C1QBP Promotes Homologous Recombination by Stabilizing MRE11 and Controlling the Assembly and Activation of MRE11/RAD50/NBS1 Complex.

MRE11 nuclease forms a trimeric complex (MRN) with RAD50 and NBS1 and plays a central role in preventing genomic instability. When DNA double-strand breaks (DSBs) occur, MRN is quickly recruited to the damage site and initiates DNA end resection; accordingly, MRE11 must be tightly regulated to avoid inefficient repair or nonspecific resection. Here, we show that MRE11 and RAD50 form a complex (MRC) with C1QBP, which stabilizes MRE11/RAD50, while inhibiting MRE11 nuclease activity by preventing its binding to DNA or chromatin. Upon DNA damage, ATM phosphorylates MRE11-S676/S678 to quickly dissociate the MRC complex. Either excess or insufficient C1QBP impedes the recruitment of MRE11 to DSBs and impairs the DNA damage response. C1QBP is highly expressed in breast cancer and positively correlates with MRE11 expression, and the inhibition of C1QBP enhances tumor regression with chemotherapy. By influencing MRE11 at multiple levels, C1QBP is, thus, an important player in the DNA damage response.

The ARK2N-CK2 complex initiates transcription-coupled repair through enhancing the interaction of CSB with lesion-stalled RNAPII.

Transcription is extremely important for cellular processes but can be hindered by RNA polymerase II (RNAPII) pausing and stalling. Cockayne syndrome protein B (CSB) promotes the progression of paused RNAPII or initiates transcription-coupled nucleotide excision repair (TC-NER) to remove stalled RNAPII. However, the specific mechanism by which CSB initiates TC-NER upon damage remains unclear. In this study, we identified the indispensable role of the ARK2N-CK2 complex in the CSB-mediated initiation of TC-NER. The ARK2N-CK2 complex is recruited to damage sites through CSB and then phosphorylates CSB. Phosphorylation of CSB enhances its binding to stalled RNAPII, prolonging the association of CSB with chromatin and promoting CSA-mediated ubiquitination of stalled RNAPII. Consistent with this finding, Ark2n-/- mice exhibit a phenotype resembling Cockayne syndrome. These findings shed light on the pivotal role of the ARK2N-CK2 complex in governing the fate of RNAPII through CSB, bridging a critical gap necessary for initiating TC-NER.

A survey of experimental and computational identification of small proteins.

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

Multimodal deep learning approaches for single-cell multi-omics data integration.

Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.

ATM-CHK2-Beclin 1 axis promotes autophagy to maintain ROS homeostasis under oxidative stress.

The homeostatic link between oxidative stress and autophagy plays an important role in cellular responses to a wide variety of physiological and pathological conditions. However, the regulatory pathway and outcomes remain incompletely understood. Here, we show that reactive oxygen species (ROS) function as signaling molecules that regulate autophagy through ataxia-telangiectasia mutated (ATM) and cell cycle checkpoint kinase 2 (CHK2), a DNA damage response (DDR) pathway activated during metabolic and hypoxic stress. We report that CHK2 binds to and phosphorylates Beclin 1 at Ser90/Ser93, thereby impairing Beclin 1-Bcl-2 autophagy-regulatory complex formation in a ROS-dependent fashion. We further demonstrate that CHK2-mediated autophagy has an unexpected role in reducing ROS levels via the removal of damaged mitochondria, which is required for cell survival under stress conditions. Finally, CHK2-/- mice display aggravated infarct phenotypes and reduced Beclin 1 p-Ser90/Ser93 in a cerebral stroke model, suggesting an in vivo role of CHK2-induced autophagy in cell survival. Taken together, these results indicate that the ROS-ATM-CHK2-Beclin 1-autophagy axis serves as a physiological adaptation pathway that protects cells exposed to pathological conditions from stress-induced tissue damage.

Diagnostic Efficacy of a Novel Rotating Brush for Endoscopic Sampling of Malignant Biliary Strictures: A Multicenter Prospective Study.

