Entrez Gene: gene-centered information at NCBI

PhageScanner: a reconfigurable machine learning framework for bacteriophage genomic and metagenomic feature annotation

Bacteriophages are the most prolific organisms on Earth, yet many of their genomes and assemblies from metagenomic sources lack protein sequences with identified functions. While most bacteriophage proteins are structural proteins, categorized as Phage Virion Proteins (PVPs), a considerable number remain unclassified. Complicating matters further, traditional lab-based methods for PVP identification can be tedious. To expedite the process of identifying PVPs, machine-learning models are increasingly being employed. Existing tools have developed models for predicting PVPs from protein sequences as input. However, none of these efforts have built software allowing for both genomic and metagenomic data as input. In addition, there is currently no framework available for easily curating data and creating new types of machine learning models. In response, we introduce PhageScanner, an open-source platform that streamlines data collection for genomic and metagenomic datasets, model training and testing, and includes a prediction pipeline for annotating genomic and metagenomic data. PhageScanner also features a graphical user interface (GUI) for visualizing annotations on genomic and metagenomic data. We further introduce a BLAST-based classifier that outperforms ML-based models and an efficient Long Short-Term Memory (LSTM) classifier. We then showcase the capabilities of PhageScanner by predicting PVPs in six previously uncharacterized bacteriophage genomes. In addition, we create a new model that predicts phage-encoded toxins within bacteriophage genomes, thus displaying the utility of the framework.

Gene expression profiling and the isocitrate dehydrogenase mutational landscape of temozolomide‑resistant glioblastoma

Glioblastoma multiforme (GBM) is an aggressive brain cancer that occurs more frequently than other brain tumors. The present study aimed to reveal a novel mechanism of temozolomide resistance in GBM using bioinformatics and wet lab analyses, including meta-Z analysis, Kaplan-Meier survival analysis, protein-protein interaction (PPI) network establishment, cluster analysis of co-expressed gene networks, and hierarchical clustering of upregulated and downregulated genes. Next-generation sequencing and quantitative PCR analyses revealed downregulated [tyrosine kinase with immunoglobulin and epidermal growth factor homology domains 1 (TIE1), calcium voltage-gated channel auxiliary subunit α2Δ1 (CACNA2D1), calpain 6 (CAPN6) and a disintegrin and metalloproteinase with thrombospondin motifs 6 (ADAMTS6)] and upregulated [serum amyloid (SA)A1, SAA2, growth differentiation factor 15 (GDF15) and ubiquitin specific peptidase 26 (USP26)] genes. Different statistical models were developed for these genes using the Z-score for P-value conversion, and Kaplan-Meier plots were constructed using several patient cohorts with brain tumors. The highest number of nodes was observed in the PPI network was for ADAMTS6 and TIE1. The PPI network model for all genes contained 35 nodes and 241 edges. Immunohistochemical staining was performed using isocitrate dehydrogenase (IDH)-wild-type or IDH-mutant GBM samples from patients and a significant upregulation of TIE1 (P<0.001) and CAPN6 (P<0.05) protein expression was demonstrated in IDH-mutant GBM in comparison with IDH-wild-type GBM. Structural analysis revealed an IDH-mutant model demonstrating the mutant residues (R132, R140 and R172). The findings of the present study will help the future development of novel biomarkers and therapeutics for brain tumors.

Keratinocytes drive the epithelial hyperplasia key to sea lice resistance in coho salmon

BACKGROUND

Salmonid species have followed markedly divergent evolutionary trajectories in their interactions with sea lice. While sea lice parasitism poses significant economic, environmental, and animal welfare challenges for Atlantic salmon (Salmo salar) aquaculture, coho salmon (Oncorhynchus kisutch) exhibit near-complete resistance to sea lice, achieved through a potent epithelial hyperplasia response leading to rapid louse detachment. The molecular mechanisms underlying these divergent responses to sea lice are unknown.

RESULTS

We characterized the cellular and molecular responses of Atlantic salmon and coho salmon to sea lice using single-nuclei RNA sequencing. Juvenile fish were exposed to copepodid sea lice (Lepeophtheirus salmonis), and lice-attached pelvic fin and skin samples were collected 12 h, 24 h, 36 h, 48 h, and 60 h after exposure, along with control samples. Comparative analysis of control and treatment samples revealed an immune and wound-healing response that was common to both species, but attenuated in Atlantic salmon, potentially reflecting greater sea louse immunomodulation. Our results revealed unique but complementary roles of three layers of keratinocytes in the epithelial hyperplasia response leading to rapid sea lice rejection in coho salmon. Our results suggest that basal keratinocytes direct the expansion and mobility of intermediate and, especially, superficial keratinocytes, which eventually encapsulate the parasite.

CONCLUSIONS

Our results highlight the key role of keratinocytes in coho salmon's sea lice resistance and the diverged biological response of the two salmonid host species when interacting with this parasite. This study has identified key pathways and candidate genes that could be manipulated using various biotechnological solutions to improve Atlantic salmon sea lice resistance.

A Study of Biomedical Relation Extraction Using GPT Models

Relation Extraction (RE) is a natural language processing (NLP) task for extracting semantic relations between biomedical entities. Recent developments in pre-trained large language models (LLM) motivated NLP researchers to use them for various NLP tasks. We investigated GPT-3.5-turbo and GPT-4 on extracting the relations from three standard datasets, EU-ADR, Gene Associations Database (GAD), and ChemProt. Unlike the existing approaches using datasets with masked entities, we used three versions for each dataset for our experiment: a version with masked entities, a second version with the original entities (unmasked), and a third version with abbreviations replaced with the original terms. We developed the prompts for various versions and used the chat completion model from GPT API. Our approach achieved a F1-score of 0.498 to 0.809 for GPT-3.5-turbo, and a highest F1-score of 0.84 for GPT-4. For certain experiments, the performance of GPT, BioBERT, and PubMedBERT are almost the same.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Characterization of a putative orexin receptor in Ciona intestinalis sheds light on the evolution of the orexin/hypocretin system in chordates

