tooluniverse-immunology

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Immunology Research Skill

免疫学研究技能

KEY PRINCIPLES: Multi-layer evidence; source every claim; use immunology-specific databases first (IEDB, IMGT, SAbDab); always use English gene/protein names in tool calls.

核心原则：多层证据支撑；所有结论需标注来源；优先使用免疫学专用数据库（IEDB、IMGT、SAbDab）；工具调用中始终使用英文基因/蛋白名称。

LOOK UP, DON'T GUESS

查资料，勿猜测

When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.

For MC about immune mechanisms: Look up the specific pathway/receptor/cytokine before answering. Use

PubMed_search_articles

with the exact terms from the question. The answer is almost always in the first few search results.

Specific LOOK UP targets (never guess these):

Immune cell markers: CD markers for cell subsets (e.g., Treg = CD4+CD25+FOXP3+, not just "CD4+"). Query UniProt or IEDB.
Cytokine functions: IL-17 is pro-inflammatory (Th17), IL-10 is anti-inflammatory (Treg) — but context matters. Verify via KEGG pathway or PubMed.
MHC/HLA restrictions: Which HLA allele presents which peptide — always check IEDB MHC binding data; allele-level differences are critical (HLA-A02:01 vs HLA-A02:07 have different peptide repertoires).
Antibody Kd values: Never estimate binding affinity; check SAbDab, IEDB, or published literature.

对任何科学事实存疑时，优先检索数据库（PubMed、UniProt、ChEMBL、ClinVar等），而非凭记忆推理。经数据库验证的答案永远比猜测更可靠。

针对免疫机制的选择题：作答前先检索特定通路/受体/细胞因子。使用

PubMed_search_articles

工具，输入问题中的精准术语。答案几乎总能在前几条搜索结果中找到。

需重点检索的内容（绝不猜测）：

免疫细胞标志物：细胞亚群的CD标志物（如调节性T细胞=Treg=CD4+CD25+FOXP3+，而非仅“CD4+”）。查询UniProt或IEDB。
细胞因子功能：IL-17为促炎因子（Th17细胞分泌），IL-10为抗炎因子（Treg细胞分泌）——但具体功能需结合场景判断。通过KEGG通路或PubMed验证。
MHC/HLA限制性：特定HLA等位基因呈递哪种肽段——务必查阅IEDB的MHC结合数据；等位基因层面的差异至关重要（HLA-A02:01与HLA-A02:07的肽段库不同）。
抗体Kd值：绝不估算结合亲和力；查阅SAbDab、IEDB或已发表文献。

COMPUTE, DON'T DESCRIBE

做计算，勿描述

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

当分析需要计算（统计、数据处理、评分、富集分析）时，通过Bash编写并运行Python代码。不要描述操作步骤——直接执行并汇报实际结果。使用ToolUniverse工具获取数据，再用Python（pandas、scipy、statsmodels、matplotlib）进行分析。

Reasoning Frameworks

推理框架

Immune response reasoning — Every immune response has innate → adaptive phases. Ask: which arm is relevant to the question? Innate (neutrophils, macrophages, complement, pattern recognition) or adaptive (T cells, B cells, antibodies, memory)? Innate is fast (hours) and antigen-nonspecific; adaptive is slow (days) but specific and generates memory. The transition occurs when APCs present antigen to naive T/B cells. Targeting innate suppresses broad inflammation; targeting adaptive disrupts antigen-specific responses. This determines which databases and tools are most relevant.

Antibody analysis reasoning — Structure determines function. The variable region (VH/VL, CDR loops) determines antigen specificity. The Fc region determines effector function: complement activation (IgM, IgG), ADCC via FcγR (IgG), or opsonization. When analyzing antibody data, always ask: are we studying binding (Fab — use IEDB, SAbDab, IMGT) or function (Fc — use FAERS for clinical safety, OpenTargets for target biology, TheraSAbDab for therapeutic format/isotype)? Isotype switching changes effector function without changing specificity.

