Local Sequence Research Articles

Cytoplasmic Aggregates of Splicing Factor Proline-Glutamine Rich Disrupt Nucleocytoplasmic Transport and Induce Persistent Stress Granules.

Splicing factor proline-glutamine rich (SFPQ), a multifunctional RNA-binding protein (RBP), shows cytoplasmic colocalisation with stress granule (SG) markers; however, the causative relationship and mechanism underlying this coalescence of SFPQ aggregates and SGs remain unclear. In this study, we demonstrate that SFPQ lacking its nuclear localisation sequence spontaneously forms cytoplasmic aggregates that abnormally incorporate immature RNA and induce persistent SGs. mRNA profiling showed that SFPQ mislocalisation induced extensive changes in RNA processing, with a subset of alternatively spliced transcripts associated with nucleocytoplasmic transport. Notably, these altered transporters were sequestered into SFPQ aggregates, constituting aberrant protein-RNA complexes. Importantly, suppression of SG nucleation could not block cytoplasmic SFPQ aggregation with immature RNA and nucleocytoplasmic transporters, both of which, however, were moderately ameliorated by the inhibition of alternative splicing or nuclear export. Our results unveil the physiopathological role and mechanism for mislocalised SFPQ in the RNA metabolism, nucleocytoplasmic transport and pathological SGs.

TPepPro: a deep learning model for predicting peptide-protein interactions.

Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of peptide-protein interactions (PepPIs) and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory. To address this gap, we proposed TPepPro, a Transformer-based model for PepPI prediction. We trained TPepPro on a dataset of 19,187 pairs of peptide-protein complexes with both sequential and structural features. TPepPro utilizes a strategy that combines local protein sequence feature extraction with global protein structure feature extraction. Moreover, TPepPro optimizes the architecture of structural featuring neural network in BN-ReLU arrangement, which notably reduced the amount of computing resources required for peptide-protein interactions prediction. According to comparison analysis, the accuracy reached 0.855 in TPepPro, achieving an 8.1% improvement compared to the second-best model TAGPPI. TPepPro achieved an AUC of 0.922, surpassing the second-best model TAGPPI with 0.844. Moreover, the newly developed TPepPro identify certain PepPIs that can be validated according to previous experimental evidence, thus indicating the efficiency of TPepPro to detect high potential PepPIs that would be helpful for amino acid drug applications. The source code of TPepPro is available at https://github.com/wanglabhku/TPepPro. Supplementary data are available at Bioinformatics online.\.

Crosstalk between paralogs and isoforms influences p63-dependent regulatory element activity.

The p53 family of transcription factors (p53, p63 and p73) regulate diverse organismal processes including tumor suppression, maintenance of genome integrity and the development of skin and limbs. Crosstalk between transcription factors with highly similar DNA binding profiles, like those in the p53 family, can dramatically alter gene regulation. While p53 is primarily associated with transcriptional activation, p63 mediates both activation and repression. The specific mechanisms controlling p63-dependent gene regulatory activity are not well understood. Here, we use massively parallel reporter assays (MPRA) to investigate how local DNA sequence context influences p63-dependent transcriptional activity. Most regulatory elements with a p63 response element motif (p63RE) activate transcription, although binding of the p63 paralog, p53, drives a substantial proportion of that activity. p63RE sequence content and co-enrichment with other known activating and repressing transcription factors, including lineage-specific factors, correlates with differential p63RE-mediated activities. p63 isoforms dramatically alter transcriptional behavior, primarily shifting inactive regulatory elements towards high p63-dependent activity. Our analysis provides novel insight into how local sequence and cellular context influences p63-dependent behaviors and highlights the key, yet still understudied, role of transcription factor paralogs and isoforms in controlling gene regulatory element activity.

MCNN-AAPT: accurate classification and functional prediction of amino acid and peptide transporters in secondary active transporters using protein language models and multi-window deep learning

