Androgen receptor splice variants drive castration-resistant prostate cancer metastasis by activating distinct transcriptional programs.

One critical mechanism through which prostate cancer (PCa) adapts to treatments targeting androgen receptor (AR) signaling is the emergence of ligand-binding domain-truncated and constitutively active AR splice variants, particularly AR-V7. While AR-V7 has been intensively studied, its ability to activate distinct biological functions compared with the full-length AR (AR-FL), and its role in regulating the metastatic progression of castration-resistant PCa (CRPC), remain unclear. Our study found that, under castrated conditions, AR-V7 strongly induced osteoblastic bone lesions, a response not observed with AR-FL overexpression. Through combined ChIP-seq, ATAC-seq, and RNA-seq analyses, we demonstrated that AR-V7 uniquely accesses the androgen-responsive elements in compact chromatin regions, activating a distinct transcription program. This program was highly enriched for genes involved in epithelial-mesenchymal transition and metastasis. Notably, we discovered that SOX9, a critical metastasis driver gene, was a direct target and downstream effector of AR-V7. Its protein expression was dramatically upregulated in AR-V7-induced bone lesions. Moreover, we found that Ser81 phosphorylation enhanced AR-V7's pro-metastasis function by selectively altering its specific transcription program. Blocking this phosphorylation with CDK9 inhibitors impaired the AR-V7-mediated metastasis program. Overall, our study has provided molecular insights into the role of AR splice variants in driving the metastatic progression of CRPC.

Comparative chemical genomics in Babesia species identifies the alkaline phosphatase PhoD as a determinant of antiparasitic resistance.

Babesiosis is an emerging zoonosis and widely distributed veterinary infection caused by 100+ species of Babesia parasites. The diversity of Babesia parasites and the lack of specific drugs necessitate the discovery of broadly effective antibabesials. Here, we describe a comparative chemogenomics (CCG) pipeline for the identification of conserved targets. CCG relies on parallel in vitro evolution of resistance in independent populations of Babesia spp. (B. bovis and B. divergens). We identified a potent antibabesial, MMV019266, from the Malaria Box, and selected for resistance in two species of Babesia. After sequencing of multiple independently derived lines in the two species, we identified mutations in a membrane-bound metallodependent phosphatase (phoD). In both species, the mutations were found in the phoD-like phosphatase domain. Using reverse genetics, we validated that mutations in bdphoD confer resistance to MMV019266 in B. divergens. We have also demonstrated that BdPhoD localizes to the endomembrane system and partially with the apicoplast. Finally, conditional knockdown and constitutive overexpression of BdPhoD alter the sensitivity to MMV019266 in the parasite. Overexpression of BdPhoD results in increased sensitivity to the compound, while knockdown increases resistance, suggesting BdPhoD is a pro-susceptibility factor. Together, we have generated a robust pipeline for identification of resistance loci and identified BdPhoD as a resistance mechanism in Babesia species.

Beta-Sitosterol Alters Collagen Distribution in Prostate Fibroblasts.

Herbal supplements containing several types of plant sterols, vitamins, and minerals, are marketed for prostate health. In the majority of these supplements, the most abundant plant sterol is saw palmetto extract or its' principal component, beta-sitosterol. In terms of prostate health, previous work almost exclusively focused on the effects of beta-sitosterol on prostatic epithelium, with little attention paid to the effects on prostatic stroma. This omission is a concern, as the abnormal accumulation of collagen, or fibrosis, of the prostatic stroma has been identified as a factor contributing to lower urinary tract symptoms and dysfunction in aging men. To address whether beta-sitosterol may be promoting prostatic fibrosis, immortalized and primary prostate stromal fibroblasts were subjected to immunoblotting, immunofluorescence, qRT-PCR, ELISA, and image quantitation and analysis techniques to elucidate the effects of beta-sitosterol on cell viability and collagen expression and cellular localization. The results of these studies show that beta-sitosterol is nontoxic to prostatic fibroblasts and does not stimulate collagen production by these cells. However, beta-sitosterol alters collagen distribution and sequesters collagen within prostatic fibroblasts, likely in an age-dependent manner. This is a significant finding as prostate health supplements are used predominantly by middle aged and older men who may, then, be affected disproportionately by these effects.

