A random forest model for predicting exosomal proteins using evolutionary information and motifs

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence-based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif-based and ML-based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

Directed Network Comparison Using Motifs

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node's involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix, which is composed of the motif distribution vector of every node and the Jensen-Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models, as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.

Combinatorial expression motifs in signaling pathways

In animal cells, molecular pathways often comprise families of variant components, such as ligands or receptors. These pathway components are differentially expressed by different cell types, potentially tailoring pathway function to cell context. However, it has remained unclear how pathway expression profiles are distributed across cell types and whether similar profiles can occur in dissimilar cell types. Here, using single-cell gene expression datasets, we identified pathway expression motifs, defined as recurrent expression profiles that are broadly distributed across diverse cell types. Motifs appeared in core pathways, including TGF-β, Notch, Wnt, and the SRSF splice factors, and involved combinatorial co-expression of multiple components. Motif usage was weakly correlated between pathways in adult cell types and during dynamic developmental transitions. Together, these results suggest a mosaic view of cell type organization, in which different cell types operate many of the same pathways in distinct modes.

MkcDBGAS: a reference-free approach to identify comprehensive alternative splicing events in a transcriptome

Alternative splicing (AS) is an essential post-transcriptional mechanism that regulates many biological processes. However, identifying comprehensive types of AS events without guidance from a reference genome is still a challenge. Here, we proposed a novel method, MkcDBGAS, to identify all seven types of AS events using transcriptome alone, without a reference genome. MkcDBGAS, modeled by full-length transcripts of human and Arabidopsis thaliana, consists of three modules. In the first module, MkcDBGAS, for the first time, uses a colored de Bruijn graph with dynamic- and mixed- kmers to identify bubbles generated by AS with precision higher than 98.17% and detect AS types overlooked by other tools. In the second module, to further classify types of AS, MkcDBGAS added the motifs of exons to construct the feature matrix followed by the XGBoost-based classifier with the accuracy of classification greater than 93.40%, which outperformed other widely used machine learning models and the state-of-the-art methods. Highly scalable, MkcDBGAS performed well when applied to Iso-Seq data of Amborella and transcriptome of mouse. In the third module, MkcDBGAS provides the analysis of differential splicing across multiple biological conditions when RNA-sequencing data is available. MkcDBGAS is the first accurate and scalable method for detecting all seven types of AS events using the transcriptome alone, which will greatly empower the studies of AS in a wider field.

Analytic relationship of relative synchronizability to network structure and motifs

Synchronization phenomena on networks have attracted much attention in studies of neural, social, economic, and biological systems, yet we still lack a systematic understanding of how relative synchronizability relates to underlying network structure. Indeed, this question is of central importance to the key theme of how dynamics on networks relate to their structure more generally. We present an analytic technique to directly measure the relative synchronizability of noise-driven time-series processes on networks, in terms of the directed network structure. We consider both discrete-time autoregressive processes and continuous-time Ornstein-Uhlenbeck dynamics on networks, which can represent linearizations of nonlinear systems. Our technique builds on computation of the network covariance matrix in the space orthogonal to the synchronized state, enabling it to be more general than previous work in not requiring either symmetric (undirected) or diagonalizable connectivity matrices and allowing arbitrary self-link weights. More importantly, our approach quantifies the relative synchronization specifically in terms of the contribution of process motif (walk) structures. We demonstrate that in general the relative abundance of process motifs with convergent directed walks (including feedback and feedforward loops) hinders synchronizability. We also reveal subtle differences between the motifs involved for discrete or continuous-time dynamics. Our insights analytically explain several known general results regarding synchronizability of networks, including that small-world and regular networks are less synchronizable than random networks.

EasySSR: a user-friendly web application with full command-line features for large-scale batch microsatellite mining and samples comparison

Microsatellites, also known as SSRs or STRs, are polymorphic DNA regions with tandem repetitions of a nucleotide motif of size 1-6 base pairs with a broad range of applications in many fields, such as comparative genomics, molecular biology, and forensics. However, the majority of researchers do not have computational training and struggle while running command-line tools or very limited web tools for their SSR research, spending a considerable amount of time learning how to execute the software and conducting the post-processing data tabulation in other tools or manually-time that could be used directly in data analysis. We present EasySSR, a user-friendly web tool with command-line full functionality, designed for practical use in batch identifying and comparing SSRs in sequences, draft, or complete genomes, not requiring previous bioinformatic skills to run. EasySSR requires only a FASTA and an optional GENBANK file of one or more genomes to identify and compare STRs. The tool can automatically analyze and compare SSRs in whole genomes, convert GenBank to PTT files, identify perfect and imperfect SSRs and coding and non-coding regions, compare their frequencies, abundancy, motifs, flanking sequences, and iterations, producing many outputs ready for download such as PTT files, interactive charts, and Excel tables, giving the user the data ready for further analysis in minutes. EasySSR was implemented as a web application, which can be executed from any browser and is available for free at https://computationalbiology.ufpa.br/easyssr/. Tutorials, usage notes, and download links to the source code can be found at https://github.com/engbiopct/EasySSR.

Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets

Protein-protein interactions that involve recognition of short peptides are critical in cellular processes. Protein-peptide interaction surface areas are relatively small and shallow, and there are often overlapping specificities in families of peptide-binding domains. Therefore, dissecting selectivity determinants can be challenging. PDZ domains are a family of peptide-binding domains located in several intracellular signaling and trafficking pathways. These domains are also directly targeted by pathogens, and a hallmark of many oncogenic viral proteins is a PDZ-binding motif. However, amidst sequences that target PDZ domains, there is a wide spectrum in relative promiscuity. For example, the viral HPV16 E6 oncoprotein recognizes over double the number of PDZ domain-containing proteins as the cystic fibrosis transmembrane conductance regulator (CFTR) in the cell, despite similar PDZ targeting-sequences and identical motif residues. Here, we determine binding affinities for PDZ domains known to bind either HPV16 E6 alone or both CFTR and HPV16 E6, using peptides matching WT and hybrid sequences. We also use energy minimization to model PDZ-peptide complexes and use sequence analyses to investigate this difference. We find that while the majority of single mutations had marginal effects on overall affinity, the additive effect on the free energy of binding accurately describes the selectivity observed. Taken together, our results describe how complex and differing PDZ interactomes can be programmed in the cell.