Secondary active transporters play a crucial role in cellular physiology by facilitating the movement of molecules across cell membranes. Identifying the functional classes of these transporters, particularly amino acid and peptide transporters, is essential for understanding their involvement in various physiological processes and disease pathways, including cancer. This study aims to develop a robust computational framework that integrates pre-trained protein language models and deep learning techniques to classify amino acid and peptide transporters within the secondary active transporter (SAT) family and predict their functional association with solute carrier (SLC) proteins. The study leverages a comprehensive dataset of 448 secondary active transporters, including 36 solute carrier proteins, obtained from UniProt and the Transporter Classification Database (TCDB). Three state-of-the-art protein language models, ProtTrans, ESM-1b, and ESM-2, are evaluated within a deep learning neural network architecture that employs a multi-window scanning technique to capture local and global sequence patterns. The ProtTrans-based feature set demonstrates exceptional performance, achieving a classification accuracy of 98.21% with 87.32% sensitivity and 99.76% specificity for distinguishing amino acid and peptide transporters from other SATs. Furthermore, the model maintains strong predictive ability for SLC proteins, with an overall accuracy of 88.89% and a Matthews Correlation Coefficient (MCC) of 0.7750. This study showcases the power of integrating pre-trained protein language models and deep learning techniques for the functional classification of secondary active transporters and the prediction of associated solute carrier proteins. The findings have significant implications for drug development, disease research, and the broader understanding of cellular transport mechanisms.

MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction

Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to representing biological sequences and automatically extracting their key features for improving SE identification. We propose a deep learning model MuSE (Multi-Feature Fusion for Super-Enhancer), based on multi-feature fusion. This model utilizes two encoding methods, one-hot and DNA2Vec, to signify DNA sequences. Specifically, one-hot encoding reflects single nucleotide information, while k-mer representations based on DNA2Vec capture both local sequence fragment information and global sequence characteristics. These types of feature vectors are conducted and combined by neural networks, which aim at SE prediction. To validate the effectiveness of MuSE, we design extensive experiments on human and mouse species datasets. Compared to baselines such as SENet, MuSE improves the prediction of F1 score to a maximum improvement exceeding 0.05 on mouse species. The k-mer representations based on DNA2Vec among the given features have the most important impact on predictions. This feature effectively captures context semantic knowledge and positional information of DNA sequences. However, its representation of the individuality of each species negatively affects MuSE's generalization ability. Nevertheless, the cross-species prediction results of MuSE improve again to reach an AUC of nearly 0.8, after removing this type of feature. Source codes are available at https://github.com/15831959673/MuSE.

Sequence specificity of an essential nuclear localization sequence in Mcm3.

Transporting proteins into and out of the cell nucleus is essential for chromosome-associated activities. Nuclear localization sequences (NLSs), short motifs rich in basic amino acids, are commonly found in nuclear proteins. NLSs work by interacting with importin, a key transport receptor responsible for recognizing and guiding NLS-containing proteins through the nuclear pore complex into the nucleus. Other than being rich in basic amino acids, NLSs generally lack a discernible consensus sequence, raising questions about how they specifically control nuclear transport through their interactions with importins. Through a detailed mutagenesis study of a conserved and essential NLS in Mcm3, a subunit of the replicative DNA helicase, we demonstrate that the use of AlphaFold 3 (AF3), alongside genetic, biochemical, and cell biological analyses, define key contacts between Mcm3's NLS and importin that are required for nuclear import of Mcm3.

Nuclear localization sequence of MoHTR1, a Magnaporthe oryzae effector, for transcriptional reprogramming of immunity genes in rice

Plant pathogens secrete nuclear effectors into the host nuclei to modulate the host immune system. Although several nuclear effectors of fungal pathogens have been recently reported, the molecular mechanism of NLS-associated transport vehicles of nuclear effectors and the roles of NLS in transcriptional reprogramming of host immunity genes remain enigmatic. We previously reported the MoHTR1, a nuclear effector of the rice blast fungus, Magnaporthe oryzae. MoHTR1 is translocated to rice nuclei but not in fungal nuclei. Here, we identify the core NLS (RxKK) responsible for MoHTR1’s nuclear localization. MoHTR1 is translocated in the host nucleus through interaction with rice importin α. MoHTR1 NLS empowers it to translocate the cytoplasmic effectors of M. oryzae into rice nuclei. Furthermore, other nuclear effector candidates of the blast pathogen and rice proteins which have RxKK also exhibit nuclear localization, highlighting the crucial role of RxKK in this process. We also unveil the importance of SUMOylation in the stability of MoHTR1 and translocation of MoHTR1 to host nuclei. Moreover, MoHTR1 NLS is essential for the pathogenicity of M. oryzae by reprogramming immunity-associated genes in the host. Our findings provide insights into the significance of plant-specific NLS on fungal nuclear effectors and its role in plant-pathogen interactions.

