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PubMed2026

Characterising the motif composition and allele length distribution of ZFHX3 GGC repeat expansions in amyotrophic lateral sclerosis.

A pathogenic GGC repeat expansion in zinc finger homeobox 3 (ZFHX3), encoding a pure polyglycine (polyG) tract, causes spinocerebellar ataxia type 4 (SCA4). Intermediate expansions of other SCA loci have been implicated in amyotrophic lateral sclerosis (ALS), while repeat motif composition is recognised to influence pathogenicity in neurodegenerative diseases. Given the genetic pleiotropy between ALS and SCA, we evaluated whether ZFHX3 GGC expansions are associated with ALS and characterised repeat motif composition. ZFHX3 GGC repeat sizes were genotyped using ExpansionHunter in short-read whole-genome sequencing data from ALS cases and healthy controls of European ancestry. Repeat sizes were visually inspected using REViewer, and motif configurations were manually derived from a subset. Receiver operating characteristic analysis and Youden's J statistic identified a candidate repeat size threshold. Logistic regression tested associations of repeat length and motif composition with ALS, while regression models assessed clinical phenotypes. Across 5785 ALS cases and 7982 controls, no association was observed between ZFHX3 expansions and ALS risk. Longer alleles showed a nominal association with later disease onset, however this did not remain significant after Bonferroni correction. Among 802 ALS cases and 800 controls, 50 distinct motif compositions were identified, including 11 encoding pure polyG tracts characteristic of pathogenic SCA4 expansions; none were associated with ALS. Although no association with ALS was observed, this study established the dynamic nature of ZFHX3 repeat motif composition and configuration. Variation within and between repeat sizes, including pure polyG repeats, supports consideration of motif composition alongside allele length when evaluating neurodegenerative disease risk.

Journal Article↗

PubMed2005

Automated methods of predicting the function of biological sequences using GO and BLAST.

BACKGROUND: With the exponential increase in genomic sequence data there is a need to develop automated approaches to deducing the biological functions of novel sequences with high accuracy. Our aim is to demonstrate how accuracy benchmarking can be used in a decision-making process evaluating competing designs of biological function predictors. We utilise the Gene Ontology, GO, a directed acyclic graph of functional terms, to annotate sequences with functional information describing their biological context. Initially we examine the effect on accuracy scores of increasing the allowed distance between predicted and a test set of curator assigned terms. Next we evaluate several annotator methods using accuracy benchmarking. Given an unannotated sequence we use the Basic Local Alignment Search Tool, BLAST, to find similar sequences that have already been assigned GO terms by curators. A number of methods were developed that utilise terms associated with the best five matching sequences. These methods were compared against a benchmark method of simply using terms associated with the best BLAST-matched sequence (best BLAST approach). RESULTS: The precision and recall of estimates increases rapidly as the amount of distance permitted between a predicted term and a correct term assignment increases. Accuracy benchmarking allows a comparison of annotation methods. A covering graph approach performs poorly, except where the term assignment rate is high. A term distance concordance approach has a similar accuracy to the best BLAST approach, demonstrating lower precision but higher recall. However, a discriminant function method has higher precision and recall than the best BLAST approach and other methods shown here. CONCLUSION: Allowing term predictions to be counted correct if closely related to a correct term decreases the reliability of the accuracy score. As such we recommend using accuracy measures that require exact matching of predicted terms with curator assigned terms. Furthermore, we conclude that competing designs of BLAST-based GO term annotators can be effectively compared using an accuracy benchmarking approach. The most accurate annotation method was developed using data mining techniques. As such we recommend that designers of term annotators utilise accuracy benchmarking and data mining to ensure newly developed annotators are of high quality.

Benchmarking↗

PubMed2026

Digenic inheritance of mutations in SPG7 and AFG3L2 causes motor neuron and cerebellar disorders.