Autoimmunity reasoning — Autoimmunity = loss of self-tolerance. Ask: is the attack cell-mediated (T cells destroying tissue → Type 1 diabetes, MS) or antibody-mediated (autoantibodies → SLE, myasthenia gravis, Graves')? Cell-mediated disease implicates MHC class I/II and TCR repertoire; antibody-mediated implicates B cell activation, affinity maturation, and complement. This determines the disease mechanism, the relevant genetic loci (HLA alleles dominate both, but TCR genes matter more for T-cell diseases), and the therapeutic approach (biologics targeting T cells vs. B cells vs. complement).

Antibody-antigen interaction reasoning — Binding strength has two axes: affinity (Kd of single binding site, typically nM–pM for therapeutic mAbs) and avidity (combined strength of all binding sites — IgM pentamer has low affinity but high avidity). When analyzing binding data: Kd < 1 nM = very high affinity; 1–100 nM = moderate; > 100 nM = weak. Epitope mapping strategy depends on the question: linear epitopes → peptide arrays or IEDB linear epitope search; conformational epitopes → HDX-MS, cryo-EM, or cross-linking MS. For therapeutic antibodies, check SAbDab for co-crystal structures and TheraSAbDab for clinical-stage format/engineering details.

Immune signaling cascade reasoning — When asked "what happens when cytokine X activates cell Y", trace the full pathway: receptor (which subunits?) → proximal kinase (JAK1/2/3, TYK2, Src family?) → transcription factor (STAT1/3/4/5/6, NF-kB, NFAT?) → effector genes (cytokines, cytotoxic molecules, survival factors). Example: IL-12 + T cell → IL-12R (IL12RB1+IL12RB2) → JAK2/TYK2 → STAT4 → IFN-gamma production (Th1 differentiation). Use KEGG pathway hsa04630 (JAK-STAT) and Reactome R-HSA-1280215 (Cytokine Signaling) to verify. Key signaling modules: JAK-STAT (most cytokines), NF-kB (TNF, TLRs, TCR/BCR co-stimulation), MAPK/ERK (growth factors, TCR), PI3K-AKT (co-stimulation, survival).

Complement system reasoning — Three activation pathways converge on C3 convertase: Classical (C1q binds antibody-antigen complexes — IgM or IgG → C4b2a), Lectin (MBL binds mannose on pathogens → C4b2a), Alternative (spontaneous C3 hydrolysis + factor B/D → C3bBb, amplification loop). All converge on C5 convertase → MAC (C5b-9). When to check which: suspected immune complex disease (SLE) → classical pathway (C1q, C4); recurrent bacterial infections → alternative or lectin (factor B, MBL); paroxysmal nocturnal hemoglobinuria → terminal pathway (CD55/CD59 deficiency). Therapeutic targets: eculizumab blocks C5; avacopan blocks C5aR.

Evidence grading — A (strong): GWAS p < 5e-8 + functional data + clinical signal. B (moderate): genetics or pathway evidence, limited functional data. C (preliminary): single-database hit only. Converging genetic (GWAS/Orphanet) + protein interaction (IntAct/BioGRID) + pathway data raises confidence. FAERS PRR > 2 with IC025 > 0 is a signal, not causal proof. TIMER2 deconvolution estimates require orthogonal validation.

免疫应答推理——所有免疫应答均包含固有→适应性阶段。思考：问题涉及哪一阶段？固有免疫（中性粒细胞、巨噬细胞、补体、模式识别受体）还是适应性免疫（T细胞、B细胞、抗体、记忆细胞）？固有免疫起效快（数小时）且无抗原特异性；适应性免疫起效慢（数天）但具有特异性并产生记忆。当抗原呈递细胞（APC）将抗原呈递给初始T/B细胞时，两种免疫阶段发生转换。靶向固有免疫可抑制广泛炎症；靶向适应性免疫可阻断抗原特异性应答。这决定了最相关的数据库与工具。

