HAPPI: an online database of comprehensive human annotated and predicted protein interactions

<p>Abstract</p> <p>Background</p> <p>Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.</p> <p>Results</p> <p>We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at <url>http://bio.informatics.iupui.edu/HAPPI/</url>. The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.</p> <p>Conclusion</p> <p>HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.</p

Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range of unseen classes, it may struggle when domain gap increases, such as in fine-grained classification and satellite image segmentation. To address this limitation, we propose Retrieval-enhanced Prompt learning (RePrompt), which introduces retrieval mechanisms to cache the knowledge representations from downstream tasks. we first construct a retrieval database from training examples, or from external examples when available. We then integrate this retrieval-enhanced mechanism into various stages of a simple prompt learning baseline. By referencing similar samples in the training set, the enhanced model is better able to adapt to new tasks with few samples. Our extensive experiments over 15 vision datasets, including 11 downstream tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate that RePrompt achieves considerably improved performance. Our proposed approach provides a promising solution to the challenges faced by prompt learning when domain gap increases. The code and models will be available

Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications

poster abstractWith the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications

Evaluation of genetic susceptibility of common variants in CACNA1D with schizophrenia in Han Chinese

The heritability of schizophrenia (SCZ) has been estimated to be as high as 80%, suggesting that genetic factors may play an important role in the etiology of SCZ. Cav1.2 encoded by CACNA1C and Cav1.3 encoded by CACNA1D are dominant calcium channel-forming subunits of L-type Voltage-dependent Ca(2+) channels, expressed in many types of neurons. The CACNA1C has been consistently found to be a risk gene for SCZ, but it is unknown for CACNA1D. To investigate the association of CACNA1D with SCZ, we designed a two-stage case-control study, including a testing set with 1117 cases and 1815 controls and a validation set with 1430 cases and 4295 controls in Han Chinese. A total of selected 97 tag single nucleotide polymorphisms (SNPs) in CACNA1D were genotyped, and single-SNP association, imputation analysis and gender-specific association analyses were performed in the two independent datasets. None was found to associate with SCZ. Further genotype and haplotype association analyses indicated a similar pattern in the two-stage study. Our findings suggested CACNA1D might not be a risk gene for SCZ in Han Chinese population, which add to the current state of knowledge regarding the susceptibility of CACNA1D to SCZ

Evaluation of voltage-dependent calcium channel gamma gene families identified several novel potential susceptible genes to schizophrenia

Voltage-gated L-type calcium channels (VLCC) are distributed widely throughout the brain. Among the genes involved in schizophrenia (SCZ), genes encoding VLCC subunits have attracted widespread attention. Among the four subunits comprising the VLCC (α − 1, α −2/δ, β, and γ), the γ subunit that comprises an eight-member protein family is the least well understood. In our study, to further investigate the risk susceptibility by the γ subunit gene family to SCZ, we conducted a large-scale association study in Han Chinese individuals. The SNP rs17645023 located in the intergenic region of CACNG4 and CACNG5 was identified to be significantly associated with SCZ (OR = 0.856, P = 5.43 × 10(−5)). Similar results were obtained in the meta-analysis with the current SCZ PGC data (OR = 0.8853). We also identified a two-SNP haplotype (rs10420331-rs11084307, P = 1.4 × 10(−6)) covering the intronic region of CACNG8 to be significantly associated with SCZ. Epistasis analyses were conducted, and significant statistical interaction (OR = 0.622, P = 2.93 × 10(−6), P(perm) < 0.001) was observed between rs192808 (CACNG6) and rs2048137 (CACNG5). Our results indicate that CACNG4, CACNG5, CACNG6 and CACNG8 may contribute to the risk of SCZ. The statistical epistasis identified between CACNG5 and CACNG6 suggests that there may be an underlying biological interaction between the two genes

Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein-coding genes with human aging. The relations of microRNA expression with age and age-related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole-blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P \u3c 3.3 x 10(-4) (Bonferroni-corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age-associated mRNA expression possibly driven by age-associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict \u27microRNA age\u27 that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Deltaage) and found that Deltaage was associated with all-cause mortality (hazards ratio 1.1 per year difference, P = 4.2 x 10(-5) adjusted for sex and chronological age). Additionally, Deltaage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole-blood microRNA expression profiling. Age-associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age-related diseases. Wiley and Sons Ltd

Effect of acupuncture inclusion in the enhanced recovery after surgery protocol on tumor patient gastrointestinal function: a systematic review and meta-analysis of randomized controlled studies

