Structure-Function Studies of Two Yeast Homing Endonucleases that Evolved to Cleave Identical Targets with Dissimilar Rates and Specificities

The LAGLIDADG family of homing endonucleases (LHEs) bind to and cleave their DNA recognition sequences with high specificity. Much of our understanding for how these proteins evolve their specificities has come from studying LHE homologues. To gain insight into the molecular basis of LHE specificity, we characterized I-WcaI, the homologue of the Saccharomyces cerevisiae I-SceI LHE found in Wickerhamomyces canadensis. Although I-WcaI and I-SceI cleave the same recognition sequence, expression of I-WcaI, but not I-SceI, is toxic in bacteria. Toxicity suppressing mutations frequently occur at I-WcaI residues critical for activity and I-WcaI cleaves many more non-cognate sequences in the Escherichia coli genome than I-SceI, suggesting I-WcaI endonuclease activity is the basis of toxicity. In vitro, I-WcaI is a more active and a less specific endonuclease than I-SceI, again accounting for the observed toxicity in vivo. We determined the X-ray crystal structure of I-WcaI bound to its cognate target site and found that I-WcaI and I-SceI use residues at different positions to make similar base-specific contacts. Furthermore, in some regions of the DNA interface where I-WcaI specificity is lower, the protein makes fewer DNA contacts than I-SceI. Taken together, these findings demonstrate the plastic nature of LHE site recognition and suggest that I-WcaI and I-SceI are situated at different points in their evolutionary pathways towards acquiring target site specificity.     

MLR-OOD: A Markov Chain Based Likelihood Ratio Method for Out-Of-Distribution Detection of Genomic Sequences

Machine learning or deep learning models have been widely used for taxonomic classification of metagenomic sequences and many studies reported high classification accuracy. Such models are usually trained based on sequences in several training classes in hope of accurately classifying unknown sequences into these classes. However, when deploying the classification models on real testing data sets, sequences that do not belong to any of the training classes may be present and are falsely assigned to one of the training classes with high confidence. Such sequences are referred to as out-of-distribution (OOD) sequences and are ubiquitous in metagenomic studies. To address this problem, we develop a deep generative model-based method, MLR-OOD, that measures the probability of a testing sequencing belonging to OOD by the likelihood ratio of the maximum of the in-distribution (ID) class conditional likelihoods and the Markov chain likelihood of the testing sequence measuring the sequence complexity. We compose three different microbial data sets consisting of bacterial, viral, and plasmid sequences for comprehensively benchmarking OOD detection methods. We show that MLR-OOD achieves the state-of-the-art performance demonstrating the generality of MLR-OOD to various types of microbial data sets. It is also shown that MLR-OOD is robust to the GC content, which is a major confounding effect for OOD detection of genomic sequences. In conclusion, MLR-OOD will greatly reduce false positives caused by OOD sequences in metagenomic sequence classification.     

Structural Insight into Chromatin Recognition by Multiple Domains of the Tumor Suppressor RBBP1

Retinoblastoma-binding protein 1 (RBBP1) is involved in gene regulation, epigenetic regulation, and disease processes. RBBP1 contains five domains with DNA-binding or histone-binding activities, but how RBBP1 specifically recognizes chromatin is still unknown. An AT-rich interaction domain (ARID) in RBBP1 was proposed to be the key region for DNA-binding and gene suppression. Here, we first determined the solution structure of a tandem PWWP-ARID domain mutant of RBBP1 after deletion of a long flexible acidic loop L12 in the ARID domain. NMR titration results indicated that the ARID domain interacts with DNA with no GC- or AT-rich preference. Surprisingly, we found that the loop L12 binds to the DNA-binding region of the ARID domain as a DNA mimic and inhibits DNA binding. The loop L12 can also bind weakly to the Tudor and chromobarrel domains of RBBP1, but binds more strongly to the DNA-binding region of the histone H2A-H2B heterodimer. Furthermore, both the loop L12 and DNA can enhance the binding of the chromobarrel domain to H3K4me3 and H4K20me3. Based on these results, we propose a model of chromatin recognition by RBBP1, which highlights the unexpected multiple key roles of the disordered acidic loop L12 in the specific binding of RBBP1 to chromatin.     