A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data.

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as transcription factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Cell cycle-regulated ApiAP2s and parasite development: the Toxoplasma paradigm.

The cell division cycle of T. gondii is driven by cyclically expressed ApiAP2 transcription factors (AP2s) that promote gene sets (regulons) associated with specific biological functions. AP2s drive other AP2s, thereby propelling the progressive gene expression waves defining the lytic cycle. AP2s can act as dimers by themselves, in combination with other AP2s (constitutive or cyclical) or in complexes with epigenetic factors. Exit from the cell cycle into either the extracellular state or differentiation into bradyzoites results in major changes in gene expression. Surprisingly, both transitions lead to expression of a shared set of unique AP2s that suggest a shared stress response that, governed by the specific conditions, leads to different outcomes.

Comparative chemical genomics in Babesia species identifies the alkaline phosphatase phoD as a novel determinant of resistance.

Babesiosis is an emerging zoonosis and widely distributed veterinary infection caused by 100+ species of Babesia parasites. The diversity of Babesia parasites, coupled with the lack of potent inhibitors necessitates the discovery of novel conserved druggable targets for the generation of broadly effective antibabesials. Here, we describe a comparative chemogenomics (CCG) pipeline for the identification of novel and conserved targets. CCG relies on parallel in vitro evolution of resistance in independent populations of evolutionarily-related Babesia spp. ( B. bovis and B. divergens ). We identified a potent antibabesial inhibitor from the Malaria Box, MMV019266. We were able to select for resistance to this compound in two species of Babesia, achieving 10-fold or greater resistance after ten weeks of intermittent selection. After sequencing of multiple independently derived lines in the two species, we identified mutations in a single conserved gene in both species: a membrane-bound metallodependent phosphatase (putatively named PhoD). In both species, the mutations were found in the phoD-like phosphatase domain, proximal to the predicted ligand binding site. Using reverse genetics, we validated that mutations in PhoD confer resistance to MMV019266. We have also demonstrated that PhoD localizes to the endomembrane system and partially with the apicoplast. Finally, conditional knockdown and constitutive overexpression of PhoD alter the sensitivity to MMV019266 in the parasite: overexpression of PhoD results in increased sensitivity to the compound, while knockdown increases resistance, suggesting PhoD is a resistance mechanism. Together, we have generated a robust pipeline for identification of resistance loci, and identified PhoD as a novel determinant of resistance in Babesia species. Highlights: Use of two species for in vitro evolution identifies a high confidence locus associated with resistance Resistance mutation in phoD was validated using reverse genetics in B. divergens Perturbation of phoD using function genetics results in changes in the level of resistance to MMV019266Epitope tagging reveals localization to the ER/apicoplast, a conserved localization with a similar protein in diatoms Together, phoD is a novel resistance determinant in multiple Babesia spp .

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data.

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Babesia divergens egress from host cells is orchestrated by essential and druggable kinases and proteases.

Apicomplexan egress from host cells is fundamental to the spread of infection and is poorly characterized in Babesia spp., parasites of veterinary importance and emerging zoonoses. Through the use of video microscopy, transcriptomics and chemical genetics, we have implicated signaling, proteases and gliding motility as key drivers of egress by Babesia divergens. We developed reverse genetics to perform a knockdown screen of putative mediators of egress, identifying kinases and proteases involved in distinct steps of egress (ASP3, PKG and CDPK4) and invasion (ASP2, ASP3 and PKG). Inhibition of egress leads to continued intracellular replication, indicating exit from the replication cycle is uncoupled from egress. Chemical genetics validated PKG, ASP2 and ASP3 as druggable targets in Babesia spp. All taken together, egress in B. divergens more closely resembles T. gondii than the more evolutionarily-related Plasmodium spp. We have established a molecular framework for biological and translational studies of B. divergens egress.

A rising tide of parasite transcriptomics propels pathogen biology.

Twenty years ago, the first transcriptome of the intraerythrocytic developmental cycle of the malaria parasite Plasmodium falciparum was published in PLOS Biology. Since then, transcriptomics studies have transformed the study of parasite biology.