Nuclear EGFR in breast cancer suppresses NK cell recruitment and cytotoxicity.

Natural Killer (NK) cells can target and destroy cancer cells, yet tumor microenvironments typically suppress NK cell recruitment and cytotoxicity. The epidermal growth factor receptor (EGFR) is a potent oncogene that can activate survival, migration, and proliferation pathways, and clinical data suggests it may also play an immunomodulating role in cancers. Recent work has demonstrated a novel role for nuclear EGFR (nEGFR) in regulating transcriptional events unique from the kinase domain. Using a novel peptide therapeutic (cSNX1.3) that inhibits retrograde trafficking of EGFR and an EGFR nuclear localization mutant, we discovered that nEGFR suppresses NK cell recruitment and cytotoxicity. RNA-Seq analysis of breast cancer cells treated with cSNX1.3 or modified to lack a nuclear localization sequence (EGFRΔNLS) revealed the EGF-dependent induction of NK activating receptor ligands, while kinase inhibition by erlotinib did not impact these genes. NanoString analysis of tumor-bearing WAP-TGFα transgenic mice treated with cSNX1.3 demonstrated an increase in immune cell populations and activating genes. Additionally, immunohistochemistry confirmed an increase in NK cells upon cSNX1.3 treatment. Finally, cSNX1.3 treatment was found to enhance NK cell recruitment and cytotoxicity in vitro. Together, the data demonstrate a unique immunomodulatory role for nEGFR.

The SBP-box transcription factor PlSPL2 negatively regulates stem development in herbaceous peony.

The SBP-box transcription factor PlSPL2 silencing in herbaceous peony enhanced stem strength by regulating xylem development, whereas its overexpression in tobacco resulted in weaker stem strength and undeveloped xylem. The strength of plant stems is a critical determinant of lodging resistance of plants, which significantly affects crop yield and cut-flower quality. Squamosa promoter binding (SBP) protein-like (SPL) transcription factors (TFs), participate in multiple regulatory processes, particularly in stem development. In this study, PlSPL2, an orthologous gene of Arabidopsis AtSPL2 in herbaceous peony, was isolated and found to contain a conserved SBP domain featuring two typical Zn-binding sites, as well as a nuclear localization sequence (NLS). Subsequently, transient infection of tobacco leaf epidermal cells using Agrobacterium confirmed the nuclear localization of PISPL2 protein. Additionally, gene expression analyses revealed that PlSPL2 was preferentially expressed in stems, and demonstrated a download trend in expression levels within vascular bundles during stem cell wall development. Furthermore, silencing of PlSPL2 in herbaceous peony enhanced stem strength. The silenced plants exhibited more developed xylems with wider radii and higher numbers of cell layers. Overexpression of PlSPL2 in tobacco, however, resulted in weaker stem strength, accompanied by a narrower radius of the xylem. These findings suggested that PlSPL2 was a negative regulator of herbaceous peony stem development, and its discovery and research could significantly contribute to a deeper understanding of stem growth and development mechanisms.

Hybrid deep learning model for vegetable price forecasting based on principal component analysis and attention mechanism

Abstract With the aim of enhancing the accuracy of current models for forecasting vegetable prices and improving market structures, this study focuses on the prices of bell peppers at the Nanhuanqiao Market in Suzhou. In this paper, we propose a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model for vegetable price forecasting based on Principal Component Analysis (PCA) and Attention Mechanism (ATT). Initially, we utilized the Pearson correlation coefficient to filter out the factors impacting prices. Then, we applied PCA to reduce dimensionality, extracting key price features. Next, we captured local sequence patterns with CNN, while handling time-series features with GRU. Finally, these outputs were integrated via ATT to generate the final prediction. Our results indicate that the hybrid CNN-GRU model, enhanced by PCA and ATT, achieved a Root Mean Square Error (RMSE) as low as 0.16. This performance is 11.11%, 11.11%, and 15.79% better than that of the PCA-CNN, PCA-GRU, and CNN-GRU-ATT models, respectively. Furthermore, in order to prove the effectiveness of our proposed model, the proposed model is compared with the state-of-the-art models and classical machine learning algorithms under the same dataset, the results indicate that our proposed hybrid deep learning model based on PCA and ATT shows the best performance. Consequently, our model offers a valuable reference for vegetable price prediction.

Nuclear-Cytoplasmic Shuttling of the Usher Syndrome 1G Protein SANS Differs from Its Paralog ANKS4B.