BACKGROUND: Biallelic SPG7 mutations cause one of the most common forms of hereditary spastic paraplegia (HSP). Several reports have suggested that heterozygous SPG7 variants may also play a role in HSP, but also in amyotrophic lateral sclerosis (ALS). However, it remains controversial whether heterozygous SPG7 mutations are pathogenic on their own, or if other mechanisms are at play. We recently provided evidence for non-Mendelian inheritance in spastic paraplegia 7 (SPG7), as heterozygous carriers of SPG7 mutations often also carried mutations in other disease-related genes, including AFG3L2, more frequently than expected by chance. Given that SPG7 and AFG3L2 encode interacting subunits of the mitochondrial m-AAA protease complex, we hypothesized that combined heterozygous mutations in these genes may act synergistically to disrupt mitochondrial function and contribute to disease. In this study, we aimed to examine whether digenic heterozygous mutations in SPG7 and AFG3L2 can lead to a spectrum of neurodegenerative disorders. METHODS: We first analyzed genome and exome sequencing data of 6644 unrelated individuals including 4817 motor neuron disorder (MND) and ataxia patients and 1827 controls. We next analyzed an additional 18,748 exome data from rare disease cohorts to further examine the occurrence of variants in SPG7 and AFG3L2. RESULTS: Among the first 4817 MND and ataxia patients, we identified a total of 6 patients, 4 of whom were unrelated, who carried potentially pathogenic variants in both SPG7 and AFG3L2, in contrast to none in 1827 unrelated controls. Further analysis of the 18,748 additional patients with rare disease, as well as a comprehensive literature review, identified 6 more patients, 5 of whom were unrelated, who had digenic mutations in SPG7 and AFG3L2. In the two families we identified, digenic mutations in SPG7 and AFG3L2 perfectly segregated with the disease. The 12 patients reported here exhibited predominant signs of motor neuron and cerebellar involvement. CONCLUSIONS: Our findings demonstrate that digenic inheritance of concurrent heterozygous mutations in SPG7 and AFG3L2 may cause motor neuron and cerebellar disorders. Screening of the entire SPG7 and AFG3L2 genes in genetically undiagnosed cases of MND and spastic ataxia may help to increase the diagnostic yield.

Humans↗

PubMed2006

Schistosoma mansoni TGF-beta receptor II: role in host ligand-induced regulation of a schistosome target gene.

Members of transforming growth factor-beta (TGF-beta) superfamily play pivotal roles in development in multicellular organisms. We report the functional characterization of the Schistosoma mansoni type II receptor (SmTbetaRII). Mining of the S. mansoni expressed sequence tag (EST) database identified an EST clone that shows homology to the kinase domain of type II receptors from different species. The amplified EST sequence was used as a probe to isolate a cDNA clone spanning the entire coding region of a type II serine/threonine kinase receptor. The interaction of SmTbetaRII with SmTbetaRI was elucidated and shown to be dependent on TGF-beta ligand binding. Furthermore, in the presence of human TGF-beta1, SmTbetaRII was able to activate SmTbetaRI, which in turn activated SmSmad2 and promoted its interaction with SmSmad4, proving the transfer of the signal from the receptor complex to the Smad proteins. Gynaecophoral canal protein (GCP), whose expression in male worms is limited to the gynaecophoric canal, was identified as a potential TGF-beta target gene in schistosomes. Knocking down the expression of SmTbetaRII using short interfering RNA molecules (siRNA) resulted in a concomitant reduction in the expression of GCP. These data provide evidence for the direct involvement of SmTbetaRII in mediating TGF-beta-induced activation of the TGF-beta target gene, SmGCP, within schistosome parasites. The results also provide additional evidence for a role for the TGF-beta signaling pathway in male-induced female reproductive development.

Activin Receptors, Type I↗

PubMed2003

PreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machine.

BACKGROUND: The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND. RESULTS: Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days. CONCLUSIONS: Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.

Algorithms↗

PubMed2026

Benchmarking large language models for extracting biobank-derived insights into health and disease.

Biobank-scale datasets such as the UK Biobank have become foundational resources for advancing biomedical discovery. Yet the complexity and heterogeneity of these resources, spanning genomics, imaging, clinical records, and metadata, pose substantial barriers to access and interpretation. Large Language Models (LLMs) offer a promising avenue for making such datasets more navigable through natural language interfaces. However, the extent to which current general-purpose LLMs can retrieve and synthesize biobank-specific insights has not yet been systematically evaluated. In this study, we present a reproducible, multi-metric evaluation framework to benchmark the capabilities of leading LLMs. We evaluated six leading large language models: Gemini 3 Pro, Claude Opus 4.5, Claude Sonnet 4.5, GPT-5.2, Mistral Large 2, and DeepSeek V3, on four benchmark tasks designed to assess biobank-related knowledge retrieval. We evaluate model performance across six dimensions (semantic accuracy, factual correctness, domain knowledge, reasoning quality, response depth, and biobank specificity) and assessed output consistency using curated UK Biobank references and a robust random baseline. All models outperformed the baseline by 2× to 3× , with strong statistical separation (p < 0.001), confirming meaningful biobank-specific knowledge retrieval. Gemini 3 Pro achieved the highest overall accuracy across tasks such as keyword synthesis, institution recognition, and topic inference, while Claude Sonnet 4.5 demonstrated the most uniform performance across evaluation dimensions. Our benchmark provides a rigorous framework for evaluating LLMs in biomedical settings. Using the UK Biobank as a real-world testbed, we highlight both the capabilities and limitations of current models, measuring their capacity to recall structured biomedical knowledge consistent with authoritative biobank metadata.

