SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

Identification and validation of the surface proteins FIBG, PDGF-ß, and TGF-ß on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

OBJECTIVES: The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. METHODS: In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multicenter study comprising 404 individuals. In the application phase, we evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. RESULTS: Through 4D-DIA proteomic analysis, we identified 7 potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of 3 EVs surface proteins, FIBG, PDGF-ß and TGF-ß, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an AUC of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. CONCLUSIONS: Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-ß, and TGF-ß as novel diagnostic biomarkers for CRC. And the development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

Enhancing Predictions of Drug Solubility through Multidimensional Structural Characterization Exploitation.

Solubility is not only a significant physical property of molecules but also a vital factor in smallmolecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions. We also employ Selective Kernel Convolution combined with Global and Local attention mechanisms to capture molecular features context at different scales. Additionally, various descriptors are calculated to enrich the molecular representation. For the 2D and 3D structural data of molecules, we design different data augmentation strategies to enhance generalization ability and prevent the model from learning irrelevant information. Extensive experiments on benchmark and independent datasets demonstrate MSCSol's superior performance. Ablation studies further confirm the effectiveness of different modules. Interpretability analysis highlights the importance of various atomic groups and substructures for solubility and verifies that our model effectively captures functional molecular structures and higher-order knowledge. The source code and datasets are freely available at https://github.com/ZiyuFanCSU/MSCSol.

Transcriptional dynamics and regulatory function of milRNAs in Ascosphaera apis invading Apis mellifera larvae.

In the present study, small RNA (sRNA) data from Ascosphaera apis were filtered from sRNA-seq datasets from the gut tissues of A. apis-infected Apis mellifera ligustica worker larvae, which were combined with the previously gained sRNA-seq data from A. apis spores to screen differentially expressed milRNAs (DEmilRNAs), followed by trend analysis and investigation of the DEmilRNAs in relation to significant trends. Additionally, the interactions between the DEmilRNAs and their target mRNAs were verified using a dual-luciferase reporter assay. In total, 974 A. apis milRNAs were identified. The first base of these milRNAs was biased toward U. The expression of six milRNAs was confirmed by stem-loop RT-PCR, and the sequences of milR-3245-y and milR-10285-y were validated using Sanger sequencing. These miRNAs grouped into four significant trends, with the target mRNAs of DEmilRNAs involving 42 GO terms and 120 KEGG pathways, such as the fungal-type cell wall and biosynthesis of secondary metabolites. Further investigation demonstrated that 299 DEmilRNAs (novel-m0011-3p, milR-10048-y, bantam-y, etc.) potentially targeted nine genes encoding secondary metabolite-associated enzymes, while 258 (milR-25-y, milR-14-y, milR-932-x, etc.) and 419 (milR-4561-y, milR-10125-y, let-7-x, etc.) DEmilRNAs putatively targeted virulence factor-encoded genes and nine genes involved in the MAPK signaling pathway, respectively. Additionally, the interaction between ADM-B and milR-6882-x, as well as between PKIA and milR-7009-x were verified. Together, these results not only offer a basis for clarifying the mechanisms underlying DEmilRNA-regulated pathogenesis of A. apis and a novel insight into the interaction between A. apis and honey bee larvae, but also provide candidate DEmilRNA-gene axis for further investigation.

TREM2 alleviates white matter injury after traumatic brain injury in mice might be mediated by regulation of DHCR24/LXR pathway in microglia.

