A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Differential Hsp90-dependent gene expression is strain-specific and common among yeast strains.

Enhanced phenotypic diversity increases a population's likelihood of surviving catastrophic conditions. Hsp90, an essential molecular chaperone and a central network hub in eukaryotes, has been observed to suppress or enhance the effects of genetic variation on phenotypic diversity in response to environmental cues. Because many Hsp90-interacting genes are involved in signaling transduction pathways and transcriptional regulation, we tested how common Hsp90-dependent differential gene expression is in natural populations. Many genes exhibited Hsp90-dependent strain-specific differential expression in five diverse yeast strains. We further identified transcription factors (TFs) potentially contributing to variable expression. We found that on Hsp90 inhibition or environmental stress, activities or abundances of Hsp90-dependent TFs varied among strains, resulting in differential strain-specific expression of their target genes, which consequently led to phenotypic diversity. We provide evidence that individual strains can readily display specific Hsp90-dependent gene expression, suggesting that the evolutionary impacts of Hsp90 are widespread in nature.

TRIPBASE: a database for identifying the human genomic DNA and lncRNA triplexes.

Long-non-coding RNAs (lncRNAs) are defined as RNA sequences which are >200 nt with no coding capacity. These lncRNAs participate in various biological mechanisms, and are widely abundant in a diversity of species. There is well-documented evidence that lncRNAs can interact with genomic DNAs by forming triple helices (triplexes). Previously, several computational methods have been designed based on the Hoogsteen base-pair rule to find theoretical RNA-DNA:DNA triplexes. While powerful, these methods suffer from a high false-positive rate between the predicted triplexes and the biological experiments. To address this issue, we first collected the experimental data of genomic RNA-DNA triplexes from antisense oligonucleotide (ASO)-mediated capture assays and used Triplexator, the most widely used tool for lncRNA-DNA interaction, to reveal the intrinsic information on true triplex binding potential. Based on the analysis, we proposed six computational attributes as filters to improve the in-silico triplex prediction by removing most false positives. Further, we have built a new database, TRIPBASE, as the first comprehensive collection of genome-wide triplex predictions of human lncRNAs. In TRIPBASE, the user interface allows scientists to apply customized filtering criteria to access the potential triplexes of human lncRNAs in the cis-regulatory regions of the human genome. TRIPBASE can be accessed at https://tripbase.iis.sinica.edu.tw/.

Short human eccDNAs are predictable from sequences.

BACKGROUND: Ubiquitous presence of short extrachromosomal circular DNAs (eccDNAs) in eukaryotic cells has perplexed generations of biologists. Their widespread origins in the genome lacking apparent specificity led some studies to conclude their formation as random or near-random. Despite this, the search for specific formation of short eccDNA continues with a recent surge of interest in biomarker development. RESULTS: To shed new light on the conflicting views on short eccDNAs' randomness, here we present DeepCircle, a bioinformatics framework incorporating convolution- and attention-based neural networks to assess their predictability. Short human eccDNAs from different datasets indeed have low similarity in genomic locations, but DeepCircle successfully learned shared DNA sequence features to make accurate cross-datasets predictions (accuracy: convolution-based models: 79.65 ± 4.7%, attention-based models: 83.31 ± 4.18%). CONCLUSIONS: The excellent performance of our models shows that the intrinsic predictability of eccDNAs is encoded in the sequences across tissue origins. Our work demonstrates how the perceived lack of specificity in genomics data can be re-assessed by deep learning models to uncover unexpected similarity.

Genome-wide identification of associations between enhancer and alternative splicing in human and mouse.