Tunicates are evolutionary model organisms bridging the gap between vertebrates and invertebrates. A genomic sequence in Ciona intestinalis (CiOX) shows high similarity to vertebrate orexin receptors and protostome allatotropin receptors (ATR). Here, molecular phylogeny suggested that CiOX is divergent from ATRs and human orexin receptors (hOX1/2). However, CiOX appears closer to hOX1/2 than to ATR both in terms of sequence percent identity and in its modelled binding cavity, as suggested by molecular modelling. CiOX was heterologously expressed in a recombinant HEK293 cell system. Human orexins weakly but concentration-dependently activated its Gq signalling (Ca2+ elevation), and the responses were inhibited by the non-selective orexin receptor antagonists TCS 1102 and almorexant, but only weakly by the OX1-selective antagonist SB-334867. Furthermore, the 5-/6-carboxytetramethylrhodamine (TAMRA)-labelled human orexin-A was able to bind to CiOX. Database mining was used to predict a potential endogenous C. intestinalis orexin peptide (Ci-orexin-A). Ci-orexin-A was able to displace TAMRA-orexin-A, but not to induce any calcium response at the CiOX. Consequently, we suggested that the orexin signalling system is conserved in Ciona intestinalis, although the relevant peptide-receptor interaction was not fully elucidated.

Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource

Natural language processing (NLP) has become an essential technique in various fields, offering a wide range of possibilities for analyzing data and developing diverse NLP tasks. In the biomedical domain, understanding the complex relationships between compounds and proteins is critical, especially in the context of signal transduction and biochemical pathways. Among these relationships, protein-protein interactions (PPIs) are of particular interest, given their potential to trigger a variety of biological reactions. To improve the ability to predict PPI events, we propose the protein event detection dataset (PEDD), which comprises 6823 abstracts, 39 488 sentences and 182 937 gene pairs. Our PEDD dataset has been utilized in the AI CUP Biomedical Paper Analysis competition, where systems are challenged to predict 12 different relation types. In this paper, we review the state-of-the-art relation extraction research and provide an overview of the PEDD's compilation process. Furthermore, we present the results of the PPI extraction competition and evaluate several language models' performances on the PEDD. This paper's outcomes will provide a valuable roadmap for future studies on protein event detection in NLP. By addressing this critical challenge, we hope to enable breakthroughs in drug discovery and enhance our understanding of the molecular mechanisms underlying various diseases.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Toward Unified AI Drug Discovery with Multimodal Knowledge

Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.

CrustyBase v.2.0: new features and enhanced utilities to support open science

BACKGROUND

Transcriptomes present a rich, multi-dimensional subset of genomics data. They provide broad insights into genetic sequence, and more significantly gene expression, across biological samples. This technology is frequently employed for describing the genetic response to experimental conditions and has created vast libraries of datasets which shed light on gene function across different tissues, diseases, diets and developmental stages in many species. However, public accessibility of these data is impeded by a lack of suitable software interfaces and databases with which to locate and analyse them. BODY: Here we present an update on the status of CrustyBase.org, an online resource for analysing and sharing crustacean transcriptome datasets. Since its release in October 2020, the resource has provided many thousands of transcriptome sequences and expression profiles to its users and received 19 new dataset imports from researchers across the globe. In this article we discuss user analytics which point towards the utilization of this resource. The architecture of the application has proven robust with over 99.5% uptime and effective reporting of bugs through both user engagement and the error logging mechanism. We also introduce several new features that have been developed as part of a new release of CrustyBase.org. Two significant features are described in detail, which allow users to navigate through transcripts directly by submission of transcript identifiers, and then more broadly by searching for encoded protein domains by keyword. The latter is a novel and experimental feature, and grants users the ability to curate gene families from any dataset hosted on CrustyBase in a matter of minutes. We present case studies to demonstrate the utility of these features.

CONCLUSION

Community engagement with this resource has been very positive, and we hope that improvements to the service will further enable the research of users of the platform. Web-based platforms such as CrustyBase have many potential applications across life science domains, including the health sector, which are yet to be realised. This leads to a wider discussion around the role of web-based resources in facilitating an open and collaborative research community.

Genetic Association and Differential RNA Expression of Histone (De)Acetylation-Related Genes in Pemphigus Foliaceus-A Possible Epigenetic Effect in the Autoimmune Response

Pemphigus foliaceus (PF) is an autoimmune skin blistering disease characterized by antidesmoglein-1 IgG production, with an endemic form (EPF) in Brazil. Genetic and epigenetic factors have been associated with EPF, but its etiology is still not fully understood. To evaluate the genetic association of histone (de)acetylation-related genes with EPF susceptibility, we evaluated 785 polymorphisms from 144 genes, for 227 EPF patients and 194 controls. Carriers of HDAC4_rs4852054*A were more susceptible (OR = 1.79, p = 0.0038), whereas those with GSE1_rs13339618*A (OR = 0.57, p = 0.0011) and homozygotes for PHF21A_rs4756055*A (OR = 0.39, p = 0.0006) were less susceptible to EPF. These variants were not associated with sporadic PF (SPF) in German samples of 75 SPF patients and 150 controls, possibly reflecting differences in SPF and EPF pathophysiology. We further evaluated the expression of histone (de)acetylation-related genes in CD4+ T lymphocytes, using RNAseq. In these cells, we found a higher expression of KAT2B, PHF20, and ZEB2 and lower expression of KAT14 and JAD1 in patients with active EPF without treatment compared to controls from endemic regions. The encoded proteins cause epigenetic modifications related to immune cell differentiation and cell death, possibly affecting the immune response in patients with PF.