The USH1G protein SANS is a small multifunctional scaffold protein. It is involved in several different cellular processes, such as intracellular transport, in the cytoplasm, or splicing of pre-mRNA, in the cell nucleus. Here, we aimed to gain insight into the regulation of the subcellular localization and the nuclear-cytoplasmic shuttling of SANS and its paralog ANKS4B, not yet reported in the nucleus. We identified karyopherins mediating the nuclear import and export by screening the nuclear interactome of SANS. Sequence analyses predicted in silico evolutionarily conserved nuclear localization sequences (NLSs) and nuclear export sequences (NESs) in SANS, but only NESs in ANKS4B, which are suitable for karyopherin binding. Quantifying the nuclear-cytoplasmic localization of wild-type SANS and NLS/NES mutants, we experimentally confirmed in silico predicted NLS and NES functioning in the nuclear-cytoplasmic shuttling in situ in cells. The comparison of SANS and its paralog ANKS4B revealed substantial differences in the interaction with the nuclear splicing protein PRPF31 and in their nuclear localization. Finally, our results on pathogenic USH1G/SANS mutants suggest that the loss of NLSs and NESs and thereby the ability to control nuclear-cytoplasmic shuttling is disease-relevant.

Unveiling the functions of the Lim-domain binding protein MaPtaB in Metarhizium acridum.

The Lim-domain binding protein PtaB, a homolog of Mfg1, governs conidiation and biofilm formation in several fungi. PtaB includes a conserved Lim-binding domain and two predicted nuclear localization sequences at its C terminus, and is co-regulated with the transcription factor Som1 downstream of the cyclic AMP-dependent protein kinase A (cAMP/PKA) pathway. However, the function of PtaB in entomopathogenic fungi remain poorly understood. Inactivation of PtaB in Metarhizium acridum resulted in delayed conidial germination, reduced conidial yield and increased sensitivities to cell wall disruptors, ultraviolet B irradiation and heat shock. In addition, the fungal virulence was significantly decreased after deletion of MaPtaB because of impairments in appressorium formation, cuticle penetration and evasion of insect immune responses in M. acridum. The MaPtaB-deletion and MaSom1-deletion strains showed similar phenotypes supporting that MaSom1/MaPtaB complex controls M. acridum normal conidiation and pathogenic progress. Upon loss of MaPtaB or MaSom1, the fungal sporulation mode in M. acridium shifted from microcycle conidiation to normal conidiation on SYA, a microcycle conidiation medium. Transcriptional analysis showed that more differentially expression genes were identified in MaSom1 RNA sequencing, and MaSom1 and MaPtaB may regulate the expression of genes for conidiation, nutrient metabolism and the cell cycle to control conidiation pattern shift. These data corroborate a complex control function for MaPtaB as an important central factor interacting with MaSom1 in the cAMP/PKA pathway, which links stress tolerance, conidiation and virulence in the entomopathogenic fungus M. acridum. © 2024 Society of Chemical Industry.

Glacial and periglacial geomorphology of the eastern flank of the north Patagonian Andes: Quaternary morphoclimatic evolution

The cold climate events of the Upper Cenozoic greatly modified the landscape of Patagonia, where a large number of relicts and active landforms of glacial, periglacial and paraglacial origin is preserved. To assess the regional paleoclimatic record when numerical ages are unavailable, the spatial distribution of local depositional landform sequences was analyzed in six mountain ranges located on the eastern margin of the North Patagonian Andes. The analysis of satellite images and digital elevation model, fieldwork research and its integration into a Geographical Information System were used to develop a detailed inventory of mountain landforms. A total of 67 glacial valleys, 97 glacial cirques, 94 moraines, 75 rock glaciers, 103 protalus ramparts, and more than 145 km2 and 100 km2 of surface affected by solifluction and mass wasting processes respectively were mapped. From the analysis of the spatial and altitudinal distribution of landforms, we defined temporal and spatial sequences of morpho-climate importance. We recognized four major morpho-climate events associated with local paleoclimate conditions and one stage of a recent paraglacial adjustment. The oldest cold climate event recognized might be correlated with the Last Glacial Maximum. The results show the rise in the average air temperature and an aridity trend for the study area since the Late Pleistocene. During this lapse, glacial environments evolved to periglacial environments, although was recognized their coexistence in some areas in the same mountain range.

