Mohammad Kashfi Haghighi, Matthieu Fortin-Deschênes, Christophe Pere
et al.
Genetic algorithms are highly effective optimization techniques for many computationally challenging problems, including combinatorial optimization tasks like portfolio optimization. Quantum computing has also shown potential in addressing these complex challenges. Combining these approaches, quantum genetic algorithms leverage the principles of superposition and entanglement to enhance the performance of classical genetic algorithms. In this work, we propose a novel quantum genetic algorithm introducing an innovative crossover strategy to generate quantum circuits from a binary solution. We incorporate a heuristic method to encode entanglement patterns from parent solutions into circuits for the next generation. Our algorithm advances quantum genetic algorithms by utilizing a limited number of entanglements, enabling efficient exploration of optimal solutions without significantly increasing circuit depth, making it suitable for near-term applications. We test this approach on a portfolio optimization problem using an IBM 127 qubits Eagle processor (ibm_quebec) and simulators. Compared to state-of-the-art algorithms, our results show that the proposed method improves fitness values by 33.6% over classical genetic algorithm and 37.2% over quantum-inspired genetic algorithm, using the same iteration counts and population sizes with real quantum hardware employing 100 qubits. These findings highlight the potential of current quantum computers to address real-world utility-scale combinatorial optimization problems.
Caffeic acid O-methyltransferase (COMT) catalyzes the penultimate methylation in monolignol biosynthesis, controlling lignin composition and abiotic-stress tolerance. Kenaf (Hibiscus cannabinus L.), a fast bast-fiber crop rich in lignin, is valued for its mechanical strength and resilience to salinity. However, the COMT gene family has not yet been systematically characterized in this species. Here, we integrated phylogenetics, synteny, promoter and transcriptome analyses to create a comprehensive profile of kenaf COMT genes. Genome-wide screening identified 81 HcCOMT genes. Phylogenetic reconstruction with COMTs from Arabidopsis thaliana and Gossypium hirsutum resolved 10 distinct clades. Synteny analysis revealed 2 collinear blocks with Arabidopsis and 14 with cotton, whereas intraspecific duplication events indicated recent lineage-specific expansion. Promoter analysis identified numerous cis-elements responsive to light, phytohormones and abiotic stress, suggesting complex transcriptional regulation. Transcriptome mining uncovered 6 candidate genes with pronounced tissue specificity and salt responsiveness; qRT-PCR confirmed these patterns in root, stem and leaf tissues under 200 mM NaCl: HcCOMT28 and HcCOMT29 were repressed in the leaf, whereas HcCOMT11, HcCOMT12, HcCOMT13, and HcCOMT17 were up-regulated, consistent with altered lignin deposition patterns. Our findings provide a comprehensive genomic resource delineating the structure, evolution, and salt-responsive expression of the kenaf COMT family, and establish a foundation for elucidating the molecular mechanisms underlying lignin-mediated salt tolerance and for breeding elite kenaf cultivars with tailored fiber properties.
Rebecca A. Milan, Mallory A. P. Sagehorn, Mallory A. P. Sagehorn
et al.
IntroductionThe COVID-19 pandemic significantly disrupted civic life, particularly for older adults at increased risk for severe morbidity and mortality. Yet, little is known about the longer-term impacts on their daily routines and how this may affect health and wellbeing.MethodsThis qualitative study utilized data from older US adults who participated in the COVID-19 Coping Study’s three-year follow-up online survey, conducted in April–May 2023. The primary aim was to understand how and why daily routines have changed among older Americans (N = 1,309).ResultsParticipants had an average age of 71 years, with approximately 74% female and 93% identifying as Non-Hispanic White. We conducted content and thematic analysis of open-ended survey responses to identify five key reasons for still-altered routines 3 years after the pandemic onset: (1) COVID-19 risk and exposure, (2) altered access, (3) broader life circumstances, (4) emotional health, and (5) physical health.DiscussionThese findings highlight the enduring impacts of the pandemic on older adults’ routines and underscore the importance of integrating public health strategies that prioritize routine stability to enhance mental, physical, and social health. To support older adults’ wellbeing during and beyond public health emergencies, we recommend strengthening community-based programs, improving access to health and social services, and designing adaptable interventions that help individuals rebuild and maintain meaningful daily routines.
Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF in colorectal cancer is crucial for clinical decision making. In this paper, we propose a whole slide image (WSI) based genetic biomarker prediction method via prompting techniques. Our work aims at addressing the following challenges: (1) extracting foreground instances related to genetic biomarkers from gigapixel WSIs, and (2) the interaction among the fine-grained pathological components in WSIs.Specifically, we leverage large language models to generate medical prompts that serve as prior knowledge in extracting instances associated with genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker information within the tumor microenvironment. This involves extracting instances related to genetic biomarkers using coarse medical prior knowledge, grouping pathology instances into fine-grained pathological components and mining their interactions. Experimental results on two colorectal cancer datasets show the superiority of our method, achieving 91.49% in AUC for MSI classification. The analysis further shows the clinical interpretability of our method. Code is publicly available at https://github.com/DeepMed-Lab-ECNU/PromptBio.