Emergence of terpene chemical communication in insects: Evolutionary recruitment of isoprenoid metabolism

Insects have evolved a chemical communication system using terpenoids, a structurally diverse class of specialized metabolites, previously thought to be exclusively produced by plants and microbes. Gene discovery, bioinformatics, and biochemical characterization of multiple insect terpene synthases (TPSs) revealed that isoprenyl diphosphate synthases (IDS), enzymes from primary isoprenoid metabolism, are their likely evolutionary progenitors. However, the mutations underlying the emergence of the TPS function remain a mystery. To address this gap, we present the first structural and mechanistic model for the evolutionary emergence of TPS function in insects. Through identifying key mechanistic differences between IDS and TPS enzymes, we hypothesize that the loss of isopentenyl diphosphate (IPP) binding motifs strongly correlates with the gain of the TPS function. Based on this premise, we have elaborated the first explicit structural definition of isopentenyl diphosphate-binding motifs (IBMs) and used the IBM definitions to examine previously characterized insect IDSs and TPSs and to predict the functions of as yet uncharacterized insect IDSs. Consistent with our hypothesis, we observed a clear pattern of disruptive substitutions to IBMs in characterized insect TPSs. In contrast, insect IDSs maintain essential consensus residues for binding IPP. Extending our analysis, we constructed the most comprehensive phylogeny of insect IDS sequences (430 full length sequences from eight insect orders) and used IBMs to predict the function of TPSs. Based on our analysis, we infer multiple, independent TPS emergence events across the class of insects, paving the way for future gene discovery efforts. This article is protected by copyright. All rights reserved.

Additive energetic contributions of multiple peptide positions determine the relative promiscuity of viral and human sequences for PDZ domain targets

Protein-protein interactions that include recognition of short sequences of amino acids, or peptides, are critical in cellular processes. Protein-peptide interaction surface areas are relatively small and shallow, and there are often overlapping specificities in families of peptide-binding domains. Therefore, dissecting selectivity determinants can be challenging. PDZ domains are an example of a peptide-binding domain located in several intracellular signaling and trafficking pathways, which form interactions critical for the regulation of receptor endocytic trafficking, tight junction formation, organization of supramolecular complexes in neurons, and other biological systems. These domains are also directly targeted by pathogens, and a hallmark of many oncogenic viral proteins is a PDZ-binding motif. However, amidst sequences that target PDZ domains, there is a wide spectrum in relative promiscuity. For example, the viral HPV16 E6 oncoprotein recognizes over double the number of PDZ domain-containing proteins as the cystic fibrosis transmembrane conductance regulator (CFTR) in the cell, despite similar PDZ targeting-sequences and identical motif residues. Here, we determine binding affinities for PDZ domains known to bind either HPV16 E6 alone or both CFTR and HPV16 E6, using peptides matching WT and hybrid sequences. We also use energy minimization to model PDZ-peptide complexes and use sequence analyses to investigate this difference. We find that while the majority of single mutations had a marginal effect on overall affinity, the additive effect on the free energy of binding accurately describes the selectivity observed. Taken together, our results describe how complex and differing PDZ interactomes can be programmed in the cell.

The gene regulatory network of Staphylococcus aureus ST239-SCCmecIII strain Bmb9393 and assessment of genes associated with the biofilm in diverse backgrounds

Introduction

Staphylococcus aureus is one of the most prevalent and relevant pathogens responsible for a wide spectrum of hospital-associated or community-acquired infections. In addition, methicillin-resistant Staphylococcus aureus may display multidrug resistance profiles that complicate treatment and increase the mortality rate. The ability to produce biofilm, particularly in device-associated infections, promotes chronic and potentially more severe infections originating from the primary site. Understanding the complex mechanisms involved in planktonic and biofilm growth is critical to identifying regulatory connections and ways to overcome the global health problem of multidrug-resistant bacteria.

Methods

In this work, we apply literature-based and comparative genomics approaches to reconstruct the gene regulatory network of the high biofilm-producing strain Bmb9393, belonging to one of the highly disseminating successful clones, the Brazilian epidemic clone. To the best of our knowledge, we describe for the first time the topological properties and network motifs for the Staphylococcus aureus pathogen. We performed this analysis using the ST239-SCCmecIII Bmb9393 strain. In addition, we analyzed transcriptomes available in the literature to construct a set of genes differentially expressed in the biofilm, covering different stages of the biofilms and genetic backgrounds of the strains.

Results and discussion

The Bmb9393 gene regulatory network comprises 1,803 regulatory interactions between 64 transcription factors and the non-redundant set of 1,151 target genes with the inclusion of 19 new regulons compared to the N315 transcriptional regulatory network published in 2011. In the Bmb9393 network, we found 54 feed-forward loop motifs, where the most prevalent were coherent type 2 and incoherent type 2. The non-redundant set of differentially expressed genes in the biofilm consisted of 1,794 genes with functional categories relevant for adaptation to the variable microenvironments established throughout the biofilm formation process. Finally, we mapped the set of genes with altered expression in the biofilm in the Bmb9393 gene regulatory network to depict how different growth modes can alter the regulatory systems. The data revealed 45 transcription factors and 876 shared target genes. Thus, the gene regulatory network model provided represents the most up-to-date model for Staphylococcus aureus, and the set of genes altered in the biofilm provides a global view of their influence on biofilm formation from distinct experimental perspectives and different strain backgrounds.

Mining novel cis-regulatory elements from the emergent host Rhodosporidium toruloides using transcriptomic data

The demand for robust microbial cell factories that produce valuable biomaterials while resisting stresses imposed by current bioprocesses is rapidly growing. Rhodosporidium toruloides is an emerging host that presents desirable features for bioproduction, since it can grow in a wide range of substrates and tolerate a variety of toxic compounds. To explore R. toruloides suitability for application as a cell factory in biorefineries, we sought to understand the transcriptional responses of this yeast when growing under experimental settings that simulated those used in biofuels-related industries. Thus, we performed RNA sequencing of the oleaginous, carotenogenic yeast in different contexts. The first ones were stress-related: two conditions of high temperature (37 and 42°C) and two ethanol concentrations (2 and 4%), while the other used the inexpensive and abundant sugarcane juice as substrate. Differential expression and functional analysis were implemented using transcriptomic data to select differentially expressed genes and enriched pathways from each set-up. A reproducible bioinformatics workflow was developed for mining new regulatory elements. We then predicted, for the first time in this yeast, binding motifs for several transcription factors, including HAC1, ARG80, RPN4, ADR1, and DAL81. Most putative transcription factors uncovered here were involved in stress responses and found in the yeast genome. Our method for motif discovery provides a new realm of possibilities in studying gene regulatory networks, not only for the emerging host R. toruloides, but for other organisms of biotechnological importance.

Identification of potential driver mutations in glioblastoma using machine learning

Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.