An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences

N4-methylcytosine (4mC) is a chemical modification that occurs on one of the four nucleotide bases in DNA and plays a vital role in DNA expression, repair, and replication. It also actively participates in the regulation of cell differentiation and gene expression. Consequently, it is important to comprehend the role of 4mC in the epigenetic regulation for revealing the complications of the gene expression and their associated governing cellular operations. However, the inherent resource requirements and time constraints of the experimental procedure, present challenges to the cellular culture process. While data-driven methodologies present promising solutions to mitigate the demand for extensive experimental efforts, their performance relies on the suitability and existence of high-quality data. This study presents a multi-model framework that integrates convolutional neural network (CNN) with the distributed k-mer and embedding feature extraction techniques to enhance the identification of 4mC sites in DNA sequences. The integration of k-mers ensures the effective representation of the local sequence patterns, while the utilization of embedding enables a more holistic encoding by considering the broader context and semantics of the sequence data. Following the initial step, the obtained distributed representation of the DNA sequence seamlessly enters the CNN, triggering a crucial convolution operation wherein a set of adaptable filters systematically convolves across the sequence to detect vital local patterns. The proposed integrated multi-model framework was applied to six publicly available datasets and evaluated against the cutting-edge 4mCPred, 4mCCNN, iDNA4mC, Meta-4mCpred, DeepTorrent, 4mCPred-SVM, and DMKL-HFIS methods. The evaluation was based on accuracy, specificity, sensitivity, and Matthews Correlation Coefficient. The results demonstrated that the proposed multi-model framework outperformed the state-of-the-art methods, as well as one-hot encoding and the hybrid of one-hot & TNC features, in accurately identifying 4mC sites.

Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Adsorption and Distribution of Triplex-Hybridizing Bioconjugated Gold Nanoclusters Inside Spermatozoa- A Study Using Confocal Microscopy and Fluorescence Lifetime Imaging.

The study of the interactions between biofunctionalized gold nanoclusters (Au NCs) and spermatozoa is highly relevant to evaluate the potential of Au NCs as imaging probes and transfection agents in reproductive biology. In this work, confocal laser scanning microscopy (CLSM) was used to investigate the distribution of Au NCs bioconjugated with peptide (nuclear localisation sequence, NLS) and oligonucleotide (locked nucleic acid, LNA) ligands in bovine spermatozoa. Fluorescence lifetime imaging (FLIM) was employed to detect changes in the NC's chemical environment. We observed a pronounced regio-selective accumulation of the bioconjugates in spermatozoa with high concentration at the equatorial segment. Furthermore, 3D-CLSM showed successful non-endosomal cellular uptake of the conjugates by intact sperm cells and the distribution of the bioconjugates was found to be influenced by the ligand types. Interestingly, the FLIM data showed differences in lifetime depending on membrane integrity. Furthermore, ligand-dependent changes in lifetime between NC bioconjugates carrying peptide and oligonucleotide ligands were found, probably attributed to specific interactions with sperm cell compartments.

A novel daily runoff forecasting model based on global features and enhanced local feature interpretation

The development of artificial intelligence has introduced new perspectives to the field of hydrological forecasting. However, there is still a lack of research on efficiently identifying the physical characteristics of runoff sequences and developing prediction models that consider global and local sequence features. This study proposes a parallel computing prediction model called IMCAEN (Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network) to address these issues. Unlike existing models, this model can monitor fluctuations and anomalies in time series. Incorporating the CDC-AA (Causal Dilated Convolutional Network with Aggregation Attention) and encoder structure captures both local sequence variations and global abrupt anomalies, allowing for comprehensive attention to sequence features. When predicting runoff data from three different hydrological conditions, the IMCAEN model achieved NSEC (Nash-Sutcliffe Efficiency Coefficient) values of 0.98, 0.97, and 0.88, respectively, and outperformed benchmark models in other evaluation indicators as well. Given the opacity of the feature distribution process in AI models, SHAP (SHapleyAdditive exPlanations) analysis and spatial expression of feature distribution are used to assess the contribution of each feature variable to the long-term trend of runoff and to verify the distribution of features trained in each module. The proposed IMCAEN model efficiently captures local and global information in the runoff evolution process through parallel computing and shared features, enabling accurate runoff forecasting and providing critical references for timely warnings and predictions.

Template switching during DNA replication is a prevalent source of adaptive gene amplification.

