Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization.

BACKGROUND: Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization. RESULTS: MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. CONCLUSION: These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

BACKGROUND: Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and have outperformed statistic approaches in cell clustering. RESULTS: Using cross-correlation to capture gene-gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene-gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporal features. CONCLUSION: Static gene-gene interactions have been extracted as temporal features to boost clustering performance in single-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that only employ temporal patterns.

Research Progress on Photoperiod Gene Regulation of Heading Date in Rice.

Heading date is a critical physiological process in rice that is influenced by both genetic and environmental factors. The photoperiodic pathway is a primary regulatory mechanism for rice heading, with key florigen genes Hd3a (Heading date 3a) and RFT1 (RICE FLOWERING LOCUS T1) playing central roles. Upstream regulatory pathways, including Hd1 and Ehd1, also significantly impact this process. This review aims to provide a comprehensive examination of the localization, cloning, and functional roles of photoperiodic pathway-related genes in rice, and to explore the interactions among these genes as well as their pleiotropic effects on heading date. We systematically review recent advancements in the identification and functional analysis of genes involved in the photoperiodic pathway. We also discuss the molecular mechanisms underlying rice heading date variation and highlight the intricate interactions between key regulatory genes. Significant progress has been made in understanding the molecular mechanisms of heading date regulation through the cloning and functional analysis of photoperiod-regulating genes. However, the regulation of heading date remains complex, and many underlying mechanisms are not yet fully elucidated. This review consolidates current knowledge on the photoperiodic regulation of heading date in rice, emphasizing novel findings and gaps in the research. It highlights the need for further exploration of the interactions among flowering-related genes and their response to environmental signals. Despite advances, the full regulatory network of heading date remains unclear. Further research is needed to elucidate the intricate gene interactions, transcriptional and post-transcriptional regulatory mechanisms, and the role of epigenetic factors such as histone methylation in flowering time regulation. This review provides a detailed overview of the current understanding of photoperiodic pathway genes in rice, setting the stage for future research to address existing gaps and improve our knowledge of rice flowering regulation.

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions.

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.

BACKGROUND: As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. RESULTS: Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress. CONCLUSIONS: To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

A Conversation with James Ntambi.

An interview with James M. Ntambi, professor of biochemistry and the Katherine Berns Van Donk Steenbock Professor in Nutrition, College of Agricultural and Life Sciences, at the University of Wisconsin-Madison, took place via Zoom in April 2022. He was interviewed by Patrick J. Stover, director of the Institute for Advancing Health through Agriculture and professor of nutrition and biochemistry and biophysics at Texas A&M University. Dr. James Ntambi is a true pioneer in the field of nutritional biochemistry. He was among the very first to discover and elucidate the role that diet and nutrients play in regulating metabolism through changes in the expression of metabolic genes, focusing on the de novo lipogenesis pathways. As an African immigrant from Uganda, his love of science and his life experiences in African communities suffering from severe malnutrition molded his scientific interests at the interface of biochemistry and nutrition. Throughout his career, he has been an academic role model, a groundbreaking nutrition scientist, and an educator. His commitment to experiential learning through the many study-abroad classes he has hosted in Uganda has provided invaluable context for American students in nutrition. Dr. Ntambi's passion for education and scientific discovery is his legacy, and the field of nutrition has benefited enormously from his unique perspectives and contributions to science that are defined by his scientific curiosity, his generosity to his students and colleagues, and his life experiences. The following is an edited transcript.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms.

BACKGROUND: Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. PURPOSE: A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). METHODS: The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values. RESULTS: In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. CONCLUSIONS: In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Identifying Core Genes Related to Low-Temperature Stress Resistance in Quinoa Seedlings Based on WGCNA.

Quinoa is a nutritious crop that is tolerant to extreme environmental conditions; however, low-temperature stress can affect quinoa growth, development, and quality. Considering the lack of molecular research on quinoa seedlings under low-temperature stress, we utilized a Weighted Gene Co-Expression Network Analysis to construct weighted gene co-expression networks associated with physiological indices and metabolites related to low-temperature stress resistance based on transcriptomic data. We screened 11 co-expression modules closely related to low-temperature stress resistance and selected 12 core genes from the two modules that showed the highest associations with the target traits. Following the functional annotation of these genes to determine the key biological processes and metabolic pathways involved in low-temperature stress, we identified four important transcription factors involved in resistance to low-temperature stress: gene-LOC110731664, gene-LOC110736639, gene-LOC110684437, and gene-LOC110720903. These results provide insights into the molecular genetic mechanism of quinoa under low-temperature stress and can be used to breed lines with tolerance to low-temperature stress.

Linking plant functional genes to rhizosphere microbes: a review.