抗体分析推理——结构决定功能。可变区（VH/VL、CDR环）决定抗原特异性。Fc区决定效应功能：补体激活（IgM、IgG）、通过FcγR介导的抗体依赖的细胞毒性（ADCC，IgG）或调理作用。分析抗体数据时，始终明确：研究的是结合功能（Fab段——使用IEDB、SAbDab、IMGT）还是效应功能（Fc段——使用FAERS获取临床安全性数据、OpenTargets获取靶点生物学信息、TheraSAbDab获取治疗性抗体的形式/同种型）？同种型转换会改变效应功能，但不会改变抗原特异性。

自身免疫推理——自身免疫=自身耐受缺失。思考：攻击是细胞介导型（T细胞破坏组织→1型糖尿病、多发性硬化症）还是抗体介导型（自身抗体→系统性红斑狼疮SLE、重症肌无力、格雷夫斯病）？细胞介导型疾病涉及MHC I/II类分子和TCR库；抗体介导型疾病涉及B细胞激活、亲和力成熟和补体。这决定了疾病机制、相关基因位点（HLA等位基因在两类疾病中均占主导，但TCR基因在T细胞疾病中更重要）以及治疗方案（靶向T细胞、B细胞或补体的生物制剂）。

抗体-抗原相互作用推理——结合强度包含两个维度：亲和力（单个结合位点的Kd值，治疗性单克隆抗体通常为nM–pM级别）和avidity（所有结合位点的综合强度——IgM五聚体亲和力低但avidity高）。分析结合数据时：Kd < 1 nM = 极高亲和力；1–100 nM = 中等亲和力；> 100 nM = 弱亲和力。表位定位策略取决于研究问题：线性表位→肽阵列或IEDB线性表位检索；构象表位→氢氘交换质谱（HDX-MS）、冷冻电镜（cryo-EM）或交联质谱。针对治疗性抗体，查阅SAbDab获取共晶结构，查阅TheraSAbDab获取临床阶段的形式/工程化细节。

免疫信号级联推理——当被问及“细胞因子X激活细胞Y后会发生什么”时，追踪完整通路：受体（包含哪些亚基？）→近端激酶（JAK1/2/3、TYK2、Src家族？）→转录因子（STAT1/3/4/5/6、NF-kB、NFAT？）→效应基因（细胞因子、细胞毒性分子、存活因子）。示例：IL-12 + T细胞 → IL-12受体（IL12RB1+IL12RB2）→ JAK2/TYK2 → STAT4 → γ干扰素（IFN-gamma）产生（Th1细胞分化）。使用KEGG通路hsa04630（JAK-STAT通路）和Reactome通路R-HSA-1280215（细胞因子信号传导）验证。关键信号模块：JAK-STAT（多数细胞因子）、NF-kB（TNF、TLR、TCR/BCR共刺激）、MAPK/ERK（生长因子、TCR）、PI3K-AKT（共刺激、存活）。

补体系统推理——三条激活通路均汇聚于C3转化酶：经典通路（C1q结合抗体-抗原复合物——IgM或IgG→C4b2a）、凝集素通路（MBL结合病原体表面的甘露糖→C4b2a）、旁路通路（C3自发水解+因子B/D→C3bBb，放大循环）。所有通路最终汇聚于C5转化酶→膜攻击复合物（MAC，C5b-9）。不同场景对应不同通路：疑似免疫复合物疾病（SLE）→经典通路（C1q、C4）；反复细菌感染→旁路或凝集素通路（因子B、MBL）；阵发性睡眠性血红蛋白尿→终末通路（CD55/CD59缺陷）。治疗靶点：依库珠单抗（eculizumab）阻断C5；阿伐考班（avacopan）阻断C5aR。

证据分级——A级（强证据）：GWAS p值 < 5e-8 + 功能数据 + 临床信号。B级（中等证据）：遗传学或通路证据，功能数据有限。C级（初步证据）：仅单一数据库命中结果。遗传学（GWAS/Orphanet）+蛋白相互作用（IntAct/BioGRID）+通路数据的交叉验证可提升置信度。FAERS中PRR > 2且IC025 > 0为信号，但并非因果证明。TIMER2反卷积估算需经正交验证。