INTRODUCTION: Although cytologic examination of biliary stricture brushings obtained by endoscopic retrograde cholangiopancreatography is commonly used for diagnosing malignant biliary strictures (MBSs), it has low sensitivity. Several new brushes have capabilities that are still being debated. We have developed a novel brush working from conventional back-and-forth movement to rotation in situ (RIS) that may be more efficient for MBS sampling. We aimed to compare the MBS detection sensitivity of our RIS brush with that of the conventional brush. METHODS: In this multicenter prospective study, we enrolled patients who underwent endoscopic retrograde cholangiopancreatography for suspected MBSs involving biliary stricture brushings obtained using our RIS brush. The historical control group consisted of the 30-brushing arm of our previous randomized trial (patient inclusion, 2018-2020) that used the study design in the same centers and with the same endoscopists as were used in this study. The primary outcome was to compare the sensitivity and specificity of detecting MBSs by cytologic evaluation of biliary stricture brushings between the 2 groups. RESULTS: We enrolled 155 patients in the intent-to-treat analysis. Using the same number of brushing cycles, the RIS brush showed a higher sensitivity than the conventional brush (0.73 vs 0.56, P = 0.003). In per-protocol population, the sensitivity was also higher in the RIS brush group than in the conventional brush group (0.75 vs 0.57, P = 0.002). Multivariate analysis revealed that the RIS brush was the only predictive factor for MBS detection. No significant differences were observed in procedure-related complications between the 2 groups. DISCUSSION: The RIS brush was a promising tool for effective and safe MBS sampling and diagnosis. Further randomized studies are warranted to confirm our results (Chictr.org.cn, identifier: ChiCTR2100047270).

Letter to the editor: evaluating computational tools for lncRNA identification on independent datasets.

The authors of the BASiNET tool claim that the survey paper 'A systematic evaluation of computational tools for lncRNA identification' incorrectly evaluates the BASiNET tool. Here, we point out that the survey paper correctly evaluates the BASiNET tool and why the evaluation should not be carried out as BASiNET authors suggest.

Computational analyses of bacterial strains from shotgun reads.

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Enhanced Photocatalytic Nitrogen Reduction via Bismuth Nanoparticle-Decorating ZnCdS Solid Solution.

The construction of photocatalysts with a surface plasmon resonance effect (SPR) has been demonstrated as a highly effective strategy for enhancing photocatalytic efficiency. In this paper, we synthesized a catalyst with bismuth metal loaded on ZnCdS nanospheres for an efficient photocatalytic nitrogen reduction reaction (PNRR). The SPR effect induced by Bi nanoparticles under light excitation significantly promoted the ammonia production efficiency of the photocatalyst. Under air ambient conditions with lactic acid as the sacrificial agent, the photocatalytic NH4+ yield of 3% Bi@ZnCdS was 58.93 µmol·g-1·h-1, which exhibited an approximately 7.7 times that of the pure phase ZnCdS. The experimental characterization results demonstrate that the incorporation of metallic bismuth enhances the light absorption capacity of the catalyst and improves the separation efficiency of the photogenerated carriers. Theoretical calculations proved that Bi NPs provide more photogenerated electrons to convert N2 to NH3 for solid-solution ZnCdS. This work presents a novel concept for the development of advanced plasma nanomaterials to enhance the photocatalytic nitrogen fixation reaction.

Effect of vinegar steaming on the composition and structure of Schisandra chinensis polysaccharide and its anti-colitis activity.

In this study, infrared spectroscopy, high-performance liquid chromatography, and matrix-assisted laser desorption ionization-time-of-flight-mass spectrometry (MALDI-TOF-MS) technology were applied to systematically explain the Schisandra chinensis's polysaccharide transformation in configuration, molecular weight, monosaccharide composition, and anti-ulcerative colitis (UC) activity after vinegar processing. Scanning electron microscopic results showed that the appearance of S. chinensis polysaccharide changed significantly after steaming with vinegar. The MALDI-TOF-MS results showed that the mass spectra of raw S. chinensis polysaccharides (RSCP) were slightly lower than those of vinegar-processed S. chinensis polysaccharides (VSCP). The RSCP showed higher peaks at m/z 1350.790, 2016.796, and 2665.985, all with left-skewed distribution, and the molecular weights were concentrated in the range of 1300-3100, with no higher peak above m/z 5000. The VSCPs showed a whole band below m/z 3000, with m/z 1021.096 being the highest peak, and the intensity decreased with the increase of m/z. In addition, compared to RSCPs, VSCPs can significantly increase the content of intestinal short-chain fatty acids (SCFAs). This study showed that the apparent morphology and molecular weight of S. chinensis's polysaccharides significantly changed after steaming with vinegar. These changes directly affect its anti-UC effect significantly, and its mechanism is closely related to improving the structure and diversity of gut microbiota and SCFA metabolism.