High-Resolution Small RNAs Landscape Provides Insights into Alkane Adaptation in the Marine Alkane-Degrader Alcanivorax dieselolei B-5

Alkanes are widespread in the ocean, and Alcanivorax is one of the most ubiquitous alkane-degrading bacteria in the marine ecosystem. Small RNAs (sRNAs) are usually at the heart of regulatory pathways, but sRNA-mediated alkane metabolic adaptability still remains largely unknown due to the difficulties of identification. Here, differential RNA sequencing (dRNA-seq) modified with a size selection (~50-nt to 500-nt) strategy was used to generate high-resolution sRNAs profiling in the model species Alcanivorax dieselolei B-5 under alkane (n-hexadecane) and non-alkane (acetate) conditions. As a result, we identified 549 sRNA candidates at single-nucleotide resolution of 5'-ends, 63.4% of which are with transcription start sites (TSSs), and 36.6% of which are with processing sites (PSSs) at the 5'-ends. These sRNAs originate from almost any location in the genome, regardless of intragenic (65.8%), antisense (20.6%) and intergenic (6.2%) regions, and RNase E may function in the maturation of sRNAs. Most sRNAs locally distribute across the 15 reference genomes of Alcanivorax, and only 7.5% of sRNAs are broadly conserved in this genus. Expression responses to the alkane of several core conserved sRNAs, including 6S RNA, M1 RNA and tmRNA, indicate that they may participate in alkane metabolisms and result in more actively global transcription, RNA processing and stresses mitigation. Two novel CsrA-related sRNAs are identified, which may be involved in the translational activation of alkane metabolism-related genes by sequestering the global repressor CsrA. The relationships of sRNAs with the characterized genes of alkane sensing (ompS), chemotaxis (mcp, cheR, cheW2), transporting (ompT1, ompT2, ompT3) and hydroxylation (alkB1, alkB2, almA) were created based on the genome-wide predicted sRNA-mRNA interactions. Overall, the sRNA landscape lays the ground for uncovering cryptic regulations in critical marine bacterium, among which both the core and species-specific sRNAs are implicated in the alkane adaptive metabolisms.

Increased AR expression in castration-resistant prostate cancer rapidly induces AR signaling reprogramming with the collaboration of EZH2.

Elevated androgen receptor (AR) expression is a hallmark of castration-resistant prostate cancer (CRPC) and contributes to the restoration of AR signaling under the conditions of androgen deprivation. However, whether overexpressed AR alone with the stimulation of castrate levels of androgens can be sufficient to induce the reprogramming of AR signaling for the adaptation of prostate cancer (PCa) cells remains unclear. In this study, we used a PCa model with inducible overexpression of AR to examine the acute effects of AR overexpression on its cistrome and transcriptome. Our results show that overexpression of AR alone in conjunction with lower androgen levels can rapidly redistribute AR chromatin binding and activates a distinct transcription program that is enriched for DNA damage repair pathways. Moreover, using a recently developed bioinformatic tool, we predicted the involvement of EZH2 in this AR reprogramming and subsequently identified a subset of AR/EZH2 co-targeting genes, which are overexpressed in CRPC and associated with worse patient outcomes. Mechanistically, we found that AR-EZH2 interaction is impaired by the pre-castration level of androgens but can be recovered by the post-castration level of androgens. Overall, our study provides new molecular insights into AR signaling reprogramming with the engagement of specific epigenetic factors.

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles.

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing "just-in-time" expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer.

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.

Vitamin A deficiency affects gene expression in the Drosophila melanogaster head.