Tool Reference

工具参考

Antibody / Structural (SAbDab, TheraSAbDab)

抗体/结构分析（SAbDab、TheraSAbDab）

Tool	Key Parameters
`SAbDab_get_structure`	`pdb_id` (str) — structure details and chain info
`SAbDab_get_summary`	`pdb_id` (str) — CDR and chain summary
`SAbDab_search_structures`	`query` (str) — returns browse URL only, not JSON
`TheraSAbDab_search_therapeutics`	`query` (str, e.g. "pembrolizumab") — INN, target, format, phase
`TheraSAbDab_search_by_target`	`target` (str) — all therapeutics for an antigen
`TheraSAbDab_get_all_therapeutics`	(none) — full therapeutic antibody list

工具	关键参数
`SAbDab_get_structure`	`pdb_id` （字符串）——结构详情及链信息
`SAbDab_get_summary`	`pdb_id` （字符串）——CDR及链信息汇总
`SAbDab_search_structures`	`query` （字符串）——仅返回浏览URL，不返回JSON
`TheraSAbDab_search_therapeutics`	`query` （字符串，例如“pembrolizumab”）——国际非专利名称（INN）、靶点、形式、研发阶段
`TheraSAbDab_search_by_target`	`target` （字符串）——针对某一抗原的所有治疗性抗体
`TheraSAbDab_get_all_therapeutics`	无参数——完整治疗性抗体列表

Epitope and Immune Assays (IEDB)

表位与免疫实验（IEDB）

All search tools accept

limit

offset

filters

(PostgREST dict).

Tool	Extra Parameters
`iedb_search_epitopes`	`sequence_contains` , `structure_type`
`iedb_search_tcell_assays`	`sequence_contains` , `mhc_class` , `qualitative_measure`
`iedb_search_bcell`	filters only
`iedb_search_mhc`	filters only
`iedb_search_tcr_sequences` / `iedb_search_bcr_sequences`	filters only

Detail tools by

structure_id

iedb_get_epitope_antigens

iedb_get_epitope_mhc

iedb_get_epitope_tcell_assays

iedb_get_epitope_references

所有检索工具均支持

limit

、

offset

、

filters

（PostgREST字典）参数。

工具	额外参数
`iedb_search_epitopes`	`sequence_contains` 、 `structure_type`
`iedb_search_tcell_assays`	`sequence_contains` 、 `mhc_class` 、 `qualitative_measure`
`iedb_search_bcell`	仅支持filters参数
`iedb_search_mhc`	仅支持filters参数
`iedb_search_tcr_sequences` / `iedb_search_bcr_sequences`	仅支持filters参数

通过

structure_id

调用详情工具：

iedb_get_epitope_antigens

、

iedb_get_epitope_mhc

、

iedb_get_epitope_tcell_assays

、

iedb_get_epitope_references

。

Immunoglobulin Genes (IMGT)

免疫球蛋白基因（IMGT）

IMGT_search_genes

IMGT_get_gene_info

IMGT_get_sequence

— all take

gene_name

(e.g. "IGHV1-2").

IMGT_search_genes

、

IMGT_get_gene_info

、

IMGT_get_sequence

——均接受

gene_name

参数（例如“IGHV1-2”）。

Protein Interactions (IntAct, BioGRID)

蛋白相互作用（IntAct、BioGRID）

Tool	Key Parameters
`intact_get_interaction_network`	`identifier` (UniProt accession — gene symbols return 0 results), `limit`
`intact_search_interactions`	`query` (keyword), `limit`
`BioGRID_get_interactions`	`gene_names` (list), `organism` ("9606" string), `limit`
`BioGRID_get_chemical_interactions`	`gene_names` (list), `chemical_names` (list), `organism` (int)

Weight interaction evidence: co-IP and two-hybrid = direct; co-expression or text-mining = hypothesis-generating.