BACKGROUND: White matter injury (WMI) is an important pathological process after traumatic brain injury (TBI). The correlation between white matter functions and the myeloid cells expressing triggering receptor-2 (TREM2) has been convincingly demonstrated. Moreover, a recent study revealed that microglial sterol metabolism is crucial for early remyelination after demyelinating diseases. However, the potential roles of TREM2 expression and microglial sterol metabolism in WMI after TBI have not yet been explored. METHODS: Controlled cortical injury was induced in both wild-type (WT) and TREM2 depletion (TREM2 KO) mice to simulate clinical TBI. COG1410 was used to upregulate TREM2, while PLX5622 and GSK2033 were used to deplete microglia and inhibit the liver X receptor (LXR), respectively. Immunofluorescence, Luxol fast blue staining, magnetic resonance imaging, transmission electron microscopy, and oil red O staining were employed to assess WMI after TBI. Neurological behaviour tests and electrophysiological recordings were utilized to evaluate cognitive functions following TBI. Microglial cell sorting and transcriptomic sequencing were utilized to identify alterations in microglial sterol metabolism-related genes, while western blot was conducted to validate the findings. RESULTS: TREM2 expressed highest at 3 days post-TBI and was predominantly localized to microglial cells within the white matter. Depletion of TREM2 worsened aberrant neurological behaviours, and this phenomenon was mediated by the exacerbation of WMI, reduced renewal of oligodendrocytes, and impaired phagocytosis ability of microglia after TBI. Subsequently, the upregulation of TREM2 alleviated WMI, promoted oligodendrocyte regeneration, and ultimately facilitated the recovery of neurological behaviours after TBI. Finally, the expression of DHCR24 increased in TREM2 KO mice after TBI. Interestingly, TREM2 inhibited DHCR24 and upregulated members of the LXR pathway. Moreover, LXR inhibition could partially reverse the effects of TREM2 upregulation on electrophysiological activities. CONCLUSIONS: We demonstrate that TREM2 has the potential to alleviate WMI following TBI, possibly through the DHCR24/LXR pathway in microglia.

Extracellular vesicles-derived CXCL4 is a candidate serum tumor biomarker for colorectal cancer.

Extracellular vesicles (EVs) were promising circulating biomarkers for multiple diseases, but whether serum EVs-derived proteins could be used as a reliable tumor biomarker for colorectal cancer (CRC) remained inconclusive. In this study, we identified CXCL4 by a 4D data-independent acquisition-based quantitative proteomics assay of serum EVs-derived proteins in 40 individuals and subsequently analyzed serum EVs-derived CXCL4 levels by ELISA in 2 cohorts of 749 individuals. The results revealed that EVs-derived CXCL4 levels were dramatically elevated in CRC patients than in benign colorectal polyp patients or healthy controls (HC). Furthermore, receiver operating characteristic curves revealed that EVs-derived CXCL4 exhibited superior diagnostic performance with area under the curve of 0.948 in the training cohort. Additionally, CXCL4 could effectively distinguish CRC in stage I/II from HC. Notably, CRC patients with high levels of EVs-derived CXCL4 have shorter 2-year progression-free survival than those with low levels. Overall, our findings demonstrated that serum EVs-derived CXCL4 was a candidate diagnostic and prognostic biomarker for CRC.

Feasibility and tolerability of eribulin-based chemotherapy versus other chemotherapy regimens for patients with metastatic triple-negative breast cancer: a single-centre retrospective study.

Background: Patients with Triple-negative breast cancer (TNBC) face a poor prognosis and limited therapeutic options. Current data on eribulin usage to treat TNBC is scarce. Therefore, we sought to compare the feasibility and tolerability of eribulin-based regimens with other chemotherapy regimens in patients with TNBC. Method: This retrospective study was conducted at Fujian Medical University Cancer Hospital and included 159 patients with TNBC enrolled between October 2011 and January 2023. Patients underwent treatment with eribulin-based and other chemotherapy regimens. The study's primary endpoints were progression-free survival (PFS) and overall survival (OS), while its secondary endpoint was objective response rate (ORR), disease control rate (DCR), and safety. Tumour response was assessed using RECIST V.1.1 criteria. Results: Of the 159 participants in the study, 42 individuals (26.4%) received treatment with eribulin, whereas 117 participants (73.6%) were administered alternative chemotherapy regimens, which included nab-paclitaxel-based therapy (n = 45) and platinum-based therapy (n = 51). The follow-up period for all patients ended on 31 December 2022, and the median follow-up time was 18.3 months (range:0.7-27.5). Following propensity score matching (PSM), eribulin-based treatment resulted in longer median progression-free survival compared to platinum-based (hazard ratio (HR) = 0.41, p = 0.006), nab-paclitaxel-based (hazard ratio = 0.36, p = 0.001) and other chemotherapy (HR = 0.39, p < 0.001). Also, eribulin induced a remarkable prolongation of the median overall survival duration in all three comparative groups. The group receiving eribulin treatment showed significantly reduced incidences of any grade of anaemia, peripheral neuropathy, nausea and vomiting, and hair loss compared to other chemotherapy groups. Conclusion: For the salvage treatment of advanced TNBC, treatment with eribulin produced longer median PFS and OS than other chemotherapy regimens, with a well-tolerated safety profile. Therefore, further investigation of eribulin-based treatment in larger randomized trials for patients with advanced TNBC is warranted.