Computational Analysis of Short Linear Motifs in the Spike Protein of SARS-CoV-2 Variants Provides Possible Clues into the Immune Hijack and Evasion Mechanisms of Omicron Variant

Short linear motifs (SLiMs) are short linear sequences that can mediate protein–protein interaction. Mimicking eukaryotic SLiMs to compete with extra- or intracellular binding partners, or to sequester host proteins is the crucial strategy of viruses to pervert the host system. Evolved proteins in viruses facilitate minimal protein–protein interactions that significantly affect intracellular signaling networks. Unfortunately, very little information about SARS-CoV-2 SLiMs is known, especially across SARS-CoV-2 variants. Through the ELM database-based sequence analysis of spike proteins from all the major SARS-CoV-2 variants, we identified four overriding SLiMs in the SARS-CoV-2 Omicron variant, namely, LIG_TRFH_1, LIG_REV1ctd_RIR_1, LIG_CaM_NSCaTE_8, and MOD_LATS_1. These SLiMs are highly likely to interfere with various immune functions, interact with host intracellular proteins, regulate cellular pathways, and lubricate viral infection and transmission. These cellular interactions possibly serve as potential therapeutic targets for these variants, and this approach can be further exploited to combat emerging SARS-CoV-2 variants.

ToxinPred2: an improved method for predicting toxicity of proteins

Proteins/peptides have shown to be promising therapeutic agents for a variety of diseases. However, toxicity is one of the obstacles in protein/peptide-based therapy. The current study describes a web-based tool, ToxinPred2, developed for predicting the toxicity of proteins. This is an update of ToxinPred developed mainly for predicting toxicity of peptides and small proteins. The method has been trained, tested and evaluated on three datasets curated from the recent release of the SwissProt. To provide unbiased evaluation, we performed internal validation on 80% of the data and external validation on the remaining 20% of data. We have implemented the following techniques for predicting protein toxicity; (i) Basic Local Alignment Search Tool-based similarity, (ii) Motif-EmeRging and with Classes-Identification-based motif search and (iii) Prediction models. Similarity and motif-based techniques achieved a high probability of correct prediction with poor sensitivity/coverage, whereas models based on machine-learning techniques achieved balance sensitivity and specificity with reasonably high accuracy. Finally, we developed a hybrid method that combined all three approaches and achieved a maximum area under receiver operating characteristic curve around 0.99 with Matthews correlation coefficient 0.91 on the validation dataset. In addition, we developed models on alternate and realistic datasets. The best machine learning models have been implemented in the web server named 'ToxinPred2', which is available at https://webs.iiitd.edu.in/raghava/toxinpred2/ and a standalone version at https://github.com/raghavagps/toxinpred2. This is a general method developed for predicting the toxicity of proteins regardless of their source of origin.

Characterization of WRKY Gene Family in Whole-Genome and Exploration of Flowering Improvement Genes in Chrysanthemum lavandulifolium

Chrysanthemum is a well-known ornamental plant with numerous uses. WRKY is a large family of transcription factors known for a variety of functions ranging from stress resistance to plant growth and development. Due to the limited research on the WRKY family in chrysanthemums, we examined them for the first time in Chrysanthemum lavandulifolium. A total of 138 ClWRKY genes were identified, which were classified into three groups. Group III in C. lavandulifolium contains 53 members, which is larger than group III of Arabidopsis. The number of introns varied from one to nine in the ClWRKY gene family. The "WRKYGQK" motif is conserved in 118 members, while other members showed slight variations. AuR and GRE responsive cis-acting elements were located in the promoter region of WRKY members, which are important for plant development and flowering induction. In addition, the W box was present in most genes; the recognition site for the WRKY gene may play a role in autoregulation and cross-regulation. The expression of the most variable 19 genes in terms of different parameters was observed at different stages. Among them, 10 genes were selected due to the presence of CpG islands, while nine genes were selected based on their close association with important Arabidopsis genes related to floral traits. ClWRKY36 and ClWRKY45 exhibit differential expression at flowering stages in the capitulum, while methylation is detected in three genes, including ClWRKY31, ClWRKY100, and ClWRKY129. Our results provide a basis for further exploration of WRKY members to find their functions in plant growth and development, especially in flowering traits.

Molecular Determinants and Specificity of mRNA with Alternatively-Spliced UPF1 Isoforms, Influenced by an Insertion in the 'Regulatory Loop'

The nonsense-mediated mRNA decay (NMD) pathway rapidly detects and degrades mRNA containing premature termination codons (PTCs). UP-frameshift 1 (UPF1), the master regulator of the NMD process, has two alternatively-spliced isoforms; one carries 353-GNEDLVIIWLR-363 insertion in the ‘regulatory loop (involved in mRNA binding)’. Such insertion can induce catalytic and/or ATPase activity, as determined experimentally; however, the kinetics and molecular level information are not fully understood. Herein, applying all-atom molecular dynamics, we probe the binding specificity of UPF1 with different GC- and AU-rich mRNA motifs and the influence of insertion to the viable control over UPF1 catalytic activity. Our results indicate two distinct conformations between 1B and RecA2 domains of UPF1: ‘open (isoform_2; without insertion)’ and ‘closed (isoform_1; with insertion)’. These structural movements correspond to an important stacking pattern in mRNA motifs, i.e., absence of stack formation in mRNA, with UPF1 isoform_2 results in the ‘open conformation’. Particularly, for UPF1 isoform_1, the increased distance between 1B and RecA2 domains has resulted in reducing the mRNA–UPF1 interactions. Lower fluctuating GC-rich mRNA motifs have better binding with UPF1, compared with AU-rich sequences. Except CCUGGGG, all other GC-rich motifs formed a 4-stack pattern with UPF1. High occupancy R363, D364, T627, and G862 residues were common binding GC-rich motifs, as were R363, N535, and T627 for the AU-rich motifs. The GC-rich motifs behave distinctly when bound to either of the isoforms; lower stability was observed with UPF1 isoform_2. The cancer-associated UPF1 variants (P533L/T and A839T) resulted in decreased protein–mRNA binding efficiency. Lack of mRNA stacking poses in the UPF1_P533T system significantly decreased UPF1-mRNA binding efficiency and increased distance between 1B-RecA2. These novel findings can serve to further inform NMD-associated mechanistic and kinetic studies.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Maximally selective single-cell target for circuit control in epilepsy models

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.

A Comprehensive Phylogenetic Analysis of the MAP4K Family in the Green Lineage

The kinase-mediated phosphorylation impacts every basic cellular process. While mitogen-activated protein kinase technology kinase kinases (MAP4Ks) are evolutionarily conserved, there is no comprehensive overview of the MAP4K family in the green lineage (Viridiplantae). In this study, we identified putative MAP4K members from representative species of the two core groups in the green lineage: Chlorophyta, which is a diverse group of green algae, and Streptophyta, which is mostly freshwater green algae and land plants. From that, we inferred the evolutionary relationships of MAP4K proteins through a phylogenetic reconstruction. Furthermore, we provided a classification of the MAP4Ks in the green lineage into three distinct.