A Comprehensive Evaluation of Biomedical Entity Linking Models

Biomedical entity linking (BioEL) is the process of connecting entities referenced in documents to entries in biomedical databases such as the Unified Medical Language System (UMLS) or Medical Subject Headings (MeSH). The study objective was to comprehensively evaluate nine recent state-of-the-art biomedical entity linking models under a unified framework. We compare these models along axes of (1) accuracy, (2) speed, (3) ease of use, (4) generalization, and (5) adaptability to new ontologies and datasets. We additionally quantify the impact of various preprocessing choices such as abbreviation detection. Systematic evaluation reveals several notable gaps in current methods. In particular, current methods struggle to correctly link genes and proteins and often have difficulty effectively incorporating context into linking decisions. To expedite future development and baseline testing, we release our unified evaluation framework and all included models on GitHub at https://github.com/davidkartchner/biomedical-entity-linking.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

Evaluation of the Genetic Background of Patients with Niemann-Pick Disease

Background

Congenital liver disease refers to a group of heterogeneous diseases from a clinical genetic point of view. The most crucial features are hepatosplenomegaly and elevated liver enzymes. This study aims to identify genetic variants causing the disease in three Iranian families with congenital liver disease using molecular techniques.

Methods

Patients were referred to Next Generation Genetic Polyclinic (NGGC) in Mashhad after confirmed congenital liver disease diagnosis by gastroenterologists. Following informed consent signed by participants, DNA was extracted from blood samples. Whole exome sequencing (WES) was performed for three probands. After the analysis of raw data, candidate variants were confirmed in the patients and their parents.

Results

We have found the possible disease-causing variant as the c.1718G>C variant (p. Trp573Ser) in the SMPD1 gene in the F-1 patient and c.1718G>C (p. Trp573Ser) in the SMPD1 gene in the F-3 patient. Moreover, we have found the c.3175C>T variant (p. Arg1059Ter) in the NPC1 gene in the F-2 patient.

Conclusions

In this study, disease-causing variants were identified in three probands suspected of Niemann-Pick disease. Such results show the relatively high power of molecular techniques to assist clinicians with disease management, therapeutic strategies, and preventive options such as preimplantation genetic diagnosis and prenatal diagnosis.

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery

Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

A systems biology approach for discovering the cellular and molecular aspects of psychogenic non-epileptic seizure

Objectives

Psychogenic non-epileptic seizure (PNES) is the most common non-epileptic disorder in patients referring to epilepsy centers. Contrary to common beliefs about the disease's harmlessness, the death rate of PNES patients is similar to drug-resistant epilepsy. Meanwhile, the molecular pathomechanism of PNES is unknown with very limited related research. Thus, the aim of this in silico study was to find different proteins and hormones associated with PNES via a systems biology approach.

Methods

Different bioinformatics databases and literature review were used to find proteins associated with PNES. The protein-hormone interaction network of PNES was constructed to discover its most influential compartments. The pathways associated with PNES pathomechanism were found by enrichment analysis of the identified proteins. Besides, the relationship between PNES-related molecules and psychiatric diseases was discovered, and the brain regions that could express altered levels of blood proteins were discovered.

Results

Eight genes and three hormones were found associated with PNES through the review process. Proopiomelanocortin (POMC), neuropeptide Y (NPY), cortisol, norepinephrine, and brain-derived neurotrophic factor (BDNF) were identified to have a high impact on the disease pathogenesis network. Moreover, activation of Janus kinase-signaling transducer and activator of transcription (JAK-STAT) and JAK, as well as signaling of growth hormone receptor, phosphatidylinositol 3-kinase /protein kinase B (PI3K/AKT), and neurotrophin were found associated with PNES molecular mechanism. Several psychiatric diseases such as depression, schizophrenia, and alcohol-related disorders were shown to be associated with PNES predominantly through signaling molecules.

Significance

This study was the first to gather the biochemicals associated with PNES. Multiple components and pathways and several psychiatric diseases associated with PNES, and some brain regions that could be altered during PNES were suggested, which should be confirmed in further studies. Altogether, these findings could be used in future molecular research on PNES patients.

A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks

Identifying disease-gene associations is important for understanding molecule mechanisms of diseases, finding diagnostic markers and therapeutic targets. Many computational methods have been proposed to predict disease related genes by integrating different biological databases into heterogeneous networks. However, it remains a challenging task to leverage heterogeneous topological and semantic information from multi-source biological data to enhance disease-gene prediction. In this study, we propose a knowledge graph-based disease-gene prediction system (GenePredict-KG) by modeling semantic relations extracted from various genotypic and phenotypic databases. We first constructed a knowledge graph that comprised 2,292,609 associations between 73,358 entities for 14 types of phenotypic and genotypic relations and 7 entity types. We developed a knowledge graph embedding model to learn low-dimensional representations of entities and relations, and utilized these embeddings to infer new disease-gene interactions. We compared GenePredict-KG with several state-of-the-art models using multiple evaluation metrics. GenePredict-KG achieved high performances [AUROC (the area under receiver operating characteristic) = 0.978, AUPR (the area under precision-recall) = 0.343 and MRR (the mean reciprocal rank) = 0.244], outperforming other state-of-art methods.

Structure-Function of the Human WAC Protein in GABAergic Neurons: Towards an Understanding of Autosomal Dominant DeSanto-Shinawi Syndrome

Dysfunction of the WW domain-containing adaptor with coiled-coil, WAC, gene underlies a rare autosomal dominant disorder, DeSanto-Shinawi syndrome (DESSH). DESSH is associated with facial dysmorphia, hypotonia, and cognitive alterations, including attention deficit hyperactivity disorder and autism. How the WAC protein localizes and functions in neural cells is critical to understanding its role during development. To understand the genotype-phenotype role of WAC, we developed a knowledgebase of WAC expression, evolution, human genomics, and structural/motif analysis combined with human protein domain deletions to assess how conserved domains guide cellular distribution. Then, we assessed localization in a cell type implicated in DESSH, cortical GABAergic neurons. WAC contains conserved charged amino acids, phosphorylation signals, and enriched nuclear motifs, suggesting a role in cellular signaling and gene transcription. Human DESSH variants are found within these regions. We also discovered and tested a nuclear localization domain that impacts the cellular distribution of the protein. These data provide new insights into the potential roles of this critical developmental gene, establishing a platform to assess further translational studies, including the screening of missense genetic variants in WAC. Moreover, these studies are essential for understanding the role of human WAC variants in more diverse neurological phenotypes, including autism spectrum disorder.