RNA methylation-related genes INHBB and SOWAHA are associated with MSI status in colorectal cancer patients and may serve as prognostic markers for predicting immunotherapy efficacy.

The role of RNA methylation is vital in the advancement and spread of tumors. However, its exact role in microsatellite instability in colorectal cancer (CRC) is still not fully understood. To address this gap in knowledge, this study investigated the impact of genes associated with RNA methylation on the prognosis and response to immunotherapy in individuals diagnosed with low microsatellite instability (MSI-L) or microsatellite stable (MSS) CRC. The differentially expressed genes (DEGs) in two groups of patients: those with high microsatellite instability (MSI-H) and those with MSI-L/MSS was thoroughly investigated and compared with aims of exploring the association between them and the 60 RNA methylation regulators. We employed these genes and developed an MSI-RMscore to establish a risk signature capable of forecasting patient outcomes. Furthermore, an investigation of the immunophenotypic traits was conducted encompassing patients categorized as high-risk and low-risk. By combining the MSI-RMscore and clinicopathological features, a predictive nomogram was developed, which was subsequently validated using the GEO database. Furthermore, immunohistochemistry was employed to establish the correlation between INHBB and SOWAHA and the MSI status, as well as patient prognosis. Our findings indicated that the high-risk subgroup exhibited unfavorable overall survival rates, reduced responsiveness to immune checkpoint blockers, elevated estimate scores, and increased infiltration of macrophages and fibroblasts. We also confirmed that INHBB and SOWAHA were associated with CRC patient prognosis and MSI status, as well as immunotherapy response. These findings suggest that targeting INHBB and SOWAHA could be a promising strategy to enhance patient responsiveness to immunotherapy.

Macrophages Promote Subtype Conversion and Endocrine Resistance in Breast Cancer.

BACKGROUND: The progression of tumors from less aggressive subtypes to more aggressive states during metastasis poses challenges for treatment strategies. Previous studies have revealed the molecular subtype conversion between primary and metastatic tumors in breast cancer (BC). However, the subtype conversion during lymph node metastasis (LNM) and the underlying mechanism remains unclear. METHODS: We compared clinical subtypes in paired primary tumors and positive lymph nodes (PLNs) in BC patients and further validated them in the mouse model. Bioinformatics analysis and macrophage-conditioned medium treatment were performed to investigate the role of macrophages in subtype conversion. RESULTS: During LNM, hormone receptors (HRs) were down-regulated, while HER2 was up-regulated, leading to the transformation of luminal A tumors towards luminal B tumors and from luminal B subtype towards HER2-enriched (HER2-E) subtype. The mouse model demonstrated the elevated levels of HER2 in PLN while retaining luminal characteristics. Among the various cells in the tumor microenvironment (TME), macrophages were the most clinically relevant in terms of prognosis. The treatment of a macrophage-conditioned medium further confirmed the downregulation of HR expression and upregulation of HER2 expression, inducing tamoxifen resistance. Through bioinformatics analysis, MNX1 was identified as a potential transcription factor governing the expression of HR and HER2. CONCLUSION: Our study revealed the HER2-E subtype conversion during LNM in BC. Macrophages were the crucial cell type in TME, inducing the downregulation of HR and upregulation of HER2, probably via MNX1. Targeting macrophages or MNX1 may provide new avenues for endocrine therapy and targeted treatment of BC patients with LNM.