Gamma radiation-induced mutations in microorganisms can enhance their properties for the biological control of plant diseases. Mutant strains of Bacillus subtilis were found to have improved antifungal properties against Aspergillus flavus and increased production of biosurfactants and biofilms. Furthermore, combining gamma radiation with antagonists was more effective in controlling Penicillium expansum postharvest than either treatment alone. A major focus of this research was on Trichoderma species, which have shown an enhanced ability to control plant diseases through increased production of antifungal metabolites such as hydrolytic enzymes, antibiotics, and total phenols. The mechanism by which gamma radiation alters the genotype of microorganisms is the destruction of double-stranded and single-stranded DNA, resulting in changes in the genome or nucleic acid molecule, altering the antagonistic properties of microorganisms. Sensitivity to radiation is determined by the size of an organism's chromosomes, and the effect on microorganisms is primarily based on DNA or RNA disruption. Molecular analysis of gamma radiation mutants has been used to understand changes in genome composition, including downregulated genes related to secondary metabolism, cytochrome P450 s, carbohydrate-active enzymes, peptidases, and hydrophobins. Gamma radiation thus offers a promising method to induce beneficial genetic changes in microorganisms, enhancing their efficacy in the biological control of plant diseases.
Abstract The extraction of biological regulation events has been a key focus in the field of biomedical nature language processing (BioNLP). However, existing methods often encounter challenges such as cascading errors in text mining pipelines and limitations in topic coverage from the selected corpus. Fortunately, the emergence of large language models (LLMs) presents a potential solution due to their robust semantic understanding and extensive knowledge base. To explore this potential, our project at the Biomedical Linked Annotation Hackathon 8 (BLAH 8) investigates the feasibility of using LLMs to extract biological regulation events. Our findings, based on the analysis of rice literature, demonstrate the promising performance of LLMs in this task, while also highlighting several concerns that must be addressed in future LLM-based application in low-resource topic.
Objectives: Bone immune disorders are major contributors to osteoporosis development. This study aims to identify potential diagnostic markers and molecular targets for osteoporosis treatment from an immunological perspective.Method: We downloaded dataset GSE56116 from the Gene Expression Omnibus database, and identified differentially expressed genes (DEGs) between normal and osteoporosis groups. Subsequently, differentially expressed immune-related genes (DEIRGs) were identified, and a functional enrichment analysis was performed. A protein-protein interaction network was also constructed based on data from STRING database to identify hub genes. Following external validation using an additional dataset (GSE35959), effective biomarkers were confirmed using RT-qPCR and immunohistochemical (IHC) staining. ROC curves were constructed to validate the diagnostic values of the identified biomarkers. Finally, a ceRNA and a transcription factor network was constructed, and a Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed to explore the biological functions of these diagnostic markers.Results: In total, 307 and 31 DEGs and DEIRGs were identified, respectively. The enrichment analysis revealed that the DEIRGs are mainly associated with Gene Ontology terms of positive regulation of MAPK cascade, granulocyte chemotaxis, and cytokine receptor. protein–protein interaction network analysis revealed 10 hub genes: FGF8, KL, CCL3, FGF4, IL9, FGF9, BMP7, IL17RA, IL12RB2, CD40LG. The expression level of IL17RA was also found to be significantly high. RT-qPCR and immunohistochemical results showed that the expression of IL17RA was significantly higher in osteoporosis patients compared to the normal group, as evidenced by the area under the curve Area Under Curve of 0.802. Then, we constructed NEAT1-hsa-miR-128-3p-IL17RA, and SNHG1-hsa-miR-128-3p-IL17RA ceRNA networks in addition to ERF-IL17RA, IRF8-IL17RA, POLR2A-IL17RA and ERG-IL17RA transcriptional networks. Finally, functional enrichment analysis revealed that IL17RA was involved in the development and progression of osteoporosis by regulating local immune and inflammatory processes in bone tissue.Conclusion: This study identifies the immune-related gene IL17RA as a diagnostic marker of osteoporosis from an immunological perspective, and provides insight into its biological function.