Insight into Calcium-Binding Motifs of Intrinsically Disordered Proteins

Motifs within proteins help us categorize their functions. Intrinsically disordered proteins (IDPs) are rich in short linear motifs, conferring them many different roles. IDPs are also frequently highly charged and, therefore, likely to interact with ions. Canonical calcium-binding motifs, such as the EF-hand, often rely on the formation of stabilizing flanking helices, which are a key characteristic of folded proteins, but are absent in IDPs. In this study, we probe the existence of a calcium-binding motif relevant to IDPs. Upon screening several carefully selected IDPs using NMR spectroscopy supplemented with affinity quantification by colorimetric assays, we found calcium-binding motifs in IDPs which could be categorized into at least two groups-an Excalibur-like motif, sequentially similar to the EF-hand loop, and a condensed-charge motif carrying repetitive negative charges. The motifs show an affinity for calcium typically in the ~100 μM range relevant to regulatory functions and, while calcium binding to the condensed-charge motif had little effect on the overall compaction of the IDP chain, calcium binding to Excalibur-like motifs resulted in changes in compaction. Thus, calcium binding to IDPs may serve various structural and functional roles that have previously been underreported.

On the sensitivity of food webs to multiple stressors

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.

Epigenomic Landscape of Lyme Disease Spirochetes Reveals Novel Motifs

Borrelia burgdorferi, the etiological agent of Lyme disease, persists in nature through an enzootic cycle consisting of a vertebrate host and an Ixodes tick vector. The sequence motifs modified by two well-characterized restriction/modification (R/M) loci of B. burgdorferi type strain B31 were recently described, but the methylation profiles of other Lyme disease Borrelia bacteria have not been characterized. Here, the methylomes of B. burgdorferi type strain B31 and 7 clonal derivatives, along with B. burgdorferi N40, B. burgdorferi 297, B. burgdorferi CA-11, B. afzelii PKo, B. afzelii BO23, and B. garinii PBr, were defined through PacBio single-molecule real-time (SMRT) sequencing. This analysis revealed 9 novel sequence motifs methylated by the plasmid-encoded restriction/modification enzymes of these Borrelia strains. Furthermore, while a previous analysis of B. burgdorferi B31 revealed an epigenetic impact of methylation on the global transcriptome, the current data contradict those findings; our analyses of wild-type B. burgdorferi B31 revealed no consistent differences in gene expression among isogenic derivatives lacking one or more restriction/modification enzymes. IMPORTANCE The principal causative agent of Lyme disease in humans in the United States is Borrelia burgdorferi, while B. burgdorferi, B. afzelii, and B. garinii, collectively members of the Borrelia burgdorferi sensu lato species complex, cause Lyme disease in Europe and Asia. Two plasmid-encoded restriction/modification systems have been shown to limit the genetic transformation of B. burgdorferi type strain B31 with foreign DNA, but little is known about the restriction/modification systems of other Lyme disease Borrelia bacteria. This paper describes the methylation motifs present on genomic DNAs of multiple B. burgdorferi, B. afzelii, and B. garinii strains. Contrary to a previous report, we did not find evidence for an epigenetic impact on gene expression by methylation. Knowledge of the motifs recognized and methylated by the restriction/modification enzymes of Lyme disease Borrelia will facilitate molecular genetic investigations of these important human pathogens. Additionally, the similar motifs methylated by orthologous restriction/modification systems of Lyme disease Borrelia bacteria and the presence of these motifs within recombinogenic loci suggest a biological role for these ubiquitous restriction/modification systems in horizontal gene transfer.

Computational Analysis of Domains Vulnerable to HPV-16 E6 Oncoprotein and Corresponding Hot Spot Residues

BACKGROUND

Human Papilloma Virus (HPV) is the primary cause of cancers in cervix, head and neck regions. Oncoprotein E6 of HPV-16, after infecting human body, alters host protein- protein interaction networks. E6 interacts with several proteins, causing the infection to progress into cervical cancer. The molecular basis for these interactions is the presence of short linear peptide motifs on E6 identical to those on human proteins.

METHODS

Motifs of LXXLL and E/DLLL/V-G after identification on E6, were analyzed for their dynamic fluctuations by use of elastic network models. Correlation analysis of amino acid residues of E6 was also performed in specific regions of motifs.

RESULTS

Arginine, Leucine, Glutamine, Threonine and Glutamic acid have been identified as hot spot residues of E6 which can subsequently provide a platform for drug designing and understanding of pathogenesis of cervical cancer. These amino acids play a significant role in stabilizing interactions with host proteins, ultimately causing infections and cancers.

CONCLUSION

Our study validates the role of linear binding motifs of E6 of HPV in interacting with these proteins as an important event in the propagation of HPV in human cells and its transformation into cervical cancer. The study further predicts the domains of protein kinase and armadillo as part of the regions involved in the interaction of E6AP, Paxillin and TNF R1, with viral E6.

Elucidation of Novel cis-Regulatory Elements and Promoter Structures Involved in Iron Excess Response Mechanisms in Rice Using a Bioinformatics Approach

Iron (Fe) excess is a major constraint on crop production in flooded acidic soils, particularly in rice cultivation. Under Fe excess, plants activate a complex mechanism and network regulating Fe exclusion by roots and isolation in various tissues. In rice, the transcription factors and cis-regulatory elements (CREs) that regulate Fe excess response mechanisms remain largely elusive. We previously reported comprehensive microarray analyses of several rice tissues in response to various levels of Fe excess stress. In this study, we further explored novel CREs and promoter structures in rice using bioinformatics approaches with this microarray data. We first performed network analyses to predict Fe excess-related CREs through the categorization of the gene expression patterns of Fe excess-responsive transcriptional regulons, and found four major expression clusters: Fe storage type, Fe chelator type, Fe uptake type, and WRKY and other co-expression type. Next, we explored CREs within these four clusters of gene expression types using a machine-learning method called microarray-associated motif analyzer (MAMA), which we previously established. Through a comprehensive bioinformatics approach, we identified a total of 560 CRE candidates extracted by MAMA analyses and 42 important conserved sequences of CREs directly related to the Fe excess response in various rice tissues. We explored several novel cis-elements as candidate Fe excess CREs including GCWGCWGC, CGACACGC, and Myb binding-like motifs. Based on the presence or absence of candidate CREs using MAMA and known PLACE CREs, we found that the Boruta-XGBoost model explained expression patterns with high accuracy of about 83%. Enriched sequences of both novel MAMA CREs and known PLACE CREs led to high accuracy expression patterns. We also found new roles of known CREs in the Fe excess response, including the DCEp2 motif, IDEF1-, Zinc Finger-, WRKY-, Myb-, AP2/ERF-, MADS- box-, bZIP and bHLH- binding sequence-containing motifs among Fe excess-responsive genes. In addition, we built a molecular model and promoter structures regulating Fe excess-responsive genes based on new finding CREs. Together, our findings about Fe excess-related CREs and conserved sequences will provide a comprehensive resource for discovery of genes and transcription factors involved in Fe excess-responsive pathways, clarification of the Fe excess response mechanism in rice, and future application of the promoter sequences to produce genotypes tolerant of Fe excess.