A real-world study of the effectiveness and safety of apatinib-based regimens in metastatic triple-negative breast cancer.

PURPOSE: This investigation sought to examine the efficacy and safety of low-dose apatinib used alongside chemotherapy in the clinical management of patients with metastatic triple-negative breast cancer (TNBC) within a real-world setting, whilst comparing the outcomes with those treated solely with chemotherapy. METHODS: This case series study analyzed clinical data and treatment outcomes of 163 patients with metastatic TNBC who underwent rescue treatment at the Medical Oncology Department of Clinical Oncology, Fujian Cancer Hospital, School of Fujian Medical University, China, between October 2011 and January 2023. All the patients underwent rescue treatment with either chemotherapy alone or apatinib (250 mg/day) combined with chemotherapy. The study's primary outcome was progression-free survival (PFS), whereas the secondary outcomes included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety profiles. RESULTS: The study was designed to compare two groups [1]. Out of the 163 TNBC patients who participated in the study, 107 individuals (65.6%) received treatment based on chemotherapy, whereas 56 patients (34.4%) were given treatment based on a combination of low-dose apatinib (250 mg/day) and other treatments, including chemotherapy. After propensity score matching (PSM), the objective response rate (ORR) and disease control rate (DCR) of patients with advanced triple-negative breast cancer (TNBC) who received apatinib-based treatment were 50.0 and 90.0%, respectively, while they were 6.7 and 20.0%, respectively, for the chemotherapy-based group (P < 0.001). The group that received apatinib-based treatment showed superior results in both PFS and OS compared to the group that received chemotherapy. The median PFS and OS for the apatinib-based group were 7.8 and 20.3 months, respectively, while they were only 2.2 months and 9.0 months, respectively, for the chemotherapy-based group (P < 0.001) [2]. Patients who were administered combo therapies, including PD-1 inhibitors, were excluded. In total, 97 patients received chemotherapy alone, while 34 patients were treated with apatinib in combination with chemotherapy. After propensity score matching (PSM), the ORR and DCR for the total group who received combo therapies were 44.4 and 81.5%, respectively, while they were 11.1 and 22.2%, respectively, for the chemotherapy alone group (P < 0.001). The group receiving both apatinib and chemotherapy displayed notable advantages over the group solely receiving chemotherapy in regards to PFS and OS for the entirety of the population. The PFS was found to be 7.8 months in comparison to 2.1 months (P < 0.001) and the OS was 21.1 months in contrast to 9.0 months (P < 0.001). Apatinib combined with chemotherapy induced grade 3/4 hematological toxicities, including neutropenia (8.8%) and thrombocytopenia (2.9%). Additionally, non-hematological toxicities were commonly observed, such as Hand-foot syndrome (35.3%), proteinuria (26.5%), hypertension (61.8%), higher alanine aminotransferase levels (26.5%), and fatigue (35.3%). The most frequent non-hematological grade 3/4 toxicities were Hand-foot syndrome (2.9%) and hypertension (5.9%). The study did not report any fatal adverse effects. CONCLUSIONS: The combination of low-dose apatinib with chemotherapy has proven to be more effective than chemotherapy alone in treating metastatic triple-negative breast cancer (TNBC). Additionally, the occurrence of grade 3/4 non-hematologic toxicities was significantly lower compared to the recommended dose of apatinib.

Intestine bacterial community affects the growth of the Pacific white shrimp (Litopenaeus vannamei).