Selective Oxidative Cleavage of Benzyl C-N Bond under Metal-Free Electrochemical Conditions.

With the growing significance of green chemistry in organic synthesis, electrochemical oxidation has seen rapid development. Compounds undergo oxidation-reduction reactions through electron transfer at the electrode surface. This article proposes the use of electrochemical methods to achieve cleavage of the benzyl C-N bond. This method selectively oxidatively cleaves the C-N bond without the need for metal catalysts or external oxidants. Additionally, primary, secondary, and tertiary amines exhibit good adaptability under these conditions, utilizing water as the sole source of oxygen.

Interpretation of deep learning in genomics and epigenomics.

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.

A systematic evaluation of the computational tools for lncRNA identification.

The computational identification of long non-coding RNAs (lncRNAs) is important to study lncRNAs and their functions. Despite the existence of many computation tools for lncRNA identification, to our knowledge, there is no systematic evaluation of these tools on common datasets and no consensus regarding their performance and the importance of the features used. To fill this gap, in this study, we assessed the performance of 17 tools on several common datasets. We also investigated the importance of the features used by the tools. We found that the deep learning-based tools have the best performance in terms of identifying lncRNAs, and the peptide features do not contribute much to the tool accuracy. Moreover, when the transcripts in a cell type were considered, the performance of all tools significantly dropped, and the deep learning-based tools were no longer as good as other tools. Our study will serve as an excellent starting point for selecting tools and features for lncRNA identification.

Computational annotation of miRNA transcription start sites.

MOTIVATION: MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation. RESULTS: In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes. CONTACT: xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.

Prevalence of bovine coronavirus in cattle in China: A systematic review and meta-analysis.

Bovine coronavirus (BCoV) is one of the important pathogens that cause calf diarrhea (CD), winter dysentery (WD), and the bovine respiratory disease complex (BRDC), and spreads worldwide. An infection of BCoV in cattle can lead to death of young animals, stunted growth, reduced milk production, and milk quality, thus bringing serious economic losses to the bovine industry. Therefore, it is necessary to prevent and control the spread of BCoV. Here, a systematic review and meta-analysis was conducted to assess the prevalence of BCoV in cattle in China before 2022. A total of 57 articles regarding the prevalence of BCoV in cattle in China were collected from five databases (PubMed, ScienceDirect, CNKI, VIP, and Wan Fang). Based on the inclusion criteria, a total of 15,838 samples were included, and 6,136 were positive cases. The overall prevalence of BCoV was 30.8%, with the highest prevalence rate (60.5%) identified in South China and the lowest prevalence (15.6%) identified in Central China. We also analyzed other subgroup information, included sampling years, sample sources, detection methods, breeding methods, age, type of cattle, presence of diarrhea, and geographic and climatic factors. The results indicated that BCoV was widely prevalent in China. Among all subgroups, the sample sources, detection methods, breeding methods, and presence or absence of diarrheal might be potential risk factors responsible for BCoV prevalence. It is recommended to strengthen the detection of BCoV in cattle, in order to effectively control the spread of BCoV.

Three-Component Oxychalcogenation of Alkenes under Metal-Free Conditions: A Tetrabutylammonium Tribromide-Catalyzed System.

A three-component oxychalcogenation reaction, from alkenes, diselenides/thiophenols, and H2O/alcohols, has been realized herein. Tetrabutylammonium tribromide (TBATB) and dimethylsulfoxide (DMSO) are utilized as the catalyst and the terminal oxidant, respectively, to enable this difunctionalization transformation. The metal-free reaction system shows good functional group compatibility, providing a unified and practical approach to access ß-hydroxyl or ß-alkoxy organochalcogenides.

