AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.

Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder.

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

RNAenrich: a web server for non-coding RNA enrichment.

MOTIVATION: With the rapid advances of RNA sequencing and microarray technologies in non-coding RNA (ncRNA) research, functional tools that perform enrichment analysis for ncRNAs are needed. On the one hand, because of the rapidly growing interest in circRNAs, snoRNAs, and piRNAs, it is essential to develop tools for enrichment analysis for these newly emerged ncRNAs. On the other hand, due to the key role of ncRNAs' interacting target in the determination of their function, the interactions between ncRNA and its corresponding target should be fully considered in functional enrichment. Based on the ncRNA-mRNA/protein-function strategy, some tools have been developed to functionally analyze a single type of ncRNA (the majority focuses on miRNA); in addition, some tools adopt predicted target data and lead to only low-confidence results. RESULTS: Herein, an online tool named RNAenrich was developed to enable the comprehensive and accurate enrichment analysis of ncRNAs. It is unique in (i) realizing the enrichment analysis for various RNA types in humans and mice, such as miRNA, lncRNA, circRNA, snoRNA, piRNA, and mRNA; (ii) extending the analysis by introducing millions of experimentally validated data of RNA-target interactions as a built-in database; and (iii) providing a comprehensive interacting network among various ncRNAs and targets to facilitate the mechanistic study of ncRNA function. Importantly, RNAenrich led to a more comprehensive and accurate enrichment analysis in a COVID-19-related miRNA case, which was largely attributed to its coverage of comprehensive ncRNA-target interactions. AVAILABILITY AND IMPLEMENTATION: RNAenrich is now freely accessible at https://idrblab.org/rnaenr/.

Elevated CHCHD4 orchestrates mitochondrial oxidative phosphorylation to disturb hypoxic pulmonary hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a highly prevalent cardiopulmonary disorder characterized by vascular remodeling and increased resistance in pulmonary artery. Mitochondrial coiled-coil-helix-coiled-coil-helix domain (CHCHD)-containing proteins have various important pathophysiological roles. However, the functional roles of CHCHD proteins in hypoxic PAH is still ambiguous. Here, we aimed to investigate the role of CHCHD4 in hypoxic PAH and provide new insight into the mechanism driving the development of PAH. METHODS: Serotype 1 adeno-associated viral vector (AAV) carrying Chchd4 was intratracheally injected to overexpress CHCHD4 in Sprague Dawley (SD) rats. The Normoxia groups of animals were housed at 21% O2. Hypoxia groups were housed at 10% O2, for 8 h/day for 4 consecutive weeks. Hemodynamic and histological characteristics are investigated in PAH. Primary pulmonary artery smooth muscle cells of rats (PASMCs) are used to assess how CHCHD4 affects proliferation and migration. RESULTS: We found CHCHD4 was significantly downregulated among CHCHD proteins in hypoxic PASMCs and lung tissues from hypoxic PAH rats. AAV1-induced CHCHD4 elevation conspicuously alleviates vascular remodeling and pulmonary artery resistance, and orchestrates mitochondrial oxidative phosphorylation in PASMCs. Moreover, we found overexpression of CHCHD4 impeded proliferation and migration of PASMCs. Mechanistically, through lung tissues bulk RNA-sequencing (RNA-seq), we further identified CHCHD4 modulated mitochondrial dynamics by directly interacting with SAM50, a barrel protein on mitochondrial outer membrane surface. Furthermore, knockdown of SAM50 reversed the biological effects of CHCHD4 overexpression in isolated PASMCs. CONCLUSIONS: Collectively, our data demonstrated that CHCHD4 elevation orchestrates mitochondrial oxidative phosphorylation and antagonizes aberrant PASMC cell growth and migration, thereby disturbing hypoxic PAH, which could serve as a promising therapeutic target for PAH treatment.

Understanding common human driving semantics for autonomous vehicles.

Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.

DrugMAP: molecular atlas and pharma-information of all drugs.

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis.

Background: Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays. Methods: Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages "clusterProfiler" and "GSVA" were utilized for enrichment analysis. Moreover, the transcription factor (TF)-DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset. Results: A total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF-DEG regulatory network was constructed, and 13 significant TF-DEG pairs were finally identified. Conclusion: Our study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

ConSIG: consistent discovery of molecular signature from OMIC data.

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Biological activities of drug inactive ingredients.

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods.

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.

Cardiovascular-specific PSEN1 deletion leads to abnormalities in calcium homeostasis.

Mutations of PSEN1 have been reported in dilated cardiomyopathy pedigrees. Understanding the effects and mechanisms of PSEN1 in cardiomyocytes might have important implications for treatment of heart diseases. Here, we showed that PSEN1 was downregulated in ischemia-induced failing hearts. Functionally, cardiovascular specific PSEN1 deletion led to spontaneous death of the mice due to cardiomyopathy. At the age of 11 months, the ratio of the heart weight/body weight was slightly lower in the Sm22a-PSEN1-KO mice compared with that of the WT mice. Echocardiography showed that the percentage of ejection fraction and fractional shortening was significantly reduced in the Sm22a-PSEN1-KO group compared with the percent of these measures in the WT group, indicating that PSEN1-KO resulted in heart failure. The abnormally regulated genes resulted from PSEN1-KO were detected to be enriched in muscle development and dilated cardiomyopathy. Among them, several genes encode Ca2+ ion channels, promoting us to investigate the effects of PSEN1 KO on regulation of Ca2+ in isolated adult cardiomyocytes. Consistently, in isolated adult cardiomyocytes, PSEN1-KO increased the concentration of cytosolic Ca2+ and reduced Ca2+ concentration inside the sarcoplasmic reticulum (SR) lumen at the resting stage. Additionally, SR Ca2+ was decreased in the failing hearts of WT mice, but with the lowest levels observed in the failing hearts of PSEN1 knockout mice. These results indicate that the process of Ca2+ release from SR into cytoplasm was affected by PSEN1 KO. Therefore, the abnormalities in Ca2+ homeostasis resulted from downregulation of PSEN1 in failing hearts might contribute to aging-related cardiomyopathy, which might had important implications for the treatment of aging-related heart diseases.