Insufficient dietary intake of vitamin A causes various human diseases. For instance, chronic vitamin A deprivation causes blindness, slow growth, impaired immunity, and an increased risk of mortality in children. In contrast to these diverse effects of vitamin A deficiency (VAD) in mammals, chronic VAD in flies neither causes obvious developmental defects nor lethality. As in mammals, VAD in flies severely affects the visual system: it impairs the synthesis of the retinal chromophore, disrupts the formation of the visual pigments (Rhodopsins), and damages the photoreceptors. However, the molecular mechanisms that respond to VAD remain poorly understood. To identify genes and signaling pathways that are affected by VAD, we performed RNA-sequencing and differential gene expression analysis in Drosophila melanogaster. We found an upregulation of genes that are essential for the synthesis of the retinal chromophore, specific aminoacyl-tRNA synthetases, and major nutrient reservoir proteins. We also discovered that VAD affects several genes that are required for the termination of the light response: for instance, we found a downregulation of both arrestin genes that are essential for the inactivation of Rhodopsin. A comparison of the VAD-responsive genes with previously identified blue light stress-responsive genes revealed that the two types of environmental stress trigger largely nonoverlapping transcriptome responses. Yet, both stresses increase the expression of seven genes with poorly understood functions. Taken together, our transcriptome analysis offers insights into the molecular mechanisms that respond to environmental stresses.

The Extracellular Milieu of Toxoplasma's Lytic Cycle Drives Lab Adaptation, Primarily by Transcriptional Reprogramming.

Evolve and resequencing (E&R) was applied to lab adaptation of Toxoplasma gondii for over 1,500 generations with the goal of mapping host-independent in vitro virulence traits. Phenotypic assessments of steps across the lytic cycle revealed that only traits needed in the extracellular milieu evolved. Nonsynonymous single-nucleotide polymorphisms (SNPs) in only one gene, a P4 flippase, fixated across two different evolving populations, whereas dramatic changes in the transcriptional signature of extracellular parasites were identified. Newly developed computational tools correlated phenotypes evolving at different rates with specific transcriptomic changes. A set of 300 phenotype-associated genes was mapped, of which nearly 50% is annotated as hypothetical. Validation of a select number of genes by knockouts confirmed their role in lab adaptation and highlights novel mechanisms underlying in vitro virulence traits. Further analyses of differentially expressed genes revealed the development of a "pro-tachyzoite" profile as well as the upregulation of the fatty acid biosynthesis (FASII) pathway. The latter aligned with the P4 flippase SNP and aligned with a low abundance of medium-chain fatty acids at low passage, indicating this is a limiting factor in extracellular parasites. In addition, partial overlap with the bradyzoite differentiation transcriptome in extracellular parasites indicated that stress pathways are involved in both situations. This was reflected in the partial overlap between the assembled ApiAP2 and Myb transcription factor network underlying the adapting extracellular state with the bradyzoite differentiation program. Overall, E&R is a new genomic tool successfully applied to map the development of polygenic traits underlying in vitro virulence of T. gondii. IMPORTANCE It has been well established that prolonged in vitro cultivation of Toxoplasma gondii augments progression of the lytic cycle. This lab adaptation results in increased capacities to divide, migrate, and survive outside a host cell, all of which are considered host-independent virulence factors. However, the mechanistic basis underlying these enhanced virulence features is unknown. Here, E&R was utilized to empirically characterize the phenotypic, genomic, and transcriptomic changes in the non-lab-adapted strain, GT1, during 2.5 years of lab adaptation. This identified the shutdown of stage differentiation and upregulation of lipid biosynthetic pathways as the key processes being modulated. Furthermore, lab adaptation was primarily driven by transcriptional reprogramming, which rejected the starting hypothesis that genetic mutations would drive lab adaptation. Overall, the work empirically shows that lab adaptation augments T. gondii's in vitro virulence by transcriptional reprogramming and that E&R is a powerful new tool to map multigenic traits.

The Modular Circuitry of Apicomplexan Cell Division Plasticity.

