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BACKGROUND AND OBJECTIVES: Most patients with amyotrophic lateral sclerosis (ALS) lack a monogenic mutation. This study evaluates ALS cumulative genetic risk in an independent Michigan and Spanish replication cohort using polygenic scores. METHODS: Participant samples from University of Michigan were genotyped and assayed for the chromosome 9 open reading frame 72 hexanucleotide expansion. Final cohort size was 219 ALS and 223 healthy controls after genotyping and participant filtering. Polygenic scores excluding the C9 region were generated using an independent ALS genome-wide association study (20,806 cases, 59,804 controls). Adjusted logistic regression and receiver operating characteristic curves evaluated the association and classification between polygenic scores and ALS status, respectively. Population attributable fractions and pathway analyses were conducted. An independent Spanish study sample (548 cases, 2,756 controls) was used for replication. RESULTS: Polygenic scores constructed from 275 single-nucleotide variation (SNV) had the best model fit in the Michigan cohort. An SD increase in ALS polygenic score associated with 1.28 (95% CI 1.04–1.57) times higher odds of ALS with area under the curve of 0.663 vs a model without the ALS polygenic score (p value = 1 × 10(−6)). The population attributable fraction of the highest 20th percentile of ALS polygenic scores, relative to the lowest 80th percentile, was 4.1% of ALS cases. Genes annotated to this polygenic score enriched for important ALS pathomechanisms. Meta-analysis with the Spanish study, using a harmonized 132 single nucleotide variation polygenic score, yielded similar logistic regression findings (odds ratio: 1.13, 95% CI 1.04–1.23). DISCUSSION: ALS polygenic scores can account for cumulative genetic risk in populations and reflect disease-relevant pathways. If further validated, this polygenic score will inform future ALS risk models.

Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trained language models, such as generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT), have shown promising results in natural language processing (NLP) tasks. We evaluated the PPI identification performance of various GPT and BERT models using a manually curated benchmark corpus of 164 PPIs in 77 sentences from learning language in logic (LLL). BERT-based models achieved the best overall performance, with PubMedBERT achieving the highest precision (85.17%) and F1-score (86.47%) and BioM-ALBERT achieving the highest recall (93.83%). Despite not being explicitly trained for biomedical texts, GPT-4 achieved comparable performance to the best BERT models with 83.34% precision, 76.57% recall, and 79.18% F1-score. These findings suggest that GPT models can effectively detect PPIs from text data and have the potential for use in biomedical literature mining tasks.

Additional file 1: Supplementary information: Supplementary figures: Figure S1. Alpha diversity across microbiome samples. Figure S2. Dietary reversal shifts microbial taxa signature in obese PN mice. Figure S3. Full correlation analysis for microbial communities to plasma and sciatic nerve lipidomics. Figure S4. Full correlation analysis for microbial communities to plasma and sciatic nerve transcriptomics. Supplementary tables: Table S1. Study microbiome samples. Table S2. Beta diversity in microbiome samples.

Additional file 3: Supplemental Table 2. CIDO statistics including terms imported from major reference ontologies.

Additional file 3: Supplemental Table 2. CIDO statistics including terms imported from major reference ontologies.

Additional file 2: Supplemental Table 1. Resources used for our coronavirus disease-related data collection.

Additional file 1: Supplemental file 1. Visualization of the Evolution of CIDO.