IntroductionAcupuncture has been shown to be effective in restoring gastrointestinal function in tumor patients receiving the enhanced recovery after surgery (ERAS) protocol. The present systematic review and meta-analysis aimed to evaluate the rationality and efficacy of integrating acupuncture in the ERAS strategy to recuperate gastrointestinal function.MethodsWe searched eleven databases for relevant randomized clinical trials (RCTs) of acupuncture for the treatment of gastrointestinal dysfunction in tumor patients treated with the ERAS protocol. The quality of each article was assessed using the Cochrane Collaboration risk of bias criteria and the modified Jadad Scale. As individual symptoms, the primary outcomes were time to postoperative oral food intake, time to first flatus, time to first distension and peristaltic sound recovery time (PSRT). Pain control, adverse events, and acupoint names reported in the included studies were also investigated.ResultsOf the 211 reviewed abstracts, 9 studies (702 patients) met eligibility criteria and were included in the present systematic review and meta‑analysis. Compared to control groups, acupuncture groups showed a significant reduction in time to postoperative oral food intake [standardized mean difference (SMD) = -0.77, 95% confidence interval (CI) -1.18 to -0.35], time to first flatus (SMD=-0.81, 95% CI -1.13 to -0.48), time to first defecation (SMD=-0.91, 95% CI -1.41 to -0.41, PSRT (SMD=-0.92, 95% CI -1.93 to 0.08), and pain intensity (SMD=-0.60, 95% CI -0.83 to -0.37).The Zusanli (ST36) and Shangjuxu (ST37) acupoints were used in eight of the nine included studies. Adverse events related to acupuncture were observed in two studies, and only one case of bruising was reported. DiscussionThe present systematic review and meta‑analysis suggested that acupuncture significantly improves recovery of gastrointestinal function and pain control in tumor patients receiving the ERAS protocol compared to the control group. Moreover, ST36 and ST37 were the most frequently used acupoints. Although the safety of acupuncture was poorly described in the included studies, the available data suggested that acupuncture is a safe treatment with only mild side effects. These findings provide evidence-based recommendations for the inclusion of acupuncture in the ERAS protocol for tumor patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/ PROSPERO, identifier CRD42023430211

Benserazide, a cystathionine beta-synthase (CBS) inhibitor, potentially enhances the anticancer effects of paclitaxel via inhibiting the S-sulfhydration of SIRT1 and the HIF1-α/VEGF pathway

Cancer targeted therapy is essential to minimize damage to normal cells and improve treatment outcomes. The elevated activity of Cystathionine beta-synthase (CBS), an enzyme responsible for producing endogenous hydrogen sulfide (H2S), plays a significant role in promoting tumor growth, invasiveness, and metastatic potential. Consequently, the selective inhibition of CBS could represent a promising therapeutic strategy for cancer. Currently, there is much interest in combining paclitaxel with other drugs for cancer treatment. This study aimed to investigate the efficacy of combining benserazide, a CBS inhibitor, with paclitaxel in treating tumors. Firstly, we demonstrated CBS is indeed involved in the progression of multiple cancers. Then it was observed that the total binding free energy between the protein and the small molecule is −98.241 kJ/mol. The release of H2S in the group treated with 100 μM benserazide was reduced by approximately 90% compared to the negative control, and the thermal denaturation curve of the complex protein shifted to the right, suggesting that benserazide binds to and blocks the CBS protein. Next, it was found that compared to paclitaxel monotherapy, the combination of benserazide with paclitaxel demonstrated stronger antitumor activity in KYSE450, A549, and HCT8 cells, accompanied by reduced cell viability, cell migration and invasion, as well as diminished angiogenic and lymphangiogenic capabilities. In vivo studies showed that the combined administration of benserazide and paclitaxel significantly reduced the volume and weight of axillary lymph nodes in comparison to the control group and single administration group. Further mechanistic studies revealed that the combination of benserazide and paclitaxel significantly suppressed the S-sulfhydration of SIRT1 protein, thereby inhibiting the expression of SIRT1 protein and activating SIRT1 downstream Notch1/Hes1 signaling pathway in KYSE450, A549, and HCT8 cells. Meanwhile, we observed that benserazide combined with paclitaxel induced a more significant downregulation of HIF-1α, VEGF-A, VEGF-C, and VEGF-D proteins expression levels in KYSE450, A549, and HCT8 cells compared to paclitaxel alone. These findings indicated that benserazide enhances the anticancer effects of paclitaxel via inhibiting the S-sulfhydration of SIRT1 and down-regulating HIF-1α/VEGF signaling pathway. This study suggests that benserazide may have potential as a chemosensitizer in cancer treatment