工具	关键参数
`intact_get_interaction_network`	`identifier` （UniProt登录号——基因符号会返回0条结果）、 `limit`
`intact_search_interactions`	`query` （关键词）、 `limit`
`BioGRID_get_interactions`	`gene_names` （列表）、 `organism` （字符串“9606”）、 `limit`
`BioGRID_get_chemical_interactions`	`gene_names` （列表）、 `chemical_names` （列表）、 `organism` （整数）

蛋白相互作用证据权重：免疫共沉淀（co-IP）和双杂交实验=直接相互作用；共表达或文本挖掘=假说生成级证据。

Cytokine / Signaling (OpenTargets, GWAS)

细胞因子/信号通路（OpenTargets、GWAS）

Tool	Key Parameters
`OpenTargets_get_target_id_description_by_name`	`targetName` — resolves gene symbol to Ensembl ID (required before ensemblId tools)
`OpenTargets_get_target_interactions_by_ensemblID`	`ensemblId` , `size`
`OpenTargets_get_target_gene_ontology_by_ensemblID`	`ensemblId`
`OpenTargets_get_target_safety_profile_by_ensemblID`	`ensemblId`
`OpenTargets_get_associated_diseases_by_drug_chemblId`	`chemblId`
`gwas_search_associations`	`query` (disease name)
`gwas_get_snps_for_gene`	`gene_symbol` (mapped gene symbol)

工具	关键参数
`OpenTargets_get_target_id_description_by_name`	`targetName` ——将基因符号解析为Ensembl ID（使用基于ensemblId的工具前需执行此步骤）
`OpenTargets_get_target_interactions_by_ensemblID`	`ensemblId` 、 `size`
`OpenTargets_get_target_gene_ontology_by_ensemblID`	`ensemblId`
`OpenTargets_get_target_safety_profile_by_ensemblID`	`ensemblId`
`OpenTargets_get_associated_diseases_by_drug_chemblId`	`chemblId`
`gwas_search_associations`	`query` （疾病名称）
`gwas_get_snps_for_gene`	`gene_symbol` （映射后的基因符号）

Clinical / Safety (FAERS, Clinical Trials)

临床/安全性（FAERS、临床试验）

Tool	Key Parameters
`FAERS_calculate_disproportionality`	`drug_name` (generic), `adverse_event` → PRR, ROR, IC
`FAERS_filter_serious_events`	`drug_name` , `seriousness_type`
`FAERS_stratify_by_demographics`	`drug_name` , `stratify_by` (sex/age/country)
`FAERS_compare_drugs`	`drug1` , `drug2` , `adverse_event`
`search_clinical_trials`	`condition` , `intervention` , `pageSize`

工具	关键参数
`FAERS_calculate_disproportionality`	`drug_name` （通用名）、 `adverse_event` ——返回PRR、ROR、IC值
`FAERS_filter_serious_events`	`drug_name` 、 `seriousness_type`
`FAERS_stratify_by_demographics`	`drug_name` 、 `stratify_by` （性别/年龄/国家）
`FAERS_compare_drugs`	`drug1` 、 `drug2` 、 `adverse_event`
`search_clinical_trials`	`condition` 、 `intervention` 、 `pageSize`

Autoimmune Genetics (Orphanet)

自身免疫遗传学（Orphanet）

Orphanet_search_diseases(query)

→ ORPHAcode. Then:

Orphanet_get_genes

Orphanet_get_phenotypes

Orphanet_get_epidemiology

Orphanet_get_natural_history

(all take

orpha_code

Orphanet_get_gene_diseases(gene_symbol)

for reverse lookup.