Abstract Epigenetic modifications are involved in the remodeling of the tumor microenvironment (TME) and the regulation of immune response. Nonetheless, the role of histone H4 methylation (H4M) modification in the TME and immune regulation of hepatocellular carcinoma (HCC) is unknown. As a result, the purpose of this research is to discover H4M-mediated modification patterns and their effects on TME and immunologic characteristics in HCC. A total of 2305 samples were enrolled from 13 different cohorts. With the help of consensus clustering analysis, three distinct H4M modification patterns were identified. The cell-infiltrating characteristics of TME under these three patterns were highly consistent with their enriched biological processes and clinical outcome. The H4Mscore was then created using principal component analysis algorithm to quantify the H4M modification pattern of each individual tumor and was systematically correlated with representative tumor characteristics. We found that analyzing H4M modification patterns within individual tumors could predict TME infiltration, homologous recombination deficiency (HRD), intratumor heterogeneity, proliferation activity, mRNA stemness index, and prognosis. The group with a low H4Mscore had an inflamed TME phenotype and a better immunotherapy response, as well as a better survival outcome. The prognostic value of H4Mscore was validated in three internal cohorts and five external cohorts, respectively. In external immunotherapy cohorts, the low H4Mscore was also linked to an enhanced response to anti-PD-1/L1 and anti-CTLA4 immunotherapy and a better prognosis. This study revealed that H4M modification played an important role in forming TME diversity and complexity. Evaluating the H4M modification pattern of individual tumors could help us learn more about TME and develop more effective immunotherapy strategies.
Short- and medium-chain acyl-CoA synthetases catalyze similar two-step reactions in which acyl substrate and ATP bind to form an enzyme-bound acyl-adenylate, then CoA binds for formation of the acyl-CoA product. We investigated the roles of active site residues in CoA binding in acetyl-CoA synthetase (Acs) and a medium-chain acyl-CoA synthetase (Macs) that uses 2-methylbutyryl-CoA. Three highly conserved residues, Arg<sup>193</sup>, Arg<sup>528</sup>, and Arg<sup>586</sup> of <i>Methanothermobacter thermautotrophicus</i> Acs (Acs<sub>Mt</sub>), are predicted to form important interactions with the 5′- and 3′-phosphate groups of CoA. Kinetic characterization of Acs<sub>Mt</sub> variants altered at each of these positions indicates these Arg residues play a critical role in CoA binding and catalysis. The predicted CoA binding site of <i>Methanosarcina acetivorans</i> Macs (Macs<sub>Ma</sub>) is structurally more closely related to that of 4-chlorobenzoate:coenzyme A ligase (CBAL) than Acs. Alteration of Macs<sub>Ma</sub> residues Tyr<sup>460</sup>, Arg<sup>490</sup>, Tyr<sup>525</sup>, and Tyr<sup>527</sup>, which correspond to CoA binding pocket residues in CBAL, strongly affected CoA binding and catalysis without substantially affecting acyl-adenylate formation. Both enzymes discriminate between 3′-dephospho-CoA and CoA, indicating interaction between the enzyme and the 3′-phosphate group is important. Alteration of Macs<sub>Ma</sub> residues Lys<sup>461</sup> and Lys<sup>519</sup>, located at positions equivalent to Acs<sub>Mt</sub> Arg<sup>528</sup> and Arg<sup>586</sup>, respectively, had only a moderate effect on CoA binding and catalysis. Overall, our results indicate the active site architecture in Acs<sub>Mt</sub> and Macs<sub>Ma</sub> differs even though these enzymes catalyze mechanistically similar reactions. The significance of this study is that we have delineated the active site architecture with respect to CoA binding and catalysis in this important enzyme superfamily.
Chimeric antigen receptor (CAR) redirected T cells are potent therapeutic options against hematological malignancies. The current dominant manufacturing approach for CAR T cells depends on retroviral transduction. With the advent of gene editing, insertion of a CD19-CAR into the T cell receptor (TCR) alpha constant (TRAC) locus using adeno-associated viruses for gene transfer was demonstrated, and these CD19-CAR T cells showed improved functionality over their retrovirally transduced counterparts. However, clinical-grade production of viruses is complex and associated with extensive costs. Here, we optimized a virus-free genome-editing method for efficient CAR insertion into the TRAC locus of primary human T cells via nuclease-assisted homology-directed repair (HDR) using CRISPR-Cas and double-stranded template DNA (dsDNA). We evaluated DNA-sensor inhibition and HDR enhancement as two pharmacological interventions to improve cell viability and relative CAR knockin rates, respectively. While the toxicity of transfected dsDNA was not fully prevented, the combination of both interventions significantly increased CAR knockin rates and CAR T cell yield. Resulting TRAC-replaced CD19-CAR T cells showed antigen-specific cytotoxicity and cytokine production in vitro and slowed leukemia progression in a xenograft mouse model. Amplicon sequencing did not reveal significant indel formation at potential off-target sites with or without exposure to DNA-repair-modulating small molecules. With TRAC-integrated CAR+ T cell frequencies exceeding 50%, this study opens new perspectives to exploit pharmacological interventions to improve non-viral gene editing in T cells.