BACKGROUND: Alternative splicing (AS) increases the diversity of transcriptome and could fine-tune the function of genes, so that understanding the regulation of AS is vital. AS could be regulated by many different cis-regulatory elements, such as enhancer. Enhancer has been experimentally proved to regulate AS in some genes. However, there is a lack of genome-wide studies on the association between enhancer and AS (enhancer-AS association). To bridge the gap, here we developed an integrative analysis on a genome-wide scale to identify enhancer-AS associations in human and mouse. RESULT: We collected enhancer datasets which include 28 human and 24 mouse tissues and cell lines, and RNA-seq datasets which are paired with the selected tissues. Combining with data integration and statistical analysis, we identified 3,242 human and 7,716 mouse genes which have significant enhancer-AS associations in at least one tissue. On average, for each gene, about 6% of enhancers in human (5% in mouse) are associated to AS change and for each enhancer, approximately one gene is identified to have enhancer-AS association in both human and mouse. We found that 52% of the human significant (34% in mouse) enhancer-AS associations are the co-existence of homologous genes and homologous enhancers. We further constructed a user-friendly platform, named Visualization of Enhancer-associated Alternative Splicing (VEnAS, http://venas.iis.sinica.edu.tw/ ), to provide genomic architecture, intuitive association plot, and contingency table of the significant enhancer-AS associations. CONCLUSION: This study provides the first genome-wide identification of enhancer-AS associations in human and mouse. The results suggest that a notable portion of enhancers are playing roles in AS regulations. The analyzed results and the proposed platform VEnAS would provide a further understanding of enhancers on regulating alternative splicing.

pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Identification and comparative analysis of long non-coding RNAs in the brain of fire ant queens in two different reproductive states.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have been extensively identified in higher eukaryotic species. The function of lncRNAs has been reported to play important roles in diverse biological processes, including developmental regulation and behavioral plasticity. However, there are no reports of systematic characterization of long non-coding RNAs in the fire ant Solenopsis invicta. RESULTS: In this study, we performed a genome-wide analysis of lncRNAs in the brains of S. invicta from RNA-seq. In total, 1,393 novel lncRNA transcripts were identified in the fire ant. In contrast to the annotated lncRNA transcripts having at least two exons, novel lncRNAs are monoexonic transcripts with a shorter length. Besides, the transcriptome from virgin alate and dealate mated queens were analyzed and compared. The results showed 295 differentially expressed mRNA genes (DEGs) and 65 differentially expressed lncRNA genes (DELs) between virgin and mated queens, of which 17 lncRNAs were highly expressed in the virgin alates and 47 lncRNAs were highly expressed in the mated dealates. By identifying the DEL:DEG pairs with a high association in their expression (Spearman's |rho|> 0.8 and p-value < 0.01), many DELs were co-regulated with DEGs after mating. Furthermore, several remarkable lncRNAs (MSTRG.6523, MSTRG.588, and nc909) that were found to associate with particular coding genes may play important roles in the regulation of brain gene expression in reproductive transition in fire ants. CONCLUSION: This study provides the first genome-wide identification of S. invicta lncRNAs in the brains in different reproductive states. It will contribute to a fuller understanding of the transcriptional regulation underpinning reproductive changes.

Transgenic refractory Aedes aegypti lines are resistant to multiple serotypes of dengue virus.

The areas where dengue virus (DENV) is endemic have expanded rapidly, driven in part by the global spread of Aedes species, which act as disease vectors. DENV replicates in the mosquito midgut and is disseminated to the mosquito's salivary glands for amplification. Thus, blocking virus infection or replication in the tissues of the mosquito may be a viable strategy for reducing the incidence of DENV transmission to humans. Here we used the mariner Mos1 transposase to create an Aedes aegypti line that expresses virus-specific miRNA hairpins capable of blocking DENV replication. These microRNA are driven by the blood-meal-inducible carboxypeptidase A promoter or by the polyubiquitin promoter. The transgenic mosquitoes exhibited significantly lower infection rates and viral titers for most DENV serotypes 7 days after receiving an infectious blood meal. The treatment was also effective at day 14 post infection after a second blood meal had been administered. In viral transmission assay, we found there was significantly reduced transmission in these lines. These transgenic mosquitoes were effective in silencing most of the DENV genome; such an approach may be employed to control a dengue fever epidemic.

NK cell receptor and ligand composition influences the clearance of SARS-CoV-2.

To explore how the immune system controls clearance of SARS-CoV-2, we used a single-cell, mass cytometry-based proteomics platform to profile the immune systems of 21 patients who had recovered from SARS-CoV-2 infection without need for admission to an intensive care unit or for mechanical ventilation. We focused on receptors involved in interactions between immune cells and virus-infected cells. We found that the diversity of receptor repertoires on natural killer (NK) cells was negatively correlated with the viral clearance rate. In addition, NK subsets expressing the receptor DNAM1 were increased in patients who more rapidly recovered from infection. Ex vivo functional studies revealed that NK subpopulations with high DNAM1 expression had cytolytic activities in response to target cell stimulation. We also found that SARS-CoV-2 infection induced the expression of CD155 and nectin-4, ligands of DNAM1 and its paired coinhibitory receptor TIGIT, which counterbalanced the cytolytic activities of NK cells. Collectively, our results link the cytolytic immune responses of NK cells to the clearance of SARS-CoV-2 and show that the DNAM1 pathway modulates host-pathogen interactions during SARS-CoV-2 infection.