Orphanet_search_diseases(query)

——返回ORPHA编码。随后可调用：

Orphanet_get_genes

、

Orphanet_get_phenotypes

、

Orphanet_get_epidemiology

、

Orphanet_get_natural_history

（均接受

orpha_code

参数）。使用

Orphanet_get_gene_diseases(gene_symbol)

进行反向检索。

Immune Pathways (KEGG, Reactome)

免疫通路（KEGG、Reactome）

Tool	Key Parameters
`kegg_search_pathway`	`keyword`
`KEGG_get_disease` / `KEGG_get_disease_genes`	`disease_id` (e.g. "H00080" for SLE)
`KEGG_get_pathway_genes`	`pathway_id` (e.g. "hsa04060")
`Reactome_get_pathway`	`stId` (e.g. "R-HSA-168256") — NOT `pathway_id`
`ReactomeAnalysis_pathway_enrichment`	`identifiers` (space-separated STRING, not array)
`Reactome_map_uniprot_to_pathways`	`uniprot_id`

Key pathway IDs — Reactome: R-HSA-168256 (Immune System), R-HSA-168249 (Innate), R-HSA-1280218 (Adaptive), R-HSA-1280215 (Cytokine Signaling), R-HSA-202403 (TCR), R-HSA-983705 (BCR), R-HSA-166658 (Complement). KEGG: hsa04060 (Cytokine-receptor), hsa04660 (TCR), hsa04662 (BCR), hsa04620 (TLR), hsa04630 (JAK-STAT), hsa05322 (SLE), hsa05323 (RA).

工具	关键参数
`kegg_search_pathway`	`keyword`
`KEGG_get_disease` / `KEGG_get_disease_genes`	`disease_id` （例如“SLE”对应“H00080”）
`KEGG_get_pathway_genes`	`pathway_id` （例如“hsa04060”）
`Reactome_get_pathway`	`stId` （例如“R-HSA-168256”）——不支持 `pathway_id` 参数
`ReactomeAnalysis_pathway_enrichment`	`identifiers` （空格分隔的字符串，而非数组）
`Reactome_map_uniprot_to_pathways`	`uniprot_id`

关键通路ID——Reactome：R-HSA-168256（免疫系统）、R-HSA-168249（固有免疫）、R-HSA-1280218（适应性免疫）、R-HSA-1280215（细胞因子信号传导）、R-HSA-202403（TCR通路）、R-HSA-983705（BCR通路）、R-HSA-166658（补体系统）。KEGG：hsa04060（细胞因子-受体通路）、hsa04660（TCR通路）、hsa04662（BCR通路）、hsa04620（TLR通路）、hsa04630（JAK-STAT通路）、hsa05322（SLE）、hsa05323（类风湿性关节炎RA）。

Tumor Immune Microenvironment

肿瘤免疫微环境

TIMER2_immune_estimation

—

operation="immune_estimation"

cancer

(TCGA code e.g. "luad_tcga"),

gene

(symbol). Returns deconvolution estimates; validate with orthogonal methods.

TIMER2_immune_estimation

——参数

operation="immune_estimation"

、

cancer

（TCGA编码，例如“luad_tcga”）、

gene

（基因符号）。返回反卷积估算结果；需用正交方法验证。

Parameter Gotchas

参数注意事项

Issue	Wrong	Correct
Reactome param name	`pathway_id=`	`stId=`
ReactomeAnalysis identifiers	list `["STAT4","IRF5"]`	space-separated string `"STAT4 IRF5"`
OpenTargets target lookup	`query="IL6"`	`targetName="IL6"`
IntAct identifier	gene symbol `"CD274"`	UniProt accession `"Q9NZQ7"`
BioGRID organism	`"human"`	`"9606"` (string taxon ID)
BioGRID gene param	`gene_name="CD274"`	`gene_names=["CD274"]` (list)
FAERS drug name	brand name `"Keytruda"`	generic `"pembrolizumab"`
SAbDab search	expect JSON	`SAbDab_search_structures` returns URL only; use `SAbDab_get_structure` with PDB ID
TheraSAbDab by target	`search_by_target` for common names	Use `search_therapeutics(query=drug_name)` instead; target requires exact registry string
KEGG disease ID	`"lupus"`	`"H00080"`