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

Epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa

COVID-19 disease dynamics have been widely studied in different settings around the globe, but little is known about these patterns in the African continent. To investigate the epidemiology and genetic diversity of SARS-CoV-2 lineages circulating in Africa, more than 2400 complete genomes from 33 African countries were retrieved from the GISAID database and analyzed. We investigated their diversity using various clade and lineage nomenclature systems, reconstructed their evolutionary divergence and history using maximum likelihood inference methods, and studied the case and death trends in the continent. We also examined potential repeat patterns and motifs across the sequences. In this study, we show that after almost one year of the COVID-19 pandemic, only 143 out of the 782 Pango lineages found worldwide circulated in Africa, with five different lineages dominating in distinct periods of the pandemic. Analysis of the number of reported deaths in Africa also revealed large heterogeneity across the continent. Phylogenetic analysis revealed that African viruses cluster closely with those from all continents but more notably with viruses from Europe. However, the extent of viral diversity observed among African genomes is closest to that of the Oceania outbreak, most likely due to genomic under-surveillance in Africa. We also identified two motifs that could function as integrin-binding sites and N-glycosylation domains. These results shed light on the evolutionary dynamics of the circulating viral strains in Africa, elucidate the functions of protein motifs present in the genome sequences, and emphasize the need to expand genomic surveillance efforts in the continent to better understand the molecular, evolutionary, epidemiological, and spatiotemporal dynamics of the COVID-19 pandemic in Africa.

Avian Influenza H7N9 Virus Adaptation to Human Hosts

Avian influenza virus A (H7N9), after circulating in avian hosts for decades, was identified as a human pathogen in 2013. Herein, amino acid substitutions possibly essential for human adaptation were identified by comparing the 4706 aligned overlapping nonamer position sequences (1–9, 2–10, etc.) of the reported 2014 and 2017 avian and human H7N9 datasets. The initial set of virus sequences (as of year 2014) exhibited a total of 109 avian-to-human (A2H) signature amino acid substitutions. Each represented the most prevalent substitution at a given avian virus nonamer position that was selectively adapted as the corresponding index (most prevalent sequence) of the human viruses. The majority of these avian substitutions were long-standing in the evolution of H7N9, and only 17 were first detected in 2013 as possibly essential for the initial human adaptation. Strikingly, continued evolution of the avian H7N9 virus has resulted in avian and human protein sequences that are almost identical. This rapid and continued adaptation of the avian H7N9 virus to the human host, with near identity of the avian and human viruses, is associated with increased human infection and a predicted greater risk of human-to-human transmission.

Insights into the Dynamic Fluctuations of the Protein HPV16 E1 and Identification of Motifs by Using Elastic Network Modeling

BACKGROUND

Cancers of cervix, head and neck regions have been found to be associated with Human Papilloma Virus (HPV) infection. E1 protein makes an important papillomavirus replication factor. Among the ORFs of papillomaviruses, the most conserved sequence is that of the E1 ORF. It is the viral helicase with being a member of class of ATPases associated with diverse cellular activities (AAA+) helicases. The interactions of E1 with human DNA and proteins occurs in the presence of short linear peptide motifs on E1 identical to those on human proteins.

METHODS

Different Motifs were identified on HPV16 E1 by using ELMs. Elastic network models were generated by using 3D structures of E1. Their dynamic fluctuations were analyzed on the basis of B factors, correlation analysis and deformation energies.

RESULTS

3 motifs were identified on E1 which can interact with Cdk and Cyclin domains of human proteins. 11 motifs identified on E1 have their CDs of Pkinase on human proteins. LIG_MYND_2 has been identified as involved in stabilizing interaction of E1 with Hsp40 and Hsp70. These motifs and amino acids comprising these motifs play a major role in maintaining interactions with human proteins, ultimately causing infections leading to cancers.

CONCLUSION

Our study identified various motifs on E1 which interact with specific counter domains found in human proteins, already reported having the interactions with E1. We also validated the involvement of these specific motifs containing regions of E1 by modeling elastic networks of E1. These motif involving interactions could be used as drug targets.

New Putative Antimicrobial Candidates: In silico Design of Fish-Derived Antibacterial Peptide-Motifs

Antimicrobial resistance remains a great threat to global health. In response to the World Health Organizations’ global call for action, nature has been explored for novel and safe antimicrobial candidates. To date, fish have gained recognition as potential source of safe, broad spectrum and effective antimicrobial therapeutics. The use of computational methods to design antimicrobial candidates of industrial application has however, been lagging behind. To fill the gap and contribute to the current fish-derived antimicrobial peptide repertoire, this study used Support Vector Machines algorithm to fish out fish-antimicrobial peptide-motif candidates encrypted in 127 peptides submitted at the Antimicrobial Peptide Database (APD3), steered by their physico-chemical characteristics (i.e., positive net charge, hydrophobicity, stability, molecular weight and sequence length). The best two novel antimicrobial peptide-motifs (A15_B, A15_E) with the lowest instability index (−28.25, −22.49, respectively) and highest isoelectric point (pI) index (10.48 for each) were selected for further analysis. Their 3D structures were predicted using I-TASSER and PEP-FOLD servers while ProSA, PROCHECK, and ANOLEA were used to validate them. The models predicted by I-TASSER were found to be better than those predicted by PEP-FOLD upon validation. Two I-TASSER models with the lowest c-score of −0.10 and −0.30 for A15_B and A15_E peptide-motifs, respectively, were selected for docking against known bacterial-antimicrobial target-proteins retrieved from protein databank (PDB). Carbapenam-3-carboxylate synthase (PDB ID; 4oj8) yielded the lowest docking energy (−8.80 and −7.80 Kcal/mol) against motif A15_B and A15_E, respectively, using AutoDock VINA. Further, in addition to Carbapenam-3-carboxylate synthase, these peptides (A15_B and A15_E) were found to as well bind to membrane protein (PDB ID: 1by3) and Carbapenem synthetase (PDB: 1q15) when ClusPro and HPEPDOCK tools were used. The membrane protein yielded docking energy scores (DES): −290.094, −270.751; coefficient weight (CW): −763.6, 763.3 for A15_B and A15_E) whereas, Carbapenem synthetase (PDB: 1q15) had a DES of −236.802, −262.75 and a CW of −819.7, −829.7 for peptides A15_B and A15_E, respectively. Motif A15_B of amino acid positions 2–19 in Pleurocidin exhibited the strongest in silico antimicrobial potentials. This segment could be a good biological candidate of great application in pharmaceutical industries as an antimicrobial drug candidate.