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.     

Mechanism of SUMOylation-Mediated Regulation of Type I IFN Expression

Type I interferons (IFN) are cytokines that bridge the innate and adaptive immune response, and thus play central roles in human health, including vaccine efficacy, immune response to cancer and pathogen infection, and autoimmune disorders. Post-translational protein modifications by the small ubiquitin-like modifiers (SUMO) have recently emerged as an important regulator of type I IFN expression as shown by studies using murine and cellular models and recent human clinical trials. However, the mechanism regarding how SUMOylation regulates type I IFN expression remains poorly understood. In this study, we show that SUMOylation inhibition does not activate IFNB1 gene promoter that is regulated by known canonical pathways including cytosolic DNA. Instead, we identified a binding site for the chromatin modification enzyme, the SET Domain Bifurcated Histone Lysine Methyltransferase 1 (SETDB1), located between the IFNB1 promoter and a previously identified enhancer. We found that SETDB1 regulates IFNB1 expression and SUMOylation of SETDB1 is required for its binding and enhancing the H3K9me3 heterochromatin signal in this region. Heterochromatin, a tightly packed form of DNA, has been documented to suppress gene expression through suppressing enhancer function. Taken together, our study identified a novel mechanism of regulation of type I IFN expression, at least in part, through SUMOylation of a chromatin modification enzyme.     

A novel deep learning method for maize disease identification based on small sample-size and complex background datasets

Maize diseases are a major source of yield loss, but due to the lack of human experience and limitations of traditional image-recognition technology, obtaining satisfactory large-scale identification results of maize diseases are difficult. Fortunately, the advancement of deep learning-based technology makes it possible to automatically identify diseases. However, it still faces issues caused by small sample sizes and complex field background, which affect the accuracy of disease identification. To address these issues, a deep learning-based method was proposed for maize disease identification in this paper. DenseNet121 was used as the main extraction network and a multi-dilated-CBAM-DenseNet (MDCDenseNet) model was built by combining the multi-dilated module and convolutional block attention module (CBAM) attention mechanism. Five models of MDCDenseNet, DenseNet121, ResNet50, MobileNetV2, and NASNetMobile were compared and tested using three kinds of maize leave images from the PlantVillage dataset and field-collected at Northeast Agricultural University in China. Furthermore, auxiliary classifier generative adversarial network (ACGAN) and transfer learning were used to expand the dataset and pre-train for optimal identification results. When tested on field-collected datasets with a complex background, the MDCDenseNet model outperformed compared to these models with an accuracy of 98.84%. Therefore, it can provide a viable reference for the identification of maize leaf diseases collected from the farmland with a small sample size and complex background.     

Dynamic Protein-RNA recognition in primary MicroRNA processing

MicroRNAs are prevalent regulators of gene expression, controlling most of the proteome in multicellular organisms. To generate the functional small RNAs, precise processing steps are required. In animals, microRNA biogenesis is initiated by Microprocessor that minimally consists of the Drosha enzyme and its partner, DGCR8. This first step is critical for selecting primary microRNAs, and many RNA-binding proteins and regulatory pathways target both the accuracy and efficiency of microRNA maturation. Structures of Drosha and DGCR8 in complex with primary microRNAs elucidate how RNA structural features rather than sequence provide the framework for substrate recognition. Comparing multiple states of Microprocessor and the closely related Dicer homologs shed light on the dynamic protein-RNA complex assembly and disassembly required to recognize RNAs with diverse sequences via common structural features.     

The MDMX Acidic Domain Uses Allovalency to Bind Both p53 and MDMX

Autoinhibition of p53 binding to MDMX requires two short-linear motifs (SLiMs) containing adjacent tryptophan (WW) and tryptophan-phenylalanine (WF) residues. NMR spectroscopy was used to show the WW and WF motifs directly compete for the p53 binding site on MDMX and circular dichroism spectroscopy was used to show the WW motif becomes helical when it is bound to the p53 binding domain (p53BD) of MDMX. Binding studies using isothermal titration calorimetry showed the WW motif is a stronger inhibitor of p53 binding than the WF motif when they are both tethered to p53BD by the natural disordered linker. We also investigated how the WW and WF motifs interact with the DNA binding domain (DBD) of p53. Both motifs bind independently to similar sites on DBD that overlap the DNA binding site. Taken together our work defines a model for complex formation between MDMX and p53 where a pair of disordered SLiMs bind overlapping sites on both proteins.     