Large Language Models↗

PubMed2005

New data analysis and mining approaches identify unique proteome and transcriptome markers of susceptibility to autoimmune diabetes.

Non-obese diabetic (NOD) mice spontaneously develop autoimmunity to the insulin producing beta cells leading to insulin-dependent diabetes. In this study we developed and used new data analysis and mining approaches on combined proteome and transcriptome (molecular phenotype) data to define pathways affected by abnormalities in peripheral leukocytes of young NOD female mice. Cells were collected before mice show signs of autoimmunity (age, 2-4 weeks). We extracted both protein and RNA from NOD and C57BL/6 control mice to conduct both proteome analysis by two-dimensional gel electrophoresis and transcriptome analysis on Affymetrix expression arrays. We developed a new approach to analyze the two-dimensional gel proteome data that included two-way analysis of variance, cluster analysis, and principal component analysis. Lists of differentially expressed proteins and transcripts were subjected to pathway analysis using a commercial service. From the list of 24 proteins differentially expressed between strains we identified two highly significant and interconnected networks centered around oncogenes (Myc and Mycn) and apoptosis-related genes (Bcl2 and Casp3). The 273 genes with significant strain differences in RNA expression levels created six interconnected networks with a significant over-representation of genes related to cancer, cell cycle, and cell death. They contained many of the same genes found in the proteome networks (including Myc and Mycn). The combination of the eight, highly significant networks created one large network of 272 genes of which 82 had differential expression between strains either at the protein or the RNA level. We conclude that new proteome data analysis strategies and combined information from proteome and transcriptome can enhance the insights gained from either type of data alone. The overall systems biology of prediabetic NOD mice points toward abnormalities in regulation of the opposing processes of cell renewal and cell death even before there are any clear signatures of immune system activation.

Analysis of Variance↗

PubMed2026

[Effects and mechanisms of ethanol extract of Salvia miltiorrhiza on liver fibrosis in mice].

To identify clinically advantageous TCMs for anti-hepatic fibrosis and to elucidate the effects and molecular mechanisms of Salvia miltiorrhiza ethanol extract in the intervention of liver fibrosis, this study screened high-frequency anti-hepatic fibrosis TCMs through a review of clinical literature. The S. miltiorrhiza active components, potential targets, and liver fibrosis-related disease targets were obtained using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP), the GeneCards database, and other databases. Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses were performed on the shared targets between drugs and diseases. Molecular docking was conducted to evaluate the binding affinities between key components and core targets. In animal experiments, male Kunming mice were used to establish a liver fibrosis model induced by carbon tetrachloride(CCl_4). The mice were administered low, medium, and high doses of S. miltiorrhiza ethanol extract by gavage. The liver index, as well as serum aspartate aminotransferase(AST) and alanine aminotransferase(ALT) levels, were measured. Histopathological changes in liver tissue were observed using hematoxylin-eosin(HE) staining and Masson's trichrome staining. Western blot analysis was used to detect the protein expression levels of α-smooth muscle actin(α-SMA), Collagen Ⅰ, and heat shock protein 90 alpha family class A member 1(HSP90AA1) in liver tissue. The results showed that S. miltiorrhiza was the most frequently used TCM in clinical anti-hepatic fibrosis. A total of 65 active components and 135 potential targets were identified, and 109 common targets were obtained by intersecting these with liver fibrosis-related targets. The core targets included tumor protein p53(TP53), serine/threonine protein kinase AKT1(AKT1), Jun proto-oncogene(JUN), signal transducer and activator of transcription 3(STAT3), and HSP90AA1, which were mainly enriched in pathways related to cancer, hepatitis B, and the PI3K-AKT signaling pathway. Molecular docking indicated that the main active components of S. miltiorrhiza bound stably to the core targets, with the strongest binding affinity observed for HSP90AA1. Animal experiments demonstrated that the liver index, serum ALT and AST levels, and the expression of α-SMA, Collagen Ⅰ, and HSP90AA1 in liver tissue were significantly increased in the model group, accompanied by obvious pathological manifestations of fibrosis. Compared with the model group, different dose groups of S. miltiorrhiza ethanol extract reduced the liver index and serum ALT and AST levels to varying degrees, alleviated pathological damage and collagen deposition in liver tissue, and downregulated the protein expression of α-SMA, Collagen Ⅰ, and HSP90AA1. In conclusion, S. miltiorrhiza ethanol extract exerts a significant protective effect on CCl_4-induced liver fibrosis in mice, and its mechanisms may be related to the inhibition of HSP90AA1 expression and the regulation of liver fibrosis-related signaling pathways.

Animals↗