GTPase splice variants RAC1 and RAC1B display isoform-specific differences in localization, prenylation, and interaction with the chaperone protein SmgGDS

Identifying events that regulate the prenylation and localization of small GTPases will help define new strategies for therapeutic targeting of these proteins in disorders such as cancer, cardiovascular disease, and neurological deficits. Splice variants of the chaperone protein SmgGDS (encoded by RAP1GDS1) are known to regulate prenylation and trafficking of small GTPases. The SmgGDS-607 splice variant regulates prenylation by binding pre-prenylated small GTPases, but the effects of SmgGDS binding to the small GTPase RAC1 versus the splice variant RAC1B are not well defined. Here we report unexpected differences in the prenylation and localization of RAC1 and RAC1B, and their binding to SmgGDS. Compared to RAC1, RAC1B more stably associates with SmgGDS-607, is less prenylated, and accumulates more in the nucleus. We show that the small GTPase DIRAS1 inhibits binding of RAC1 and RAC1B to SmgGDS and reduces their prenylation. These results suggest that prenylation of RAC1 and RAC1B is facilitated by binding to SmgGDS-607, but the greater retention of RAC1B by SmgGDS-607 slows RAC1B prenylation. We show that inhibiting RAC1 prenylation by mutating the CAAX motif promotes RAC1 nuclear accumulation, suggesting that differences in prenylation contribute to the different nuclear localization of RAC1 versus RAC1B. Finally, we demonstrate RAC1 and RAC1B that cannot be prenylated bind GTP in cells, indicating that prenylation is not a prerequisite for activation. We report differential expression of RAC1 and RAC1B transcripts in tissues, consistent with these two splice variants having unique functions that might arise in part from their differences in prenylation and localization.

Pregnancy-specific responses to COVID-19 revealed by high-throughput proteomics of human plasma

BACKGROUND

Pregnant women are at greater risk of adverse outcomes, including mortality, as well as obstetrical complications resulting from COVID-19. However, pregnancy-specific changes that underlie such worsened outcomes remain unclear.

METHODS

Plasma samples were collected from pregnant women and non-pregnant individuals (male and female) with (n = 72 pregnant, 52 non-pregnant) and without (n = 29 pregnant, 41 non-pregnant) COVID-19. COVID-19 patients were grouped as asymptomatic, mild, moderate, severe, or critically ill according to NIH classifications. Proteomic profiling of 7,288 analytes corresponding to 6,596 unique protein targets was performed using the SOMAmer platform.

RESULTS

Herein, we profile the plasma proteome of pregnant and non-pregnant COVID-19 patients and controls and show alterations that display a dose-response relationship with disease severity; yet, such proteomic perturbations are dampened during pregnancy. In both pregnant and non-pregnant state, the proteome response induced by COVID-19 shows enrichment of mediators implicated in cytokine storm, endothelial dysfunction, and angiogenesis. Shared and pregnancy-specific proteomic changes are identified: pregnant women display a tailored response that may protect the conceptus from heightened inflammation, while non-pregnant individuals display a stronger response to repel infection. Furthermore, the plasma proteome can accurately identify COVID-19 patients, even when asymptomatic or with mild symptoms.

CONCLUSION

This study represents the most comprehensive characterization of the plasma proteome of pregnant and non-pregnant COVID-19 patients. Our findings emphasize the distinct immune modulation between the non-pregnant and pregnant states, providing insight into the pathogenesis of COVID-19 as well as a potential explanation for the more severe outcomes observed in pregnant women.

Molecular Genetics of Abnormal Redox Homeostasis in Type 2 Diabetes Mellitus

Numerous studies have shown that oxidative stress resulting from an imbalance between the production of free radicals and their neutralization by antioxidant enzymes is one of the major pathological disorders underlying the development and progression of type 2 diabetes (T2D). The present review summarizes the current state of the art advances in understanding the role of abnormal redox homeostasis in the molecular mechanisms of T2D and provides comprehensive information on the characteristics and biological functions of antioxidant and oxidative enzymes, as well as discusses genetic studies conducted so far in order to investigate the contribution of polymorphisms in genes encoding redox state-regulating enzymes to the disease pathogenesis.

Intestinal Epithelial Cells Adapt to Chronic Inflammation through Partial Genetic Reprogramming

Reactive oxygen species (ROS) are considered to be the main drivers of inflammatory bowel disease. We investigated whether this permanent insult compels intestinal stem cells to develop strategies to dampen the deleterious effects of ROS. As an adverse effect, this adaptation process may increase their tolerance to oncogenic insults and facilitate their neoplastic transformation. We submitted immortalized human colonic epithelial cells to either a mimic of chronic inflammation or to a chemical peroxide, analyzed how they adapted to stress, and addressed the biological relevance of these observations in databases. We demonstrated that cells adapt to chronic-inflammation-associated oxidative stress in vitro through a partial genetic reprogramming. Through a gene set enrichment analysis, we showed that this program is recurrently active in the intestinal mucosae of Crohn's and ulcerative colitis disease patients and evolves alongside disease progression. Based on a previously reported characterization of intestinal stem and precursor cells using tracing experiments, we lastly confirmed the activation of the program in intestinal precursor cells during murine colorectal cancer development. This adaptive process is thus likely to play a role in the progression of Crohn's and ulcerative disease, and potentially in the initiation of colorectal cancer.