Enhanced targeted DNA methylation of the CMV and endogenous promoters with dCas9-DNMT3A3L entails distinct subsequent histone modification changes in CHO cells

With the emergence of new CRISPR/dCas9 tools that enable site specific modulation of DNA methylation and histone modifications, more detailed investigations of the contribution of epigenetic regulation to the precise phenotype of cells in culture, including recombinant production subclones, is now possible. These also allow a wide range of applications in metabolic engineering once the impact of such epigenetic modifications on the chromatin state is available.In this study, enhanced DNA methylation tools were targeted to a recombinant viral promoter (CMV), an endogenous promoter that is silenced in its native state in CHO cells, but had been reactivated previously (β-galactoside α-2,6-sialyltransferase 1) and an active endogenous promoter (α-1,6-fucosyltransferase), respectively. Comparative ChIP-analysis of histone modifications revealed a general loss of active promoter histone marks and the acquisition of distinct repressive heterochromatin marks after targeted methylation. On the other hand, targeted demethylation resulted in autologous acquisition of active promoter histone marks and loss of repressive heterochromatin marks. These data suggest that DNA methylation directs the removal or deposition of specific histone marks associated with either active, poised or silenced chromatin. Moreover, we show that de novo methylation of the CMV promoter results in reduced transgene expression in CHO cells. Although targeted DNA methylation is not efficient, the transgene is repressed, thus offering an explanation for seemingly conflicting reports about the source of CMV promoter instability in CHO cells.Importantly, modulation of epigenetic marks enables to nudge the cell into a specific gene expression pattern or phenotype, which is stabilized in the cell by autologous addition of further epigenetic marks. Such engineering strategies have the added advantage of being reversible and potentially tunable to not only turn on or off a targeted gene, but also to achieve the setting of a desirable expression level.     

Detection and monitoring of HBV-related hepatocellular carcinoma from plasma cfDNA fragmentation profiles

Most hepatocellular carcinomas (HCCs) are associated with hepatitis B virus infection (HBV) in China. Early detection of HCC can significantly improve prognosis but is not yet fully clinically feasible. This study aims to develop methods for detecting HCC and studying the carcinogenesis of HBV using plasma cell-free DNA (cfDNA) whole-genome sequencing (WGS) data. Low coverage WGS was performed for 452 participants, including healthy individuals, hepatitis B patients, cirrhosis patients, and HCC patients. Then the sequencing data were processed using various machine learning models based on cfDNA fragmentation profiles for cancer detection. Our best model achieved a sensitivity of 87.10% and a specificity of 88.37%, and it showed an increased sensitivity with higher BCLC stages of HCC. Overall, this study proves the potential of a non-invasive assay based on cfDNA fragmentation profiles for the detection and prognosis of HCC and provides preliminary data on the carcinogenic mechanism of HBV.     

Migratory birds as the vehicle of transmission of multi drug resistant extended spectrum β lactamase producing Escherichia fergusonii,an emerging zoonotic pathogen