Conditionally targeted deletion of PSEN1 leads to diastolic heart dysfunction.

Recently, PSEN1 has been reported to have mutations in dilated cardiomyopathy pedigrees. However, the function and mechanism of PSEN1 in cardiomyopathy remains unresolved. Here, we established four types of genetically modified mice to determine the function of PSEN1 in cardiac development and pathology. PSEN1 null mutation resulted in perinatal death, retardation of heart growth, ventricular dilatation, septum defects, and valvular thickening. PSEN1 knockout in adults led to decreased muscle fibers, widened sarcomere Z lines and reduced lengths of sarcomeres in cardiomyocytes. Cardiovascular loss of function of PSEN1 induced by Sm22a-Cre or Myh6-Cre/ER/tamoxifen also resulted in severe ultrastructural abnormalities, such as relaxed gap junctions between neighboring cardiomyocytes. Functionally, cardiovascular deletion of PSEN1 caused spontaneous mortality from birth to adulthood and led to diastolic heart dysfunction, including decreased volume of the left ventricle at the end-systolic and end-diastolic stages. Additionally, in a myocardial ischemia model, deletion of PSEN1 in the cardiovascular system first protected mice by inducing adaptive hypertrophy but ultimately resulted in severe heart failure. Furthermore, a collection of genes was abnormally expressed in the hearts of cardiac-specific PSEN1 knockout mice. They were enriched in cell proliferation, calcium regulation, and so on. Taken together, dynamic regulation and abnormal function of PSEN1 underlie the pathogenesis of cardiovascular diseases due to ultrastructural abnormality of cardiomyocytes.

Alternative splicing in cancers: From aberrant regulation to new therapeutics.

Alternative splicing is one of the most common mechanisms for gene regulation in humans, and plays a vital role to increase the complexity of functional proteins. In this article, we seek to provide a general review on the relationships between alternative splicing and tumorigenesis. We briefly introduce the basic rules for regulation of alternative splicing, and discuss recent advances on dynamic regulation of alternative splicing in cancers by highlighting the roles of a variety of RNA splicing factors in tumorigenesis. We further discuss several important questions regarding the splicing of long noncoding RNAs and back-splicing of circular RNAs in cancers. Finally, we discuss the current technologies that can be used to manipulate alternative splicing and serve as potential cancer treatment.

MicroRNA-199a acts as a potential suppressor of cardiomyocyte autophagy through targeting Hspa5.

Autophagy is an adaptive response to cardiomyocytes survival under stress conditions. MicroRNAs (miRNAs, miR) have been described to act as potent modulators of autophagy. To investigate whether and how miR-199a modulated autophagy in vitro, primary cardiomyocytes were treated under starvation to induce autophagy. Results showed that down-regulation of miR-199a was sufficient to activate cardiomyocytes autophagy. MiR-199a suppressed cardiomyocytes autophagy through direct inhibiting heat shock protein family A member 5 (Hspa5). Forced overexpression of Hspa5 recovered the inhibitory effect of miR-199a in autophagy activation. Our results suggested miR-199a as an effective suppressor of starvation-induced cardiomyocytes autophagy and that Hspa5 was a direct target during this process. These results extend the understanding of the role and pathway of miR-199a in cardiomyocytes autophagy, and may introduce a potential therapeutic strategy for the protection of cardiomyocytes in myocardial infarction or ischemic heart disease.

Se-methylselenocysteine inhibits apoptosis induced by clusterin knockdown in neuroblastoma N2a and SH-SY5Y cell lines.

Apoptosis, as a programmed cell death process, is essential for the maintenance of tissue function in organisms. Alteration of this process is linked to many diseases. Over-expression of clusterin (Clu) can antagonize apoptosis in various cells. Selenium (Se) is an essential trace element for human health. Its biological function is also associated with cell apoptosis. To explore the function of Clu and the impact of Se in the process of apoptosis, several short-hairpin RNAs (shRNA) were designed for the construction of two sets of recombinant plasmids: one set for plasmid-transfection of mouse neuroblastoma N2a cells (N2a cells); and the other set for lentiviral infection of human neuroblastoma SH-SY5Y cells (SH-SY5Y cells). These shRNAs specifically and efficiently interfered with the intracellular expression of Clu at both the mRNA and protein levels. The Clu-knockdown cells showed apoptosis-related features, including down-regulation of antioxidative capacity and the Bcl-2/Bax ratio and up-regulation of caspase-8 activity. Se-methylselenocysteine (MSC) at an optimum concentration of 1 µM could reverse the alteration in antioxidative capacity, Bcl2/Bax ratio and caspase-8 activity caused by Clu-knockdown, thus inhibiting apoptosis and maintaining cell viability. The results hereby imply the potentiality of Clu and Se in neuroprotection.