Antimicrobial Peptides: Classification, Design, Application and Research Progress in Multiple Fields

Antimicrobial peptides (AMPs) are a class of small peptides that widely exist in nature and they are an important part of the innate immune system of different organisms. AMPs have a wide range of inhibitory effects against bacteria, fungi, parasites and viruses. The emergence of antibiotic-resistant microorganisms and the increasing of concerns about the use of antibiotics resulted in the development of AMPs, which have a good application prospect in medicine, food, animal husbandry, agriculture and aquaculture. This review introduces the progress of research on AMPs comprehensively and systematically, including their classification, mechanism of action, design methods, environmental factors affecting their activity, application status, prospects in various fields and problems to be solved. The research progress on antivirus peptides, especially anti-coronavirus (COVID-19) peptides, has been introduced given the COVID-19 pandemic worldwide in 2020.

Chirality and Surface Bonding Correlation in Atomically Precise Metal Nanoclusters

Chirality is ubiquitous in nature and occurs at all length scales. The development of applications for chiral nanostructures is rising rapidly. With the recent achievements of atomically precise nanochemistry, total structures of ligand-protected Au and other metal nanoclusters (NCs) are successfully obtained, and the origins of chirality are discovered to be associated with different parts of the cluster, including the surface ligands (e.g., swirl patterns), the organic-inorganic interface (e.g., helical stripes), and the kernel. Herein, a unified picture of metal-ligand surface bonding-induced chirality for the nanoclusters is proposed. The different bonding modes of M-X (where M = metal and X = the binding atom of ligand) lead to different surface structures on nanoclusters, which in turn give rise to various characteristic features of chirality. A comparison of Au-thiolate NCs with Au-phosphine ones further reveals the important roles of surface bonding. Compared to the Au-thiolate NCs, the Ag/Cu/Cd-thiolate systems exhibit different coordination modes between the metal and the thiolate. Other than thiolate and phosphine ligands, alkynyls are also briefly discussed. Several methods of obtaining chiroptically active nanoclusters are introduced, such as enantioseparation by high-performance liquid chromatography and enantioselective synthesis. Future perspectives on chiral NCs are also proposed.

Structural characterization and computational analysis of PDZ domains in Monosiga brevicollis

Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular-like "rosette" state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed that a number of putative human orthologs are present in choanoflagellate genomes, suggesting that a subset of these genes were necessary for the emergence of multicellularity. However, previous work is largely based on sequence alignments alone, which does not confirm structural nor functional similarity. Here, we focus on the PDZ domain, a peptide-binding domain which plays critical roles in myriad cellular signaling networks and which underwent a gene family expansion in metazoan lineages. Using a customized sequence similarity search algorithm, we identified 178 PDZ domains in the Monosiga brevicollis proteome. This includes 11 previously unidentified sequences, which we analyzed using Rosetta and homology modeling. To assess conservation of protein structure, we solved high-resolution crystal structures of representative M. brevicollis PDZ domains that are homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains. To assess functional conservation, we calculated binding affinities for mbGIPC, mbSHANK1, mbSNX27, and mbDLG-3 PDZ domains from M. brevicollis. Overall, we find that peptide selectivity is generally conserved between these two disparate organisms, with one possible exception, mbDLG-3. Overall, our results provide novel insight into signaling pathways in a choanoflagellate model of primitive multicellularity.

A phylogenetic view and functional annotation of the animal β1,3-glycosyltransferases of the GT31 CAZy family

The formation of β1,3-linkages on animal glycoconjugates is catalyzed by a subset of β1,3-glycosyltransferases grouped in the Carbohydrate-Active enZYmes family glycosyltransferase-31 (GT31). This family represents an extremely diverse set of β1,3-N-acetylglucosaminyltransferases [B3GNTs and Fringe β1,3-N-acetylglucosaminyltransferases], β1,3-N-acetylgalactosaminyltransferases (B3GALNTs), β1,3-galactosyltransferases [B3GALTs and core 1 β1,3-galactosyltransferases (C1GALTs)], β1,3-glucosyltransferase (B3GLCT) and β1,3-glucuronyl acid transferases (B3GLCATs or CHs). The mammalian enzymes were particularly well studied and shown to use a large variety of sugar donors and acceptor substrates leading to the formation of β1,3-linkages in various glycosylation pathways. In contrast, there are only a few studies related to other metazoan and lower vertebrates GT31 enzymes and the evolutionary relationships of these divergent sequences remain obscure. In this study, we used bioinformatics approaches to identify more than 920 of putative GT31 sequences in Metazoa, Fungi and Choanoflagellata revealing their deep ancestry. Sequence-based analysis shed light on conserved motifs and structural features that are signatures of all the GT31. We leverage pieces of evidence from gene structure, phylogenetic and sequence-based analyses to identify two major subgroups of GT31 named Fringe-related and B3GALT-related and demonstrate the existence of 10 orthologue groups in the Urmetazoa, the hypothetical last common ancestor of all animals. Finally, synteny and paralogy analysis unveiled the existence of 30 subfamilies in vertebrates, among which 5 are new and were named C1GALT2, C1GALT3, B3GALT8, B3GNT10 and B3GNT11. Altogether, these various approaches enabled us to propose the first comprehensive analysis of the metazoan GT31 disentangling their evolutionary relationships.

EDeepSSP: Explainable deep neural networks for exact splice sites prediction

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.

Deriving pairwise transfer entropy from network structure and motifs

Transfer entropy (TE) is an established method for quantifying directed statistical dependencies in neuroimaging and complex systems datasets. The pairwise (or bivariate) TE from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in. This relationship is studied using a discrete-time linearly coupled Gaussian model, which allows us to derive the TE for each link from the network topology. It is shown analytically that the dependence on the directed link weight is only a first approximation, valid for weak coupling. More generally, the TE increases with the in-degree of the source and decreases with the in-degree of the target, indicating an asymmetry of information transfer between hubs and low-degree nodes. In addition, the TE is directly proportional to weighted motif counts involving common parents or multiple walks from the source to the target, which are more abundant in networks with a high clustering coefficient than in random networks. Our findings also apply to Granger causality, which is equivalent to TE for Gaussian variables. Moreover, similar empirical results on random Boolean networks suggest that the dependence of the TE on the in-degree extends to nonlinear dynamics.