The acquisition of multi-drug resistance (MDR) genes by pathogenic bacterial bugs and their dispersal to different food webs has become a silent pandemic. The multiplied use of different antibacterial therapeutics during COVID-19 pandemic has accelerated the process among emerging pathogens. Wild migratory birds play an important role in the spread of MDR pathogens and MDR gene flow due to the consumption of contaminated food and water. Escherichia fergusonii is an emerging pathogen of family Enterobacteriaceae and commonly causes disease in human and animals. The present study focused on the isolation of E. fergusonii from blood, saliva, and intestine of selected migratory birds of the Hazara Division. The sensitivity of isolated strains was assessed against ten different antibiotics. The isolation frequency of E. fergusonii was 69%. In blood samples, a high rate of resistance was observed against ceftriaxone (80%) followed by ampicillin (76%) whereas, in oral and intestinal samples, ceftriaxone resistant strains were 56% and 57% while ampicillin resistance was 49% and 52% respectively. The overall ceftriaxone and ampicillin-resistant cases in all three sample sources were 71% and 65% respectively. In comparison to oral and intestinal samples, high numbers of ceftriaxone-resistant strains were isolated from the blood of mallard while ampicillin-resistant strains were observed in blood samples of cattle egrets. 16S rRNA-based confirmed strains of E. fergusonii were processed for detection of CTX-M and TEM-1 gene through Polymerase chain reaction (PCR) after DNA extraction. Hundred percent ceftriaxone resistant isolates possessed CTX-M and all ampicillin-resistant strains harbored TEM-1 genes. Amplified products were sequenced by using the Sanger sequencing method and the resulted sequences were checked for similarity in the nucleotide Database through the BLAST program. TEM-1 gene showed 99% and the CTX-M gene showed 98% similar sequences in the Database. The 16S rRNA sequence and nucleotide sequences for TEM-1 and CTX-M genes were submitted to Gene Bank with accession numbers LC521304, LC521306, LC521307 respectively. We posit to combat MDR gene flow among the bacterial pathogens across different geographical locations, regular surveillance of new zoonotic pathogens must be conducted.     

Conformational Dynamics Allows Sampling of an “Active-like” State by Oncogenic K-Ras-GDP

Mutations in K-Ras GTPase replacing Gly12 with either Asp or Val are common in cancer. These mutations decelerate intrinsic and catalyzed GTP hydrolysis, leading to accumulation of K-Ras-GTP in cells. Signaling cascades initiated by K-Ras-GTP promote cell proliferation, survival, and invasion. Despite functional differences between the most frequent G12D mutation and the most aggressive and chemotherapy resistant G12V mutation, their long-suspected distinct structural features remain elusive. Using NMR, X-ray structures, and computational methods, we found that oncogenic mutants of K-Ras4B, the predominant splice variant of K-Ras, exhibit distinct conformational dynamics when GDP-bound, visiting the “active-like” conformational state similar to the one observed in GTP-bound K-Ras. This behavior distinguishes G12V from wild type and G12D K-Ras4B-GDP. The likely reason is interactions between the aliphatic sidechain of V12 and the Switch II region of K-Ras4B^G12V-GDP, which are distinct in K-Ras4B^G12D-GDP. In the X-ray structures, crystal contacts reduce the dynamics of the sidechain at position 12 by stabilizing the Switch I region of the protein. This explains why structural differences between G12V and G12D K-Ras have yet not been reported. Together, our results suggest a previously unknown mechanism of K-Ras activation. This mechanism relies on conformational dynamics caused by specific oncogenic mutations in the GDP-bound state. Our findings also imply that the therapeutic strategies decreasing the level of K-Ras-GTP by interfering with nucleotide exchange or by expediting GTP hydrolysis may work differently in different oncogenic mutants.     

Nomogram to Assess the Risk of Central Cervical Lymph Node Metastasis in Patients With Clinical N0 Papillary Thyroid Carcinoma

ObjectiveTo develop and validate an individualized risk prediction model for the need for central cervical lymph node dissection in patients with clinical N0 papillary thyroid carcinoma (PTC) diagnosed using ultrasound.MethodsUpon retrospective review, derivation and internal validation cohorts comprised 1585 consecutive patients with PTC treated from January 2017 to December 2019 at hospital A. The external validation cohort consisted of 406 consecutive patients treated at hospital B from January 2016 to June 2020. Independent risk factors for central cervical lymph node metastasis (CLNM) were determined through univariable and multivariable logistic regression analysis. An individualized risk prediction model was constructed and illustrated as a nomogram, which was internally and externally validated.ResultsThe following risk factors of CLNM were established: a solitary primary thyroid nodule’s diameter, shape, calcification, and capsular abutment-to-lesion perimeter ratio. The areas under the receiver operating characteristic curves of the risk prediction model for the internal and external validation cohorts were 0.921 and 0.923, respectively. The calibration curve showed good agreement between the nomogram-estimated probability of CLNM and the actual CLNM rates in the 3 cohorts. The decision curve analysis confirmed the clinical usefulness of the nomogram.ConclusionThis study developed and validated a model for predicting the risk of CLNM in individual patients with clinical N0 PTC, which should be an efficient tool for guiding clinical treatment.