Peripheral neuropathy (PN) is a frequent prediabetes and type 2 diabetes (T2D) complication. Multiple clinical studies reveal that obesity and dyslipidemia can also drive PN progression, independent of glycemia. These clinical observations suggest a complex interplay of specific metabolite and/or lipid species may underlie PN. To address this, we completed plasma metabolomic and lipidomic profiling of a population with mean class 3 obesity with (n=44) and without PN (n=44), matched for glycemic status, versus lean non-neuropathic controls (n=43). Lean versus obese comparisons, regardless of PN status, identified the most significant changes in gamma-glutamyl and branched-chain amino acid metabolism from global metabolomics analysis and triacylglycerols from lipidomics. Stratification by PN status within obese individuals identified changes in polyamine, purine biosynthesis, and benzoate metabolism. Lipidomics found diacylglycerols as the most significant sub-pathway distinguishing obese individuals by PN status, with additional contributions from phosphatidylcholines, sphingomyelins, ceramides, and dihydroceramides. Stratifying the obese cohort by glycemic status did not affect separation by PN status. These results suggest obesity may be as strong a PN driver as prediabetes or T2D in individuals with class 3 obesity, at least by plasma metabolomics and lipidomics profile. In summary, both metabolic and complex lipid pathways can differentiate obese individuals with and without PN, independent of glycemic status.

Coronavirus disease 2019 (COVID-19) has impacted almost every part of human life worldwide, posing a massive threat to human health. There is no specific drug for COVID-19, highlighting the urgent need for the development of effective therapeutics. To identify potentially repurposable drugs, we employed a systematic approach to mine candidates from U.S. FDA-approved drugs and preclinical small-molecule compounds by integrating the gene expression perturbation data for chemicals from the Library of Integrated Network-Based Cellular Signatures project with a publicly available single-cell RNA sequencing dataset from mild and severe COVID-19 patients. We identified 281 FDA-approved drugs that have the potential to be effective against SARS-CoV-2 infection, 16 of which are currently undergoing clinical trials to evaluate their efficacy against COVID-19. We experimentally tested the inhibitory effects of tyrphostin-AG-1478 and brefeldin-a on the replication of the single-stranded ribonucleic acid (ssRNA) virus influenza A virus. In conclusion, we have identified a list of repurposable anti-SARS-CoV-2 drugs using a systems biology approach.

Additional file 1: Figure S1. Sample distribution based on transcriptomic data. Figure S2. MA plot showing the top 50 DEGs in Group 1 versus Group 2. Figure S3. Gene expression patterns of the most significant 100 DEGs between Group 1 and Group 2. Figure S4. Functional enrichment analysis of DEGs by Reactome and DO. Figure S5. Functional enrichment analysis of DMGs by Reactome and DO. Figure S6. Functional enrichment analysis of overlapping DEGs and DMGs in the same direction.

Genotoxicity potential is a critical component of any comprehensive toxicological profile. Compounds that induce DNA or chromosomal damage often activate p53, a transcription factor essential to cell cycle regulation. Thus, within the U.S. Tox21 Program, we screened a library of ~10,000 (~8,300 unique) environmental compounds and drugs for activation of the p53-signaling pathway using a quantitative high-throughput screening assay employing HCT-116 cells (p53+/+) containing a stably integrated β-lactamase reporter gene under control of the p53 response element (p53RE). Cells were exposed (-S9) for 16 h at 15 concentrations (generally 1.2 nM to 92 μM) three times, independently. Excluding compounds that failed analytical chemistry analysis or were suspected of inducing assay interference, 365 (4.7%) of 7,849 unique compounds were concluded to activate p53. As part of an in-depth characterization of our results, we first compared them with results from traditional in vitro genotoxicity assays (bacterial mutation, chromosomal aberration); ~15% of known, direct-acting genotoxicants in our library activated the p53RE. Mining the Comparative Toxicogenomics Database revealed that these p53 actives were significantly associated with increased expression of p53 downstream genes involved in DNA damage responses. Furthermore, 53 chemical substructures associated with genotoxicity were enriched in certain classes of p53 actives, e.g., anthracyclines (antineoplastics) and vinca alkaloids (tubulin disruptors). Interestingly, the tubulin disruptors manifested unusual non-monotonic concentration response curves suggesting activity through a unique p53 regulatory mechanism. Through analysis of our results, we aim to define a role for this assay as one component of a comprehensive toxicological characterization of large compound libraries.