multi-lineage suppression of SLE-associated interferon response signatures across B cell, T cell and myeloid compartments and near-complete reconstitution of the B cell repertoire. Together, these findings demonstrate the feasibility, safety and preliminary efficacy of CD3×CD19 T cell engagement in SLE and support further clinical development of TCEs in controlled, pivotal studies. ClinicalTrials.gov identifier: NCT06400537. In a phase 1 trial evaluating intravenous delivery of a next-generation CD3×CD19 bispecific T cell engager in patients with active systemic lupus erythematosus, treatment was well tolerated, and there were improvements in disease activity scores for most patients.
Jason Xu, Chun-Li Mei, Xin Guan et al.· Nature Medicine· 0 citations
We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events. For every one of the 104 matches of the 2026 FIFA World Cup, four frontier models -- Claude Opus 4.8, ChatGPT (GPT-5.5, high reasoning), Gemini 3.1 Pro, and Grok (Expert Mode) -- ran an identical search-act-reflect loop: gather evidence with a web tool, commit to a 1X2 (team-A win / draw / team-B win) distribution and a virtual 100-USD bet, and, after the match, reflect given only the final score. Because every match kicked off after the models'training cutoffs, the benchmark is contamination-free by construction. Crucially, we pair the four agents with a fifth competitor drawn from the same information environment -- the pre-match betting market -- collected as per-match 1X2 odds, giving an economically grounded baseline and letting us score not just what an agent predicts but what it does with money. The release contains 416 forecasts and 414 reflections with verbatim reasoning, ground truth (including penalty shootouts), odds, and a reproducible evaluation suite. A reference evaluation surfaces findings that raw accuracy hides: the four agents issue an identical top pick in 92% of matches and none beats the market's Brier score; indeed, a naive flat stake on the market favorite out-earns all four agents. Yet the agents diverge sharply as decision-makers: betting return-on-investment ranges from -18% to +10%, fading the market is unprofitable for all four, the share of forecasts that cite the market ranges from 12% to 100%, and self-reported error rates on wrong picks range from 36% to 86%. The benchmark thus measures calibration, decision quality, and self-knowledge -- axes on which frontier models differ even when their predictions do not. Data and code: https://github.com/graphuofm/FIFA2026LLM
Jiachen Ding, Congyu Guo, Jason Xu· arXiv.org· 1 citation
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