Increasing evidence suggests that intestine microorganisms are closely related to shrimp growth, but there is no existing experiment to prove this hypothesis. Here, we compared the intestine bacterial community of fast- and slow-growing shrimp at the same developmental stage with a marked difference in body size. Our results showed that the intestine bacterial communities of slow-growing shrimp exhibited less diversity but were more heterogeneous than those of fast-growing shrimp. Uncultured_bacterium_g_Candidatus Bacilloplasma, Tamlana agarivorans, Donghicola tyrosinivorans, and uncultured_bacterium_f_Flavobacteriaceae were overrepresented in the intestines of fast-growing shrimp, while Shimia marina, Vibrio sp., and Vibrio campbellii showed the opposite trends. We further found that the bacterial community composition was significantly correlated with shrimp length, and some bacterial species abundances were found to be significantly correlated with shrimp weight and length, including T. agarivorans and V. campbellii, which were chosen as indicators for a reverse gavage experiment. Finally, T. agarivorans was found to significantly promote shrimp growth after the experiment. Collectively, these results suggest that intestine bacterial community could be important factors in determining the growth of shrimp, indicating that specific bacteria could be tested in further studies against shrimp growth retardation. KEY POINTS: • A close relationship between intestine bacterial community and shrimp growth was proven by controllable experiments. • The bacterial signatures of the intestine were markedly different between slow- and fast-growing shrimp, and the relative abundances of some intestine bacterial species were correlated significantly with shrimp body size. • Reverse gavage by Tamlana agarivorans significantly promoted shrimp growth.

Diverse Regulatory Manners and Potential Roles of lncRNAs in the Developmental Process of Asian Honey Bee (Apis cerana) Larval Guts.

Long non-coding RNAs (lncRNAs) are crucial modulators in a variety of biological processes, such as gene expression, development, and immune defense. However, little is known about the function of lncRNAs in the development of Asian honey bee (Apis cerana) larval guts. Here, on the basis of our previously obtained deep-sequencing data from the 4-, 5-, and 6-day-old larval guts of A. cerana workers (Ac4, Ac5, and Ac6 groups), an in-depth transcriptome-wide investigation was conducted to decipher the expression pattern, regulatory manners, and potential roles of lncRNAs during the developmental process of A. cerana worker larval guts, followed by the verification of the relative expression of differentially expressed lncRNAs (DElncRNAs) and the targeting relationships within a competing endogenous RNA (ceRNA) axis. In the Ac4 vs. Ac5 and Ac5 vs. Ac6 comparison groups, 527 and 498 DElncRNAs were identified, respectively, which is suggestive of the dynamic expression of lncRNAs during the developmental process of larval guts. A cis-acting analysis showed that 330 and 393 neighboring genes of the aforementioned DElncRNAs were respectively involved in 29 and 32 functional terms, such as cellular processes and metabolic processes; these neighboring genes were also respectively engaged in 246 and 246 pathways such as the Hedgehog signaling pathway and the Wnt signaling pathway. Additionally, it was found that 79 and 76 DElncRNAs as potential antisense lncRNAs may, respectively, interact with 72 and 60 sense-strand mRNAs. An investigation of competing endogenous RNA (ceRNA) networks suggested that 75 (155) DElncRNAs in the Ac4 vs. Ac5 (Ac5 vs. Ac6) comparison group could target 7 (5) DEmiRNAs and further bind to 334 (248) DEmRNAs, which can be annotated to 33 (29) functional terms and 186 (210) pathways, including 12 (16) cellular- and humoral-immune pathways (lysosome pathway, necroptosis, MAPK signaling pathway, etc.) and 11 (10) development-associated signaling pathways (Wnt, Hippo, AMPK, etc.). The RT-qPCR detection of five randomly selected DElncRNAs confirmed the reliability of the used sequencing data. Moreover, the results of a dual-luciferase reporter assay were indicative of the binding relationship between MSTRG.11294.1 and miR-6001-y and between miR-6001-y and ncbi_107992440. These results demonstrate that DElncRNAs are likely to modulate the developmental process of larval guts via the regulation of the source genes' transcription, interaction with mRNAs, and ceRNA networks. Our findings not only yield new insights into the developmental mechanism underlying A. cerana larval guts, but also provide a candidate ceRNA axis for further functional dissection.

Examining the roles of experiential avoidance and cognitive fusion on the effects from mindfulness to athlete burnout: A longitudinal study.