The close-knit group of apicomplexan parasites displays a wide variety of cell division modes, which differ between parasites as well as between different life stages within a single parasite species. The beginning and endpoint of the asexual replication cycles is a 'zoite' harboring the defining apical organelles required for host cell invasion. However, the number of zoites produced per division round varies dramatically and can unfold in several different ways. This plasticity of the cell division cycle originates from a combination of hard-wired developmental programs modulated by environmental triggers. Although the environmental triggers and sensors differ between species and developmental stages, widely conserved secondary messengers mediate the signal transduction pathways. These environmental and genetic input integrate in division-mode specific chromosome organization and chromatin modifications that set the stage for each division mode. Cell cycle progression is conveyed by a smorgasbord of positively and negatively acting transcription factors, often acting in concert with epigenetic reader complexes, that can vary dramatically between species as well as division modes. A unique set of cell cycle regulators with spatially distinct localization patterns insert discrete check points which permit individual control and can uncouple general cell cycle progression from nuclear amplification. Clusters of expressed genes are grouped into four functional modules seen in all division modes: 1. mother cytoskeleton disassembly; 2. DNA replication and segregation (D&S); 3. karyokinesis; 4. zoite assembly. A plug-and-play strategy results in the variety of extant division modes. The timing of mother cytoskeleton disassembly is hard-wired at the species level for asexual division modes: it is either the first step, or it is the last step. In the former scenario zoite assembly occurs at the plasma membrane (external budding), and in the latter scenario zoites are assembled in the cytoplasm (internal budding). The number of times each other module is repeated can vary regardless of this first decision, and defines the modes of cell division: schizogony, binary fission, endodyogeny, endopolygeny.

Susceptibility-Associated Genetic Variation in NEDD9 Contributes to Prostate Cancer Initiation and Progression.

Although American men of European ancestry represent the largest population of patients with prostate cancer, men of African ancestry are disproportionately affected by prostate cancer, with higher prevalence and worse outcomes. These racial disparities in prostate cancer are due to multiple factors, but variations in genomic susceptibility such as SNP may play an important role in determining cancer aggressiveness and treatment outcome. Using public databases, we have identified a prostate cancer susceptibility SNP at an intronic enhancer of the neural precursor expressed, developmentally downregulated 9 (NEDD9) gene, which is strongly associated with increased risk of patients with African ancestry. This genetic variation increased expression of NEDD9 by modulating the chromatin binding of certain transcription factors, including ERG and NANOG. Moreover, NEDD9 displayed oncogenic activity in prostate cancer cells, promoting prostate cancer tumor growth and metastasis in vitro and in vivo. Together, our study provides novel insights into the genetic mechanisms driving prostate cancer racial disparities. SIGNIFICANCE: A prostate cancer susceptibility genetic variation in NEDD9, which is strongly associated with the increased risk of patients with African ancestry, increases NEDD9 expression and promotes initiation and progression of prostate cancer.See related commentary by Mavura and Huang, p. 3764.

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.

Histopathological images are a rich but incompletely explored data type for studying cancer. Manual inspection is time consuming, making it challenging to use for image data mining. Here we show that convolutional neural networks (CNNs) can be systematically applied across cancer types, enabling comparisons to reveal shared spatial behaviors. We develop CNN architectures to analyze 27,815 hematoxylin and eosin scanned images from The Cancer Genome Atlas for tumor/normal, cancer subtype, and mutation classification. Our CNNs are able to classify TCGA pathologist-annotated tumor/normal status of whole slide images (WSIs) in 19 cancer types with consistently high AUCs (0.995 ± 0.008), as well as subtypes with lower but significant accuracy (AUC 0.87 ± 0.1). Remarkably, tumor/normal CNNs trained on one tissue are effective in others (AUC 0.88 ± 0.11), with classifier relationships also recapitulating known adenocarcinoma, carcinoma, and developmental biology. Moreover, classifier comparisons reveal intra-slide spatial similarities, with an average tile-level correlation of 0.45 ± 0.16 between classifier pairs. Breast cancers, bladder cancers, and uterine cancers have spatial patterns that are particularly easy to detect, suggesting these cancers can be canonical types for image analysis. Patterns for TP53 mutations can also be detected, with WSI self- and cross-tissue AUCs ranging from 0.65-0.80. Finally, we comparatively evaluate CNNs on 170 breast and colon cancer images with pathologist-annotated nuclei, finding that both cellular and intercellular regions contribute to CNN accuracy. These results demonstrate the power of CNNs not only for histopathological classification, but also for cross-comparisons to reveal conserved spatial behaviors across tumors.

Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores.

MOTIVATION: Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited. RESULTS: In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN. AVAILABILITY AND IMPLEMENTATION: https://github.com/BioMLBoston/PatchDCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction.

BACKGROUND: Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. METHODS: In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. RESULTS: Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.