INSISTC: Incorporating network structure information for single-cell type classification.

Uncovering gene regulatory mechanisms in individual cells can provide insight into cell heterogeneity and function. Recent accumulated Single-Cell RNA-Seq data have made it possible to analyze gene regulation at single-cell resolution. Understanding cell-type-specific gene regulation can assist in more accurate cell type and state identification. Computational approaches utilizing such relationships are under development. Methods pioneering in integrating gene regulatory mechanism discovery with cell-type classification encounter challenges such as determine gene regulatory relationships and incorporate gene regulatory network structure. To fill this gap, we developed INSISTC, a computational method to incorporate gene regulatory network structure information for single-cell type classification. INSISTC is capable of identifying cell-type-specific gene regulatory mechanisms while performing single-cell type classification. INSISTC demonstrated its accuracy in cell type classification and its potential for providing insight into molecular mechanisms specific to individual cells. In comparison with the alternative methods, INSISTC demonstrated its complementary performance for gene regulation interpretation.

Jitter-Caused Clutter and Drift-Caused Clutter of Staring Infrared Sensor in Geostationary Orbit.

For staring infrared sensors in geostationary orbit, the clutter caused by the high-frequency jitter and low-frequency drift of the sensor line-of-sight (LOS) is the impact of background features, sensor parameters, LOS motion characteristics, and background suppression algorithms. In this paper, the spectra of LOS jitter caused by cryocoolers and momentum wheels are analyzed, and the time-related factors such as the jitter spectrum, the detector integration time, the frame period, and the temporal differencing background suppression algorithm are considered comprehensively; they are combined into a background-independent jitter-equivalent angle model. A jitter-caused clutter model in the form of multiplying the background radiation intensity gradient statistics by the jitter-equivalent angle is established. This model has good versatility and high efficiency and is suitable for the quantitative evaluation of clutter and the iterative optimization of sensor design. Based on satellite ground vibration experiments and on-orbit measured image sequences, the jitter-caused clutter and drift-caused clutter models are verified. The relative deviation between the model calculation and the actual measurement results is less than 20%.

Emerging Roles of RNF168 in Tumor Progression.

RING finger protein 168 (RNF168) is an E3 ubiquitin ligase with the RING finger domain. It is an important protein contributing to the DNA double-strand damage repair pathway. Recent studies have found that RNF168 is significantly implicated in the occurrence and development of various cancers. Additionally, RNF168 contributes to the drug resistance of tumor cells by enhancing their DNA repair ability or regulating the degradation of target proteins. This paper summarizes and prospects the research progress of the structure and main functions of RNF168, especially its roles and the underlying mechanisms in tumorigenesis.

Development of meat analogs: Focus on the current status and challenges of regulatory legislation.

Population growth and the rising enthusiasm for meat consumption in developing countries have increased the global demand for animal protein. The limited increase in traditional meat production, which results in high resource consumption, greenhouse gas emissions, and zoonotic diseases, has affected the sustainable supply of meat protein. The technological development and commercialization of meat analogs derived from plant and microbial proteins provide a strategy for solving the abovementioned problems. However, before these innovative foods are marketed, they should comply with regulations and standards to ensure food safety and consumer rights. This review briefly summarizes the global development status and challenges of plant- and fungi-based meat analog products. It focuses on the current status, characteristics, and disputes in the regulations and standards worldwide for plant- and fungi-based meat analogs and proposes suggestions for perfecting the regulatory system from the perspective of ensuring safety and supporting innovation. Although plant- and fungi-based meat analogs have had a history of safe usage as foods for a certain period around the world, the nomenclature and product standards are uncertain, which affects product innovation and global sales. Regulatory authorities should promptly formulate and revise regulations or standards to clarify the naming of meat analogs and product standards, especially the use of animal-derived ingredients and limits of nutrients (e.g., protein, fat, vitamins, and minerals) to continuously introduce start-up products to the market.