Genome-Scale Modeling and Systems Metabolic Engineering of Vibrio natriegens for the Production of 1,3-Propanediol

The fastest-growing bacterium Vibrio natriegens is a highly promising next-generation workhorse for molecular biology and industrial biotechnology. In this work, we described the workflows for developing genome-scale metabolic models and genome-editing protocols for engineering Vibrio natriegens. A case study for metabolic engineering of Vibrio natriegens for the production of 1,3-propanediol was also presented.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome

H37Rv is the most widely used Mycobacterium tuberculosis strain, and its genome is globally used as the M. tuberculosis reference sequence. Here, we present Bact-Builder, a pipeline that uses consensus building to generate complete and accurate bacterial genome sequences and apply it to three independently cultured and sequenced H37Rv aliquots of a single laboratory stock. Two of the 4,417,942 base-pair long H37Rv assemblies are 100% identical, with the third differing by a single nucleotide. Compared to the existing H37Rv reference, the new sequence contains ~6.4 kb additional base pairs, encoding ten new regions that include insertions in PE/PPE genes and new paralogs of esxN and esxJ, which are differentially expressed compared to the reference genes. New sequencing and de novo assemblies with Bact-Builder confirm that all 10 regions, plus small additional polymorphisms, are also present in the commonly used H37Rv strains NR123, TMC102, and H37Rv1998. Thus, Bact-Builder shows promise as an improved method to perform accurate and reproducible de novo assemblies of bacterial genomes, and our work provides important updates to the primary M. tuberculosis reference genome.

Human Plasma Proteome During Normal Pregnancy

The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.

Candidate Modifier Genes for the Penetrance of Leber's Hereditary Optic Neuropathy

Leber’s hereditary optic neuropathy (LHON) is a maternally transmitted disease caused by mitochondria DNA (mtDNA) mutation. It is characterized by acute and subacute visual loss predominantly affecting young men. The mtDNA mutation is transmitted to all maternal lineages. However, only approximately 50% of men and 10% of women harboring a pathogenic mtDNA mutation develop optic neuropathy, reflecting both the incomplete penetrance and its unexplained male prevalence, where over 80% of patients are male. Nuclear modifier genes have been presumed to affect the penetrance of LHON. With conventional genetic methods, prior studies have failed to solve the underlying pathogenesis. Whole exome sequencing (WES) is a new molecular technique for sequencing the protein-coding region of all genes in a whole genome. We performed WES from five families with 17 members. These samples were divided into the proband group (probands with acute onset of LHON, n = 7) and control group (carriers including mother and relative carriers with mtDNSA 11778 mutation, without clinical manifestation of LHON, n = 10). Through whole exome analysis, we found that many mitochondria related (MT-related) nuclear genes have high percentage of variants in either the proband group or control group. The MT genes with a difference over 0.3 of mutation percentage between the proband and control groups include AK4, NSUN4, RDH13, COQ3, and FAHD1. In addition, the pathway analysis revealed that these genes were associated with cofactor metabolism pathways. Family-based analysis showed that several candidate MT genes including METAP1D (c.41G > T), ACACB (c.1029del), ME3 (c.972G > C), NIPSNAP3B (c.280G > C, c.476C > G), and NSUN4 (c.4A > G) were involved in the penetrance of LHON. A GWAS (genome wide association study) was performed, which found that ADGRG5 (Chr16:575620A:G), POLE4 (Chr2:7495872T:G), ERMAP (Chr1:4283044A:G), PIGR (Chr1:2069357C:T;2069358G:A), CDC42BPB (Chr14:102949A:G), PROK1 (Chr1:1104562A:G), BCAN (Chr 1:1566582C:T), and NES (Chr1:1566698A:G,1566705T:C, 1566707T:C) may be involved. The incomplete penetrance and male prevalence are still the major unexplained issues in LHON. Through whole exome analysis, we found several MT genes with a high percentage of variants were involved in a family-based analysis. Pathway analysis suggested a difference in the mutation burden of MT genes underlining the biosynthesis and metabolism pathways. In addition, the GWAS analysis also revealed several candidate nuclear modifier genes. The new technology of WES contributes to provide a highly efficient candidate gene screening function in molecular genetics.

Diagnosis of a Single-Nucleotide Variant in Whole-Exome Sequencing Data for Patients With Inherited Diseases: Machine Learning Study Using Artificial Intelligence Variant Prioritization

BACKGROUND

In recent years, thanks to the rapid development of next-generation sequencing (NGS) technology, an entire human genome can be sequenced in a short period. As a result, NGS technology is now being widely introduced into clinical diagnosis practice, especially for diagnosis of hereditary disorders. Although the exome data of single-nucleotide variant (SNV) can be generated using these approaches, processing the DNA sequence data of a patient requires multiple tools and complex bioinformatics pipelines.

OBJECTIVE

This study aims to assist physicians to automatically interpret the genetic variation information generated by NGS in a short period. To determine the true causal variants of a patient with genetic disease, currently, physicians often need to view numerous features on every variant manually and search for literature in different databases to understand the effect of genetic variation.

METHODS

We constructed a machine learning model for predicting disease-causing variants in exome data. We collected sequencing data from whole-exome sequencing (WES) and gene panel as training set, and then integrated variant annotations from multiple genetic databases for model training. The model built ranked SNVs and output the most possible disease-causing candidates. For model testing, we collected WES data from 108 patients with rare genetic disorders in National Taiwan University Hospital. We applied sequencing data and phenotypic information automatically extracted by a keyword extraction tool from patient's electronic medical records into our machine learning model.

RESULTS

We succeeded in locating 92.5% (124/134) of the causative variant in the top 10 ranking list among an average of 741 candidate variants per person after filtering. AI Variant Prioritizer was able to assign the target gene to the top rank for around 61.1% (66/108) of the patients, followed by Variant Prioritizer, which assigned it for 44.4% (48/108) of the patients. The cumulative rank result revealed that our AI Variant Prioritizer has the highest accuracy at ranks 1, 5, 10, and 20. It also shows that AI Variant Prioritizer presents better performance than other tools. After adopting the Human Phenotype Ontology (HPO) terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108).

CONCLUSIONS

We successfully applied sequencing data from WES and free-text phenotypic information of patient's disease automatically extracted by the keyword extraction tool for model training and testing. By interpreting our model, we identified which features of variants are important. Besides, we achieved a satisfactory result on finding the target variant in our testing data set. After adopting the HPO terms by looking up the databases, the top 10 ranking list can be increased to 93.5% (101/108). The performance of the model is similar to that of manual analysis, and it has been used to help National Taiwan University Hospital with a genetic diagnosis.