Deep learning for inferring transcription factor binding sites

Deep learning is a powerful tool for predicting transcription factor binding sites from DNA sequence. Despite their high predictive accuracy, there are no guarantees that a high-performing deep learning model will learn causal sequence-function relationships. Thus a move beyond performance comparisons on benchmark datasets is needed. Interpreting model predictions is a powerful approach to identify which features drive performance gains and ideally provide insight into the underlying biological mechanisms. Here we highlight timely advances in deep learning for genomics, with a focus on inferring transcription factors binding sites. We describe recent applications, model architectures, and advances in local and global model interpretability methods, then conclude with a discussion on future research directions.

motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs

With an exponential growth in applications identifying protein post-translational modifications via mass spectrometry, discovery and presentation of motifs surrounding those modification sites have become increasingly desirable. Despite a few tools being designed, there is still a scarcity of effective and polyfunctional software for such analysis and illustrations. In this study, a versatile and user-friendly web tool is developed, motifeR, for extracting and visualizing statistically significant motifs from large datasets. Particularly, several functions are also integrated for processing multi-modification sites enrichment. Public datasets are applied to test their usability, indicating that some concurrent modification sites may form motifs and that peptides with low location probability may be not identified randomly and can be included to support motif discovery. In addition, for human phosphoproteomics datasets, the characterization of differential kinase signaling networks can be estimated and modeled by combining kinase-substrate relations based on the NetworKIN database as an optional feature for users. The motifeR toolkit can be conveniently operated by any scientific community or individuals, even those without any bioinformatics background and is freely available at https://www.omicsolution.org/wukong/motifeR.

Recurrence Resonance" in Three-Neuron Motifs

Stochastic Resonance (SR) and Coherence Resonance (CR) are non-linear phenomena, in which an optimal amount of noise maximizes an objective function, such as the sensitivity for weak signals in SR, or the coherence of stochastic oscillations in CR. Here, we demonstrate a related phenomenon, which we call "Recurrence Resonance" (RR): noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs with ternary connection strengths that the mutual information between successive network states can be maximized by adding a suitable amount of noise to the neuron inputs. This striking result suggests that noise in the brain may not be a problem that needs to be suppressed, but indeed a resource that is dynamically regulated in order to optimize information processing.

Atlas of Subcellular RNA Localization Revealed by APEX-Seq

We introduce APEX-seq, a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome as a resource, revealing extensive patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally. We show that, when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overexpression of specific 2- and 3-neuron motifs, distributions of connection numbers in clusters of 3-8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. In addition, memories loaded into the network can be retrieved, even in the presence of noise that is comparable with the baseline variations in the postsynaptic potential. The confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning. We predict that overexpression of excitatory-excitatory bidirectional connections observed in many cortical systems must be accompanied with underexpression of bidirectionally connected inhibitory-excitatory neuron pairs.SIGNIFICANCE STATEMENT Many structural and dynamical properties of local cortical networks are ubiquitously present across areas and species. Because synaptic connectivity is shaped by experience, we wondered whether continual learning, rather than genetic control, is responsible for producing such features. To answer this question, we developed a biologically constrained recurrent network of excitatory and inhibitory neurons capable of learning predefined sequences of network states. Embedding such associative memories into the network revealed that, when individual neurons are robustly loaded with a near-maximum amount of memories they can support, the network develops many properties that are consistent with experimental observations. Our findings suggest that basic structural and dynamical properties of local networks in the brain are simply a byproduct of learning and memory storage.

Transcriptomic analyses reveal groups of co-expressed, syntenic lncRNAs in four species of the genus Caenorhabditis

Long non-coding RNAs (lncRNAs) are a heterogeneous class of genes that do not code for proteins. Since lncRNAs (or a fraction thereof) are expected to be functional, many efforts have been dedicated to catalog lncRNAs in numerous organisms, but our knowledge of lncRNAs in non vertebrate species remains very limited. Here, we annotated lncRNAs using transcriptomic data from the same larval stage of four Caenorhabditis species. The number of annotated lncRNAs in self-fertile nematodes was lower than in out-crossing species. We used a combination of approaches to identify putatively homologous lncRNAs: synteny, sequence conservation, and structural conservation. We classified a total of 1,532 out of 7,635 genes from the four species into families of lncRNAs with conserved synteny and expression at the larval stage, suggesting that a large fraction of the predicted lncRNAs may be species specific. Despite both sequence and local secondary structure seem to be poorly conserved, sequences within families frequently shared BLASTn hits and short sequence motifs, which were more likely to be unpaired in the predicted structures. We provide the first multi-species catalog of lncRNAs in nematodes and identify groups of lncRNAs with conserved synteny and expression, that share exposed motifs.

miRNAmotif-A Tool for the Prediction of Pre-miRNA⁻Protein Interactions

MicroRNAs (miRNAs) are short, non-coding post-transcriptional gene regulators. In mammalian cells, mature miRNAs are produced from primary precursors (pri-miRNAs) using canonical protein machinery, which includes Drosha/DGCR8 and Dicer, or the non-canonical mirtron pathway. In plant cells, mature miRNAs are excised from pri-miRNAs by the DICER-LIKE1 (DCL1) protein complex. The involvement of multiple regulatory proteins that bind directly to distinct miRNA precursors in a sequence- or structure-dependent manner adds to the complexity of the miRNA maturation process. Here, we present a web server that enables searches for miRNA precursors that can be recognized by diverse RNA-binding proteins based on known sequence motifs to facilitate the identification of other proteins involved in miRNA biogenesis. The database used by the web server contains known human, murine, and Arabidopsis thaliana pre-miRNAs. The web server can also be used to predict new RNA-binding protein motifs based on a list of user-provided sequences. We show examples of miRNAmotif applications, presenting precursors that contain motifs recognized by Lin28, MCPIP1, and DGCR8 and predicting motifs within pre-miRNA precursors that are recognized by two DEAD-box helicases—DDX1 and DDX17. miRNAmotif is released as an open-source software under the MIT License. The code is available at GitHub (www.github.com/martynaut/mirnamotif). The webserver is freely available at http://mirnamotif.ibch.poznan.pl.

Borders of Cis-Regulatory DNA Sequences Preferentially Harbor the Divergent Transcription Factor Binding Motifs in the Human Genome

Changes in cis-regulatory DNA sequences and transcription factor (TF) repertoires provide major sources of phenotypic diversity that shape the evolution of gene regulation in eukaryotes. The DNA-binding specificities of TFs may be diversified or produce new variants in different eukaryotic species. However, it is currently unclear how various levels of divergence in TF DNA-binding specificities or motifs became introduced into the cis-regulatory DNA regions of the genome over evolutionary time. Here, we first estimated the evolutionary divergence levels of TF binding motifs and quantified their occurrence at DNase I-hypersensitive sites. Results from our in silico motif scan and experimentally derived chromatin immunoprecipitation (TF-ChIP) show that the divergent motifs tend to be introduced in the edges of cis-regulatory regions, which is probably accompanied by the expansion of the accessible core of promoter-associated regulatory elements during evolution. We also find that the genes neighboring the expanded cis-regulatory regions with the most divergent motifs are associated with functions like development and morphogenesis. Accordingly, we propose that the accumulation of divergent motifs in the edges of cis-regulatory regions provides a functional mechanism for the evolution of divergent regulatory circuits.