问题	错误用法	正确用法
Reactome参数名称	`pathway_id=`	`stId=`
ReactomeAnalysis的identifiers参数	数组 `["STAT4","IRF5"]`	空格分隔的字符串 `"STAT4 IRF5"`
OpenTargets靶点检索	`query="IL6"`	`targetName="IL6"`
IntAct标识符	基因符号 `"CD274"`	UniProt登录号 `"Q9NZQ7"`
BioGRID物种参数	`"human"`	字符串格式的分类学ID `"9606"`
BioGRID基因参数	`gene_name="CD274"`	列表格式 `gene_names=["CD274"]`
FAERS药物名称	商品名 `"Keytruda"`	通用名 `"pembrolizumab"`
SAbDab检索	期望返回JSON	`SAbDab_search_structures` 仅返回URL；需结合PDB ID使用 `SAbDab_get_structure`
TheraSAbDab按靶点检索	使用通用名称调用 `search_by_target`	改用 `search_therapeutics(query=drug_name)` ；按靶点检索需使用精准的注册字符串
KEGG疾病ID	`"lupus"`	`"H00080"`

Workflows

工作流

Antibody target research:

TheraSAbDab_search_by_target

search_therapeutics

→

SAbDab_get_structure

for PDB data →

iedb_search_epitopes

iedb_search_tcell_assays

→

intact_get_interaction_network

(UniProt ID) +

BioGRID_get_interactions

→

FAERS_calculate_disproportionality

search_clinical_trials

Autoimmune disease genetics:

Orphanet_search_diseases

→

Orphanet_get_genes

Orphanet_get_phenotypes

→

gwas_search_associations

gwas_get_snps_for_gene

for candidate genes →

KEGG_get_disease

KEGG_get_pathway_genes

→

ReactomeAnalysis_pathway_enrichment

on disease genes.

Single-cell dual receptor questions: When asked about mechanisms for dual chain expression, distinguish BIOLOGICAL mechanisms (allelic inclusion, receptor editing, autoreactivity) from TECHNICAL artifacts (doublets, ambient RNA). Questions asking "why would a cell express two chains" usually want biological mechanisms only. Doublets (1) are often included since they represent real observations, but ambient RNA (2) is typically excluded as contamination, not true expression.

Immunotherapy safety comparison:

FAERS_compare_drugs

for AE head-to-head →

FAERS_filter_serious_events

per drug →

FAERS_stratify_by_demographics

→ resolve target with

OpenTargets_get_target_id_description_by_name

→

OpenTargets_get_target_safety_profile_by_ensemblID

→

search_clinical_trials

抗体靶点研究：

TheraSAbDab_search_by_target

或

search_therapeutics

→

SAbDab_get_structure

获取PDB数据 →

iedb_search_epitopes

iedb_search_tcell_assays

→

intact_get_interaction_network

（UniProt ID）+

BioGRID_get_interactions

→

FAERS_calculate_disproportionality

search_clinical_trials

。

自身免疫疾病遗传学研究：

Orphanet_search_diseases

→

Orphanet_get_genes

Orphanet_get_phenotypes

→

gwas_search_associations

gwas_get_snps_for_gene

筛选候选基因 →

KEGG_get_disease

KEGG_get_pathway_genes

→ 对疾病基因执行

ReactomeAnalysis_pathway_enrichment

富集分析。

单细胞双受体问题：当被问及双链表达机制时，区分生物学机制（等位基因包含、受体编辑、自身反应性）与技术伪影（双细胞、环境RNA污染）。若问题为“细胞为何表达两条链”，通常只需回答生物学机制。双细胞（1）常被视为真实观测结果，但环境RNA（2）通常被排除，因其属于污染而非真实表达。

免疫治疗安全性对比：

FAERS_compare_drugs

进行不良事件（AE）头对头对比 →

FAERS_filter_serious_events

筛选各药物的严重事件 →

FAERS_stratify_by_demographics

按人口统计学分层 → 使用

OpenTargets_get_target_id_description_by_name

解析靶点 →

OpenTargets_get_target_safety_profile_by_ensemblID

获取靶点安全性概况 →

search_clinical_trials

检索临床试验数据。