OBJECTIVES: Athlete burnout is a maladaptive outcome that is potentially detrimental for performance and wellbeing. Cross-sectional evidence suggests that mindfulness might be associated with athlete burnout via experiential avoidance and cognitive fusion. In the current study, we extend knowledge of these hypothesized mediational pathways using a longitudinal design. METHODS: Data was collected at three occasions with a three-month interval. A final sample of 280 elite Chinese athletes aged 15-32 years (Mage = 19.13; SD = 2.92; Female = 130) reported their mindfulness at Time 1, experiential avoidance and cognitive fusion at Time 2, and athlete burnout at Time 3. Structural equation modelling was adopted to examine the mediating roles of experiential avoidance and cognitive fusion on the effects from mindfulness to athlete burnout. RESULTS: We found statistically meaningful directs effects from mindfulness (Time 1) to experiential avoidance and cognitive fusion (Time 2), which in turn influenced athlete burnout (Time 3). However, the direct effect from mindfulness at Time 1 to athlete burnout at Time 3 was non-significant. The indirect effects of experiential avoidance and cognitive fusion on the effects from mindfulness to athlete burnout were significant, providing longitudinal evidence that these two variables contribute meaningfully to the mindfulness-burnout pathway. CONCLUSION: With initial evidence for the mediating effects of experiential avoidance and cognitive fusion, future studies could consider using experimental designs to examine the potential changing mechanisms of mindfulness on reducing athlete burnout.

Large-scale predicting protein functions through heterogeneous feature fusion.

As the volume of protein sequence and structure data grows rapidly, the functions of the overwhelming majority of proteins cannot be experimentally determined. Automated annotation of protein function at a large scale is becoming increasingly important. Existing computational prediction methods are typically based on expanding the relatively small number of experimentally determined functions to large collections of proteins with various clues, including sequence homology, protein-protein interaction, gene co-expression, etc. Although there has been some progress in protein function prediction in recent years, the development of accurate and reliable solutions still has a long way to go. Here we exploit AlphaFold predicted three-dimensional structural information, together with other non-structural clues, to develop a large-scale approach termed PredGO to annotate Gene Ontology (GO) functions for proteins. We use a pre-trained language model, geometric vector perceptrons and attention mechanisms to extract heterogeneous features of proteins and fuse these features for function prediction. The computational results demonstrate that the proposed method outperforms other state-of-the-art approaches for predicting GO functions of proteins in terms of both coverage and accuracy. The improvement of coverage is because the number of structures predicted by AlphaFold is greatly increased, and on the other hand, PredGO can extensively use non-structural information for functional prediction. Moreover, we show that over 205 000 ($\sim $100%) entries in UniProt for human are annotated by PredGO, over 186 000 ($\sim $90%) of which are based on predicted structure. The webserver and database are available at http://predgo.denglab.org/.

USP18 enhances the resistance of BRAF-mutated melanoma cells to vemurafenib by stabilizing cGAS expression to induce cell autophagy.

This study aims to discern the possible molecular mechanism of the effect of ubiquitin-specific peptidase 18 (USP18) on the resistance to BRAF inhibitor vemurafenib in BRAF V600E mutant melanoma by regulating cyclic GMP-AMP synthase (cGAS). The cancer tissues of BRAF V600E mutant melanoma patients before and after vemurafenib treatment were collected, in which the protein expression of USP18 and cGAS was determined. A BRAF V600E mutant human melanoma cell line (A2058R) resistant to vemurafenib was constructed with its viability, apoptosis, and autophagy detected following overexpression and depletion assays of USP18 and cGAS. Xenografted tumors were transplanted into nude mice for in vivo validation. Bioinformatics analysis showed that the expression of cGAS was positively correlated with USP18 in melanoma, and USP18 was highly expressed in melanoma. The expression of cGAS and USP18 was up-regulated in cancer tissues of vemurafenib-resistant patients with BRAF V600E mutant melanoma. Knockdown of cGAS inhibited the resistance to vemurafenib in A2058R cells and the protective autophagy induced by vemurafenib in vitro. USP18 could deubiquitinate cGAS to promote its protein stability. In vivo experimentations confirmed that USP18 promoted vemurafenib-induced protective autophagy by stabilizing cGAS protein, which promoted resistance to vemurafenib in BRAF V600E mutant melanoma cells. Collectively, USP18 stabilizes cGAS protein expression through deubiquitination and induces autophagy of melanoma cells, thereby promoting the resistance to vemurafenib in BRAF V600E mutant melanoma.