Time and age dependent regulation of neuroinflammation in a rat model of mesial temporal lobe epilepsy: Correlation with human data

Acute brain insults trigger diverse cellular and signaling responses and often precipitate epilepsy. The cellular, molecular and signaling events relevant to the emergence of the epileptic brain, however, remain poorly understood. These multiplex structural and functional alterations tend also to be opposing - some homeostatic and reparative while others disruptive; some associated with growth and proliferation while others, with cell death. To differentiate pathological from protective consequences, we compared seizure-induced changes in gene expression hours and days following kainic acid (KA)-induced status epilepticus (SE) in postnatal day (P) 30 and P15 rats by capitalizing on age-dependent differential physiologic responses to KA-SE; only mature rats, not immature rats, have been shown to develop spontaneous recurrent seizures after KA-SE. To correlate gene expression profiles in epileptic rats with epilepsy patients and demonstrate the clinical relevance of our findings, we performed gene analysis on four patient samples obtained from temporal lobectomy and compared to four control brains from NICHD Brain Bank. Pro-inflammatory gene expressions were at higher magnitudes and more sustained in P30. The inflammatory response was driven by the cytokines IL-1β, IL-6, and IL-18 in the acute period up to 72 h and by IL-18 in the subacute period through the 10-day time point. In addition, a panoply of other immune system genes was upregulated, including chemokines, glia markers and adhesion molecules. Genes associated with the mitogen activated protein kinase (MAPK) pathways comprised the largest functional group identified. Through the integration of multiple ontological databases, we analyzed genes belonging to 13 separate pathways linked to Classical MAPK ERK, as well as stress activated protein kinases (SAPKs) p38 and JNK. Interestingly, genes belonging to the Classical MAPK pathways were mostly transiently activated within the first 24 h, while genes in the SAPK pathways had divergent time courses of expression, showing sustained activation only in P30. Genes in P30 also had different regulatory functions than in P15: P30 animals showed marked increases in positive regulators of transcription, of signaling pathways as well as of MAPKKK cascades. Many of the same inflammation-related genes as in epileptic rats were significantly upregulated in human hippocampus, higher than in lateral temporal neocortex. They included glia-associated genes, cytokines, chemokines and adhesion molecules and MAPK pathway genes. Uniquely expressed in human hippocampus were adaptive immune system genes including immune receptors CDs and MHC II HLAs. In the brain, many immune molecules have additional roles in synaptic plasticity and the promotion of neurite outgrowth. We propose that persistent changes in inflammatory gene expression after SE leads not only to structural damage but also to aberrant synaptogenesis that may lead to epileptogenesis. Furthermore, the sustained pattern of inflammatory genes upregulated in the epileptic mature brain was distinct from that of the immature brain that show transient changes and are resistant to cell death and neuropathologic changes. Our data suggest that the epileptogenic process may be a result of failed cellular signaling mechanisms, where insults overwhelm the system beyond a homeostatic threshold.

Pregnancy-specific responses to COVID-19 are revealed by high-throughput proteomics of human plasma

Pregnant women are at greater risk of adverse outcomes, including mortality, as well as obstetrical complications resulting from COVID-19. However, pregnancy-specific changes that underlie such worsened outcomes remain unclear. Herein, we profiled the plasma proteome of pregnant and non-pregnant COVID-19 patients and controls and showed alterations that display a dose-response relationship with disease severity; yet, such proteomic perturbations are dampened during pregnancy. In both pregnant and non-pregnant state, the proteome response induced by COVID-19 showed enrichment of mediators implicated in cytokine storm, endothelial dysfunction, and angiogenesis. Shared and pregnancy-specific proteomic changes were identified: pregnant women display a tailored response that may protect the conceptus from heightened inflammation, while non-pregnant individuals display a stronger response to repel infection. Furthermore, the plasma proteome can accurately identify COVID-19 patients, even when asymptomatic or with mild symptoms. This study represents the most comprehensive characterization of the plasma proteome of pregnant and non-pregnant COVID-19 patients.

OBI: A computational tool for the analysis and systematization of the positive selection in proteins

There are multiple tools for positive selection analysis, including vaccine design and detection of variants of circulating drug-resistant pathogens in population selection. However, applying these tools to analyze a large number of protein families or as part of a comprehensive phylogenomics pipeline could be challenging. Since many standard bioinformatics tools are only available as executables, integrating them into complex Bioinformatics pipelines may not be possible.

We have developed OBI, an open-source tool aimed to facilitate positive selection analysis on a large scale. It can be used as a stand-alone command-line app that can be easily installed and used as a Conda package.

Some advantages of using OBI are:•

It speeds up the analysis by automating the entire process

•

It allows multiple starting points and customization for the analysis

•

It allows the retrieval and linkage of structural and evolutive data for a protein through

We hope to provide with OBI a solution for reliably speeding up large-scale protein evolutionary and structural analysis.

Extended gene set analysis of human neuro-psychiatric traits shows enrichment in brain-expressed human accelerated regions across development

Human neuropsychiatric disorders are associated with genetic and environmental factors affecting the brain, which has been subjected to strong evolutionary pressures resulting in an enlarged cerebral cortex and improved cognitive performance. Thus, genes involved in human brain evolution may also play a role in neuropsychiatric disorders. We test whether genes associated with 7 neuropsychiatric phenotypes are enriched in genomic regions that have experienced rapid changes in human evolution (HARs) and importantly, whether HAR status interacts with developmental brain expression to predict associated genes. We used the most recent publicly available GWAS and gene expression data to test for enrichment of HARs, brain expression, and their interaction. These revealed significant interactions between HAR status and whole-brain expression across developmental stages, indicating that the relationship between brain expression and association with schizophrenia and intelligence is stronger among HAR than non-HAR genes. Follow-up regional analyses indicated that predicted HAR-expression interaction effects may vary substantially across regions and developmental stages. Although depression indicated significant enrichment of HAR genes, little support was found for HAR enrichment among bipolar, autism, ADHD, or Alzheimer's associated genes. Our results indicate that intelligence, schizophrenia, and depression-associated genes are enriched for those involved in the evolution of the human brain. These findings highlight promising candidates for follow-up study and considerations for novel drug development, but also caution careful assessment of the translational ability of animal models for studying neuropsychiatric traits in the context of HARs, and the importance of using humanized animal models or human-derived tissues when researching these traits.

DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform

INTRODUCTION

As a mid-size international pharmaceutical company, we initiated four years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named "Patrimony" was built-up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of Artificial Intelligence.

AREAS COVERED

Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in Immuno-inflammatory diseases, and current ongoing extension to applications to Oncology and Neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates.

EXPERT OPINION

We report our achievements, but also our challenges in implementing data access and governance processes, building-up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

Silva J, Almeida JR, Matos S. Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available.

Database URL

https://github.com/bioinformatics-ua/biocreativeVII_track2

Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohorts

Overtreatment remains a pervasive problem in prostate cancer (PCa) management due to the highly variable and often indolent course of disease. Molecular signatures derived from gene expression profiling have played critical roles in guiding PCa treatment decisions. Many gene expression signatures have been developed to improve the risk stratification of PCa and some of them have already been applied to clinical practice. However, no comprehensive evaluation has been performed to compare the performance of these signatures. In this study, we conducted a systematic and unbiased evaluation of 15 machine learning (ML) algorithms and 30 published PCa gene expression-based prognostic signatures leveraging 10 transcriptomics datasets with 1,558 primary PCa patients from public data repositories. This analysis revealed that survival analysis models outperformed binary classification models for risk assessment, and the performance of the survival analysis methods - Cox model regularized with ridge penalty (Cox-Ridge) and partial least squares regression for Cox model (Cox-PLS) - were generally more robust than the other methods. Based on the Cox-Ridge algorithm, several top prognostic signatures displayed comparable or even better performance than commercial panels. These findings will facilitate the identification of existing prognostic signatures that are promising for further validation in prospective studies and promote the development of robust prognostic models to guide clinical decision-making. Moreover, this study provides a valuable data resource from large primary PCa cohorts, which can be used to develop, validate, and evaluate novel statistical methodologies and molecular signatures to improve PCa management.

The amniotic fluid proteome changes with gestational age in normal pregnancy: a cross-sectional study

The cell-free transcriptome in amniotic fluid (AF) has been shown to be informative of physiologic and pathologic processes in pregnancy; however, the change in AF proteome with gestational age has mostly been studied by targeted approaches. The objective of this study was to describe the gestational age-dependent changes in the AF proteome during normal pregnancy by using an omics platform. The abundance of 1310 proteins was measured on a high-throughput aptamer-based proteomics platform in AF samples collected from women during midtrimester (16-24 weeks of gestation, n = 15) and at term without labor (37-42 weeks of gestation, n = 13). Only pregnancies without obstetrical complications were included in the study. Almost 25% (320) of AF proteins significantly changed in abundance between the midtrimester and term gestation. Of these, 154 (48.1%) proteins increased, and 166 (51.9%) decreased in abundance at term compared to midtrimester. Tissue-specific signatures of the trachea, salivary glands, brain regions, and immune system were increased while those of the gestational tissues (uterus, placenta, and ovary), cardiac myocytes, and fetal liver were decreased at term compared to midtrimester. The changes in AF protein abundance were correlated with those previously reported in the cell-free AF transcriptome. Intersecting gestational age-modulated AF proteins and their corresponding mRNAs previously reported in the maternal blood identified neutrophil-related protein/mRNA pairs that were modulated in the same direction. The first study to utilize an aptamer-based assay to profile the AF proteome modulation with gestational age, it reveals that almost one-quarter of the proteins are modulated as gestation advances, which is more than twice the fraction of altered plasma proteins (~ 10%). The results reported herein have implications for future studies focused on discovering biomarkers to predict, monitor, and diagnose obstetrical diseases.

Adhesion of enteropathogenic, enterotoxigenic and commensal Escherichia coli to the Major Zymogen Granule Membrane Glycoprotein 2

Pathogenic bacteria, such as enteropathogenic Escherichia coli (EPEC) and enterotoxigenic E. coli (ETEC), cause diarrhea in mammals. In particular, E. coli colonizes and infects the gastrointestinal tract via type 1 fimbriae (T1F). Here, the major zymogen granule membrane glycoprotein 2 (GP2) acts as a host cell receptor. GP2 is also secreted by the pancreas and various mucous glands, interacting with luminal type 1 fimbriae-positive E. coli. It is unknown whether GP2 isoforms demonstrate specific E. coli pathotype binding. In this study, we investigated interactions of human, porcine, and bovine EPEC and ETEC, as well as commensal E. coli isolates with human, porcine, and bovine GP2. We first defined pathotype- and host-associated FimH variants. Second, we could prove that GP2 isoforms bound to FimH variants to various degrees. However, the GP2-FimH interactions did not seem to be influenced by the host specificity of E. coli. In contrast, soluble GP2 affected ETEC infection and phagocytosis rates of macrophages. Preincubation of the ETEC pathotype with GP2 reduced the infection of cell lines. Furthermore, preincubation of E. coli with GP2 improved the phagocytosis rate of macrophages. Our findings suggest that GP2 plays a role in the defense against E. coli infection and in the corresponding host immune response.

IMPORTANCE Infection by pathogenic bacteria, such as certain Escherichia coli pathotypes, results in diarrhea in mammals. Pathogens, including zoonotic agents, can infect different hosts or show host specificity. There are Escherichia coli strains which are frequently transmitted between humans and animals, whereas other Escherichia coli strains tend to colonize only one host. This host specificity is still not fully understood. We show that glycoprotein 2 is a selective receptor for particular Escherichia coli strains or variants of the adhesin FimH but not a selector for a species-specific Escherichia coli group. We demonstrate that GP2 is involved in the regulation of colonization and infection and thus represents a molecule of interest for the prevention or treatment of disease.