Exploration of cotton leaf curl virus resistance genes and their screening in Gossypium arboreum by targeting resistance gene analogues

Cotton leaf curl virus (CLCuV) disease is one of the major limiting factors in cotton production, particularly in widely cultivated Gossypium hirsutum varieties that are susceptible to attack by this virus. Several approaches have been employed to explore putative resistance genes in another cotton species, G. arboreum. However, the exact mechanisms conferring disease resistance in cotton are still unknown. In the current study, we used various approaches to identify possible resistance genes against CLCuV infection. We report the identification and isolation of a set of genes involved in the resistance response to viral infestation. PCR products containing genomic DNA gave multiple amplifications with a single primer in most reactions, and 38 fragments were cloned from G. arboreum and G. hirsutum. The sequences of cloned fragments belonged to various pathway genes and uncharacterized proteins. However, five amplified fragments (RM1, RM6, RM8, RM12 and RM31) showed similarity with R genes. Maximum homology (94 %) was observed with G. raimondii toll/interleukin receptor-like protein. BLAST search showed the homology of all resistance gene analogues (RGAs) with more than one chromosome, and multiple hits were observed on each chromosome for each RGA. Expression analysis through RT-PCR identified variable expression levels of the different RGAs in all tested genotypes. The expression level of RGAs differed between symptomatic and asymptomatic plants, with the exception of RGA 395, whose expression level was the same in both diseased and healthy plants. Knowledge of the interaction of these genes with various cotton pathogens could be utilized to improve the resistance of susceptible G. hirsutum and other plant species.

Septin mutations and phenotypes in S. cerevisiae

Septins are highly conserved guanosine triphosphate (GTP)-binding proteins that are a component of the cytoskeletal systems of virtually all eukaryotes (except higher plants). Septins play important roles in a multitude of cellular processes, including cytokinesis, establishment of cell polarity, and cellular partitioning. The ease of genetic screens and a fully sequenced genome have made Saccharomyces cerevisiae one of the most extensively studied and well-annotated model organisms in eukaryotic biology. Here, we present a synopsis of the known point mutations in the seven S. cerevisiae septin genes: CDC3, CDC10, CDC11, CDC12, SHS1, SPR3, and SPR28. We map these mutations onto septin protein structures, highlighting important conserved motifs, and relating the functional consequences of mutations in each domain.

Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks

Sequence-specific transcription factors (TFs) bind short DNA sequences in the genome to regulate the expression of target genes. In the last decade, numerous technical advances have enabled the determination of the DNA-binding specificities of many of these factors. Large-scale screens of many TFs enabled the creation of databases of TF DNA-binding specificities, typically represented as position weight matrices (PWMs). Although great progress has been made in determining and predicting binding specificities systematically, there are still many surprises to be found when studying a particular TF's interactions with DNA in detail. Paralogous TFs' binding specificities can differ in subtle ways, in a manner that is not immediately apparent from looking at their PWMs. These differences affect gene regulatory outputs and enable TFs to rewire transcriptional networks over evolutionary time. This review discusses recent observations made in the study of TF-DNA interactions that highlight the importance of continued in-depth analysis of TF-DNA interactions and their inherent complexity. This article is categorized under: Biological Mechanisms > Regulatory Biology.

N-Terminal Cu-Binding Motifs (Xxx-Zzz-His, Xxx-His) and Their Derivatives: Chemistry, Biology and Medicinal Applications

Peptides and proteins with N-terminal amino acid sequences NH2 -Xxx-His (XH) and NH2 -Xxx-Zzz-His (XZH) form well-established high-affinity CuII -complexes. Key examples are Asp-Ala-His (in serum albumin) and Gly-His-Lys, the wound healing factor. This opens a straightforward way to add a high-affinity CuII -binding site to almost any peptide or protein, by chemical or recombinant approaches. Thus, these motifs, NH2 -Xxx-Zzz-His in particular, have been used to equip peptides and proteins with a multitude of functions based on the redox activity of Cu, including nuclease, protease, glycosidase, or oxygen activation properties, useful in anticancer or antimicrobial drugs. More recent research suggests novel biological functions, mainly based on the redox inertness of CuII in XZH, like PET imaging (with 64 Cu), chelation therapies (for instance in Alzheimer's disease and other types of neurodegeneration), antioxidant units, Cu transporters and activation of biological functions by strong CuII binding. This Review gives an overview of the chemical properties of Cu-XH and -XZH motifs and discusses the pros and cons of the vastly different biological applications, and how they could be improved depending on the application.

In Silico Approach for Prediction of Antifungal Peptides

This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp).

RNA Dependent RNA Polymerases: Insights from Structure, Function and Evolution

RNA dependent RNA polymerase (RdRp) is one of the most versatile enzymes of RNA viruses that is indispensable for replicating the genome as well as for carrying out transcription. The core structural features of RdRps are conserved, despite the divergence in their sequences. The structure of RdRp resembles that of a cupped right hand and consists of fingers, palm and thumb subdomains. The catalysis involves the participation of conserved aspartates and divalent metal ions. Complexes of RdRps with substrates, inhibitors and metal ions provide a comprehensive view of their functional mechanism and offer valuable insights regarding the development of antivirals. In this article, we provide an overview of the structural aspects of RdRps and their complexes from the Group III, IV and V viruses and their structure-based phylogeny.

Microbial P450 Enzymes in Bioremediation and Drug Discovery: Emerging Potentials and Challenges

Cytochrome P450 enzymes are a structurally conserved but functionally diverse group of heme-containing mixed function oxidases found across both prokaryotic and eukaryotic forms of the microbial world. Microbial P450s are known to perform diverse functions ranging from the synthesis of cell wall components to xenobiotic/drug metabolism to biodegradation of environmental chemicals. Conventionally, many microbial systems have been reported to mimic mammalian P450-like activation of drugs and were proposed as the in-vitro models of mammalian drug metabolism. Recent reports suggest that native or engineered forms of specific microbial P450s from these and other microbial systems could be employed for desired specific biotransformation reactions toward natural and synthetic (drug) compounds underscoring their emerging potential in drug improvement and discovery. On the other hand, microorganisms particularly fungi and actinomycetes have been shown to possess catabolic P450s with unusual potential to degrade toxic environmental chemicals including persistent organic pollutants (POPs). Wood-rotting basidiomycete fungi in particular have revealed the presence of exceptionally large P450 repertoire (P450ome) in their genomes, majority of which are however orphan (with no known function). Our pre- and post-genomic studies have led to functional characterization of several fungal P450s inducible in response to exposure to several environmental toxicants and demonstration of their potential in bioremediation of these chemicals. This review is an attempt to summarize the postgenomic unveiling of this versatile enzyme superfamily in microbial systems and investigation of their potential to synthesize new drugs and degrade persistent pollutants, among other biotechnological applications.