ABLNCPP: Attention Mechanism-Based Bidirectional Long Short-Term Memory for Noncoding RNA Coding Potential Prediction.

With the continuous development of ribosome profiling, sequencing technology, and proteomics, evidence is mounting that noncoding RNA (ncRNA) may be a novel source of peptides or proteins. These peptides and proteins play crucial roles in inhibiting tumor progression and interfering with cancer metabolism and other essential physiological processes. Therefore, identifying ncRNAs with coding potential is vital to ncRNA functional research. However, existing studies perform well in classifying ncRNAs and mRNAs, and no research has been explicitly raised to distinguish whether ncRNA transcripts have coding potential. For this reason, we propose an attention mechanism-based bidirectional LSTM network called ABLNCPP to assess the coding possibility of ncRNA sequences. Considering the sequential information loss in previous methods, we introduce a novel nonoverlapping trinucleotide embedding (NOLTE) method for ncRNAs to obtain embeddings containing sequential features. The extensive evaluations show that ABLNCPP outperforms other state-of-the-art models. In general, ABLNCPP overcomes the bottleneck of ncRNA coding potential prediction and is expected to provide valuable contributions to cancer discovery and treatment in the future. The source code and data sets are freely available at https://github.com/YinggggJ/ABLNCPP.

Revealing the roles of TLR7, a nucleic acid sensor for COVID-19 in pan-cancer.

Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 (or its causing virus SARS-CoV-2) has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We applied the computational framework in a few databases, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), lung gene expression data of mice infected with SARS-CoV-2, and the like. As a result, TLR7 expression was found to be higher in the lung of mice infected with SARS-CoV-2 than that in the control group. The analysis in the Opentargets database also confirmed the association between TLR7 and COVID-19. There are also a few exciting findings in cancers. First, the most common type of TLR7 was "Missense" at the genomic level. Second, TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types compared to normal tissues, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with mismatch repair (MMR), microsatellite instability (MSI), and tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Third, abnormal expression of TLR7 could predict the survival of Brain Lower Grade Glioma (LGG), Lung adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), and Testicular Germ Cell Tumors (TGCT) patients, respectively. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.

DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response.

MOTIVATION: The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. RESULTS: In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. AVAILABILITY AND IMPLEMENTATION: https://github.com/Hhhzj-7/DeepCoVDR.

Luman regulates the activity of the LHCGR promoter.

Testosterone in male mammals is mainly secreted by testicular Leydig cells, and its secretion process is regulated by the hypothalamic-pituitary-gonadal axis. After receiving the luteinizing hormone (LH) stimulus signal, the lutropin/choriogonadotropin receptor (LHCGR) on the Leydig cell membrane transfers the signal into the cell and finally increases the secretion of testosterone by upregulating the expression of steroid hormone synthase. In previous experiments, we found that interfering with the expression of the Luman protein can significantly increase testosterone secretion in MLTC-1 cells. In this experiment, we found that knockdown of Luman in MLTC-1 cells significantly increased the concentration of cAMP and upregulated the expression of AC and LHCGR. Moreover, an analysis of the activity of the LHCGR promoter by a dual luciferase reporter system showed that knockdown of Luman increased the activity of the LHCGR promoter. Therefore, we believe that knockdown of Luman increased the activity of the LHCGR promoter and upregulated the expression of LHCGR, thereby increasing the concentration of intracellular cAMP and ultimately leading to an increase of testosterone secretion by MLTC-1 cells.