Connecting MHC-I-binding motifs with HLA alleles via deep learning.

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned.

Targeting KDM4B that coactivates c-Myc-regulated metabolism to suppress tumor growth in castration-resistant prostate cancer.

Rationale: The progression of prostate cancer (PCa) to castration-resistant PCa (CRPC) despite continuous androgen deprivation therapy is a major clinical challenge. Over 90% of patients with CRPC exhibit sustained androgen receptor (AR) signaling. KDM4B that removes the repressive mark H3K9me3/2 is a transcriptional activator of AR and has been implicated in the development of CRPC. However, the mechanisms of KDM4B involvement in CRPC remain largely unknown. Here, we sought to demonstrate the molecular pathway mediated by KDM4B in CRPC and to provide proof-of-concept evidence that KDM4B is a potential CRPC target. Methods: CRPC cells (C4-2B or CWR22Rv1) depleted with KDM4B followed by cell proliferation (in vitro and xenograft), microarray, qRT-PCR, Seahorse Flux, and metabolomic analyses were employed to identify the expression and metabolic profiles mediated by KDM4B. Immunoprecipitation was used to determine the KDM4B-c-Myc interaction region. Reporter activity assay and ChIP analysis were used to characterize the KDM4B-c-Myc complex-mediated mechanistic actions. The clinical relevance between KDM4B and c-Myc was determined using UCSC Xena analysis and immunohistochemistry. Results: We showed that KDM4B knockdown impaired CRPC proliferation, switched Warburg to OXPHOS metabolism, and suppressed gene expressions including those targeted by c-Myc. We further demonstrated that KDM4B physically interacted with c-Myc and they were co-recruited to the c-Myc-binding sequence on the promoters of metabolic genes (LDHA, ENO1, and PFK). Importantly, KDM4B and c-Myc synergistically promoted the transactivation of the LDHA promoter in a demethylase-dependent manner. We also provided evidence that KDM4B and c-Myc are co-expressed in PCa tissue and that high expression of both is associated with worse clinical outcome. Conclusions: KDM4B partners with c-Myc and serves as a coactivator of c-Myc to directly enhance c-Myc-mediated metabolism, hence promoting CRPC progression. Targeting KDM4B is thus an alternative therapeutic strategy for advanced prostate cancers driven by c-Myc and AR.

Termite Pest Identification Method Based on Deep Convolution Neural Networks.

Several species of drywood termites, subterranean termites, and fungus-growing termites cause extensive economic losses annually worldwide. Because no universal method is available for controlling all termites, correct species identification is crucial for termite management. Despite deep neural network technologies' promising performance in pest recognition, a method for automatic termite recognition remains lacking. To develop an automated deep learning classifier for termite image recognition suitable for mobile applications, we used smartphones to acquire 18,000 original images each of four termite pest species: Kalotermitidae: Cryptotermes domesticus (Haviland); Rhinotermitidae: Coptotermes formosanus Shiraki and Reticulitermes flaviceps (Oshima); and Termitidae: Odontotermes formosanus (Shiraki). Each original image included multiple individuals, and we applied five image segmentation techniques for capturing individual termites. We used 24,000 individual-termite images (4 species × 2 castes × 3 groups × 1,000 images) for model development and testing. We implemented a termite classification system by using a deep learning-based model, MobileNetV2. Our models achieved high accuracy scores of 0.947, 0.946, and 0.929 for identifying soldiers, workers, and both castes, respectively, which is not significantly different from human expert performance. We further applied image augmentation techniques, including geometrical transformations and intensity transformations, to individual-termite images. The results revealed that the same classification accuracy can be achieved by using 1,000 augmented images derived from only 200 individual-termite images, thus facilitating further model development on the basis of many fewer original images. Our image-based identification system can enable the selection of termite control tools for pest management professionals or homeowners.

iTARGEX analysis of yeast deletome reveals novel regulators of transcriptional buffering in S phase and protein turnover.