Natural Language Processing-Assisted Literature Retrieval and Analysis for Combination Therapy in Cancer

PURPOSE

Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review.

METHODS

We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies.

RESULTS

In a small pilot study, RetriLite achieved an F1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface.

CONCLUSION

RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain.

A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining

The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug-disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist's perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.

Network medicine for disease module identification and drug repurposing with the NeDRex platform

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

mapMECFS: a portal to enhance data discovery across biological disciplines and collaborative sites

Background

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease which involves multiple body systems (e.g., immune, nervous, digestive, circulatory) and research domains (e.g., immunology, metabolomics, the gut microbiome, genomics, neurology). Despite several decades of research, there are no established ME/CFS biomarkers available to diagnose and treat ME/CFS. Sharing data and integrating findings across these domains is essential to advance understanding of this complex disease by revealing diagnostic biomarkers and facilitating discovery of novel effective therapies.

Methods

The National Institutes of Health funded the development of a data sharing portal to support collaborative efforts among an initial group of three funded research centers. This was subsequently expanded to include the global ME/CFS research community. Using the open-source comprehensive knowledge archive network (CKAN) framework as the base, the ME/CFS Data Management and Coordinating Center developed an online portal with metadata collection, smart search capabilities, and domain-agnostic data integration to support data findability and reusability while reducing the barriers to sustainable data sharing.

Results

We designed the mapMECFS data portal to facilitate data sharing and integration by allowing ME/CFS researchers to browse, share, compare, and download molecular datasets from within one data repository. At the time of publication, mapMECFS contains data curated from public data repositories, peer-reviewed publications, and current ME/CFS Research Network members.

Conclusions

mapMECFS is a disease-specific data portal to improve data sharing and collaboration among ME/CFS researchers around the world. mapMECFS is accessible to the broader research community with registration. Further development is ongoing to include novel systems biology and data integration methods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03127-3.

Data Integration Using Tensor Decomposition for The Prediction of miRNA-Disease Associations

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method.

Experiment level curation of transcriptional regulatory interactions in neurodevelopment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we reason that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the theoretical importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965; 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high resolution DTRI resource to support the development of large-scale TRNs.

The capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we reason that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of detail, we equip the users with more granular information than other similar resources, enabling more informed assessments of reliability.

StAR-Related Lipid Transfer (START) Domains Across the Rice Pangenome Reveal How Ontogeny Recapitulated Selection Pressures During Rice Domestication

The StAR-related lipid transfer (START) domain containing proteins or START proteins, encoded by a plant amplified family of evolutionary conserved genes, play important roles in lipid binding, transport, signaling, and modulation of transcriptional activity in the plant kingdom, but there is limited information on their evolution, duplication, and associated sub- or neo-functionalization. Here we perform a comprehensive investigation of this family across the rice pangenome, using 10 wild and cultivated varieties. Conservation of START domains across all 10 rice genomes suggests low dispensability and critical functional roles for this family, further supported by chromosomal mapping, duplication and domain structure patterns. Analysis of synteny highlights a preponderance of segmental and dispersed duplication among STARTs, while transcriptomic investigation of the main cultivated variety Oryza sativa var. japonica reveals sub-functionalization amongst genes family members in terms of preferential expression across various developmental stages and anatomical parts, such as flowering. Ka/Ks ratios confirmed strong negative/purifying selection on START family evolution, implying that ontogeny recapitulated selection pressures during rice domestication. Our findings provide evidence for high conservation of START genes across rice varieties in numbers, as well as in their stringent regulation of Ka/Ks ratio, and showed strong functional dependency of plants on START proteins for their growth and reproductive development. We believe that our findings advance the limited knowledge about plant START domain diversity and evolution, and pave the way for more detailed assessment of individual structural classes of START proteins among plants and their domain specific substrate preferences, to complement existing studies in animals and yeast.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.

Embryonic thermal manipulation impacts the postnatal transcriptome response of heat-challenged Japanese quails

BACKGROUND

The thermal-manipulation (TM) during egg incubation is a cyclic exposure to hot or cold temperatures during embryogenesis that is associated to long-lasting effects on growth performance, physiology, metabolism and temperature tolerance in birds. An increase of the incubation temperature of Japanese quail eggs affected the embryonic and post-hatch survival, growth, surface temperatures and blood characteristics potentially related to thermoregulation capacities. To gain new insights in the molecular basis of TM in quails, we investigated by RNA-seq the hypothalamus transcriptome of 35 days-old male and female quails that were treated by TM or not (C, control) during embryogenesis and that were exposed (HC) or not (RT) to a 36 °C heat challenge for 7 h before sampling.

RESULTS

For males, 76, 27, 47 and 0 genes were differentially expressed in the CHC vs. CRT, CRT vs. TMRT, TMHC vs. TMRT and CHC vs. TMHC comparisons, respectively. For females, 17, 0, 342 and 1 genes were differentially expressed within the same respective comparisons. Inter-individual variability of gene expression response was observed particularly when comparing RT and HC female animals. The differential expression of several genes was corroborated by RT-qPCR analysis. Gene Ontology functional analysis of the differentially expressed genes showed a prevalent enrichment of terms related to cellular responses to stimuli and gene expression regulation in both sexes. Gene Ontology terms related to the membrane transport, the endoplasmic reticulum and mitochondrial functions as well as DNA metabolism and repair were also identified in specific comparisons and sexes.

CONCLUSIONS

TM had little to no effect on the regulation of gene expression in the hypothalamus of 35 days-old Japanese quails. However, the consequences of TM on gene expression were revealed by the HC, with sex-specific and common functions altered. The effects of the HC on gene expression were most prominent in TM females with a ~ 20-fold increase of the number of differentially expressed genes, suggesting that TM may enhance the gene response during challenging conditions in female quail hypothalamus. TM may also promote new cellular strategies in females to help coping to the adverse conditions as illustrated by the identification of differentially expressed genes related to the mitochondrial and heat-response functions.