Copy number variants (CNVs)-gains and losses of genomic sequences-are an important source of genetic variation underlying rapid adaptation and genome evolution. However, despite their central role in evolution little is known about the factors that contribute to the structure, size, formation rate, and fitness effects of adaptive CNVs. Local genomic sequences are likely to be an important determinant of these properties. Whereas it is known that point mutation rates vary with genomic location and local DNA sequence features, the role of genome architecture in the formation, selection, and the resulting evolutionary dynamics of CNVs is poorly understood. Previously, we have found that the GAP1 gene in Saccharomyces cerevisiae undergoes frequent and repeated amplification and selection under long-term experimental evolution in glutamine-limiting conditions. The GAP1 gene has a unique genomic architecture consisting of two flanking long terminal repeats (LTRs) and a proximate origin of DNA replication (autonomously replicating sequence, ARS), which are likely to promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we performed experimental evolution in glutamine-limited chemostats using engineered strains lacking either the adjacent LTRs, ARS, or all elements. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. We find that although GAP1 CNVs repeatedly form and sweep to high frequency in strains with modified genome architecture, removal of local DNA elements significantly impacts the rate and fitness effect of CNVs and the rate of adaptation. We performed genome sequence analysis to define the molecular mechanisms of CNV formation for 177 CNV lineages. We find that across all four strain backgrounds, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, a distal ARS can mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination mechanisms still mediate gene amplification following de novo insertion of retrotransposon elements at the locus. Our study demonstrates the remarkable plasticity of the genome and reveals that template switching during DNA replication is a frequent source of adaptive CNVs.

MEDOC: A fast, scalable and mathematically exact algorithm for the site-specific prediction of the protonation degree in large disordered proteins.

Intrinsically disordered regions are found in most eukaryotic proteins and are enriched in positively and negatively charged residues. While it is often convenient to assume these residues follow their model-compound pK a values, recent work has shown that local charge effects (charge regulation) can upshift or downshift sidechain pK a values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions. The number of potential charge microstates that can be populated through acid/base regulation of a given number of ionizable residues in a sequence, N , scales as ∼2 N . This exponential scaling makes the assessment of the full charge landscape of most proteins computationally intractable. To address this problem, we developed MEDOC (Multisite Extent of Deprotonation Originating from Context) to determine the degree of protonation of a protein based on the local sequence context of each ionizable residue. We show that we can drastically reduce the number of parameters necessary to determine the full, analytic, Boltzmann partition function of the charge landscape at both global and site-specific levels. Our algorithm applies the structure of the q -canonical ensemble, combined with novel strategies to rapidly obtain the minimal set of parameters, thereby circumventing the combinatorial explosion of the number of charge microstates even for proteins containing a large number of ionizable amino acids. We apply MEDOC to several sequences, including a global analysis of the distribution of pK a values across the entire DisProt database. Our results show differences in the distribution of predicted pK a values for different amino acids, in agreement with NMR-measured distributions in proteins.

Nup210 Promotes Colorectal Cancer Progression by Regulating Nuclear Plasma Transport

The nuclear pore complex (NPC) regulates nucleoplasmic transport, transcription, and genomic integrity in eukaryotic cells. However, little is known about how NPC works in cancer. In this study, we investigated the role of the nuclear pore protein 210 (Nucleoporin 210, Nup210) in colorectal cancer (CRC). Bioinformatics analysis revealed that the expression of Nup210 was increased in CRC and was associated with poor patient prognosis, but it was not a statistically significant independent prognostic factor. Moreover, knockdown of Nup210 in CRC cells inhibited the proliferation, invasion, and metastasis of CRC cells in vivo and in vitro. Additionally, nuclear size and nuclear plasma material transport capacity decreased along with the number and density of NPCs on the surface of CRC cells when Nup210 expression was inhibited. Furthermore, Nup210 required nuclear localization sequences (NLS) to localize to the nuclear membrane surface and interact with importin-α/β, which in turn affected the transit of nuclear plasma material. Importazole, a small molecule inhibitor of importin, along with therapy that targets the Nup210 protein is anticipated to be a novel strategy for CRC treatment. Their combination may be able to more effectively lower CRC tumor load. In conclusion, Nup210 modulates cellular nucleoplasmic transport capability and cell surface NPC density via NLS, thus promoting CRC progression. This discovery validates the molecular function of NPC in the development of CRC and provides a theoretical foundation for NPC-regulated nuclear import targeting as a therapeutic strategy for CRC.