Integrating omics data with quantification of biological traits provides unparalleled opportunities for discovery of genetic regulators by in silico inference. However, current approaches to analyze genetic-perturbation screens are limited by their reliance on annotation libraries for prioritization of hits and subsequent targeted experimentation. Here, we present iTARGEX (identification of Trait-Associated Regulatory Genes via mixture regression using EXpectation maximization), an association framework with no requirement of a priori knowledge of gene function. After creating this tool, we used it to test associations between gene expression profiles and two biological traits in single-gene deletion budding yeast mutants, including transcription homeostasis during S phase and global protein turnover. For each trait, we discovered novel regulators without prior functional annotations. The functional effects of the novel candidates were then validated experimentally, providing solid evidence for their roles in the respective traits. Hence, we conclude that iTARGEX can reliably identify novel factors involved in given biological traits. As such, it is capable of converting genome-wide observations into causal gene function predictions. Further application of iTARGEX in other contexts is expected to facilitate the discovery of new regulators and provide observations for novel mechanistic hypotheses regarding different biological traits and phenotypes.

H3K4 methylation at active genes mitigates transcription-replication conflicts during replication stress.

Transcription-replication conflicts (TRCs) occur when intensive transcriptional activity compromises replication fork stability, potentially leading to gene mutations. Transcription-deposited H3K4 methylation (H3K4me) is associated with regions that are susceptible to TRCs; however, the interplay between H3K4me and TRCs is unknown. Here we show that H3K4me aggravates TRC-induced replication failure in checkpoint-defective cells, and the presence of methylated H3K4 slows down ongoing replication. Both S-phase checkpoint activity and H3K4me are crucial for faithful DNA synthesis under replication stress, especially in highly transcribed regions where the presence of H3K4me is highest and TRCs most often occur. H3K4me mitigates TRCs by decelerating ongoing replication, analogous to how speed bumps slow down cars. These findings establish the concept that H3K4me defines the transcriptional status of a genomic region and defends the genome from TRC-mediated replication stress and instability.

Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach.

Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.

Setting boundaries for genome-wide heterochromatic DNA deletions through flanking inverted repeats in Tetrahymena thermophila.

Eukaryotic cells pack their genomic DNA into euchromatin and heterochromatin. Boundaries between these domains have been shown to be set by boundary elements. In Tetrahymena, heterochromatin domains are targeted for deletion from the somatic nuclei through a sophisticated programmed DNA rearrangement mechanism, resulting in the elimination of 34% of the germline genome in ∼10,000 dispersed segments. Here we showed that most of these deletions occur consistently with very limited variations in their boundaries among inbred lines. We identified several potential flanking regulatory sequences, each associated with a subset of deletions, using a genome-wide motif finding approach. These flanking sequences are inverted repeats with the copies located at nearly identical distances from the opposite ends of the deleted regions, suggesting potential roles in boundary determination. By removing and testing two such inverted repeats in vivo, we found that the ability for boundary maintenance of the associated deletion were lost. Furthermore, we analyzed the deletion boundaries in mutants of a known boundary-determining protein, Lia3p and found that the subset of deletions that are affected by LIA3 knockout contained common features of flanking regulatory sequences. This study suggests a common mechanism for setting deletion boundaries by flanking inverted repeats in Tetrahymena thermophila.

CATANA: a tool for generating comprehensive annotations of alternative transcript events.

SUMMARY: In higher eukaryotes, the generation of transcript isoforms from a single gene through alternative splicing (AS) and alternative transcription (AT) mechanisms increases functional and regulatory diversities. Annotating these alternative transcript events is essential for genomic studies. However, there are no existing tools that generate comprehensive annotations of all these alternative transcript events including both AS and AT events. In the present study, we develop CATANA, with the encoded exon usage patterns based on the flattened gene model, to identify ten types of AS and AT events. We demonstrate the power and versatility of CATANA by showing greater depth of annotations of alternative transcript events according to either genome annotation or RNA-seq data. AVAILABILITY AND IMPLEMENTATION: CATANA is available on https://github.com/shiauck/CATANA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.