The importance of rhizomicrobiome in plant development, nutrition acquisition and stress tolerance is unquestionable. Relevant plant genes corresponding to the above functions also regulate rhizomicrobiome construction. Deciphering the molecular regulatory network of plant-microbe interactions could substantially contribute to improving crop yield and quality. Here, the plant gene-related nutrient uptake, biotic and abiotic stress resistance, which may influence the composition and function of microbial communities, are discussed in this review. In turn, the influence of microbes on the expression of functional plant genes, and thereby plant growth and immunity, is also reviewed. Moreover, we have specifically paid attention to techniques and methods used to link plant functional genes and rhizomicrobiome. Finally, we propose to further explore the molecular mechanisms and signalling pathways of microbe-host gene interactions, which could potentially be used for managing plant health in agricultural systems.

Natural variation in Avr3D1 from Zymoseptoria sp. contributes to quantitative gene-for-gene resistance and to host specificity.

Successful host colonization by plant pathogens requires the circumvention of host defense responses, frequently through sequence modifications in secreted pathogen proteins known as avirulence factors (Avrs). Although Avr sequences are often polymorphic, the contribution of these polymorphisms to virulence diversity in natural pathogen populations remains largely unexplored. We used molecular genetic tools to determine how natural sequence polymorphisms of the avirulence factor Avr3D1 in the wheat pathogen Zymoseptoria tritici contributed to adaptive changes in virulence. We showed that there is a continuous distribution in the magnitude of resistance triggered by different Avr3D1 isoforms and demonstrated that natural variation in an Avr gene can lead to a quantitative resistance phenotype. We further showed that homologues of Avr3D1 in two nonpathogenic sister species of Z. tritici are recognized by some wheat cultivars, suggesting that Avr-R gene-for-gene interactions can contribute to nonhost resistance. We suggest that the mechanisms underlying host range, qualitative resistance, and quantitative resistance are not exclusive.

Systematic Characterization of Multi-Rust Resistance Genes from a 'Parula × Thatcher' Population with a High-Density Genetic Map.

Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here, using a high-density genetic map, a double haploid population, and multi-rust field testing, we aimed to systematically characterize the most effective gene pyramids for rust resistance from the durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains Lr34/Yr18/Sr57 (Lr34), Lr46/Yr29/Sr58 (Lr46), Lr27/Yr30/Sr2 (Sr2), and Lr68. The efficacy, magnitude of effect, and interactions varied for the three rust diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Lr34. Using this approach, we found that Lr34 and Lr68 have a strong additive effect for leaf rust, whereas no additive effects were observed for any rusts between Lr34 and Lr46. Lr34 combined synergistically with Sr12 from Thatcher for stem rust, whereas the additive effect of Lr34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLs) from Parula were identified in this study, a stable QTL QLr-7BS and QLr-5AS, which showed Lr34 dependent expression. With these findings, we propose combining two to three high-value genes from Canadian wheat (e.g., Sr12 from Thatcher) with a foundational multi-adult plant resistance cassette for desirable and durable resistance to all three rusts in Canadian wheat.

Abscisic acid in plants under abiotic stress: crosstalk with major phytohormones.

KEY MESSAGE: Extensive crosstalk exists among ABA and different phytohormones that modulate plant tolerance against different abiotic stress. Being sessile, plants are exposed to a wide range of abiotic stress (drought, heat, cold, salinity and metal toxicity) that exert unwarranted threat to plant life and drastically affect growth, development, metabolism, and yield of crops. To cope with such harsh conditions, plants have developed a wide range of protective phytohormones of which abscisic acid plays a pivotal role. It controls various physiological processes of plants such as leaf senescence, seed dormancy, stomatal closure, fruit ripening, and other stress-related functions. Under challenging situations, physiological responses of ABA manifested in the form of morphological, cytological, and anatomical alterations arise as a result of synergistic or antagonistic interaction with multiple phytohormones. This review provides new insight into ABA homeostasis and its perception and signaling crosstalk with other phytohormones at both molecular and physiological level under critical conditions including drought, salinity, heavy metal toxicity, and extreme temperature. The review also reveals the role of ABA in the regulation of various physiological processes via its positive or negative crosstalk with phytohormones, viz., gibberellin, melatonin, cytokinin, auxin, salicylic acid, jasmonic acid, ethylene, brassinosteroids, and strigolactone in response to alteration of environmental conditions. This review forms a basis for designing of plants that will have an enhanced tolerance capability against different abiotic stress.

Patient with PSEN1 Glu318Gly and Other Possible Disease Risk Mutations, Diagnosed with Early Onset Alzheimer's Disease.

In this manuscript, we introduced a French EOAD patient in Korea who carried the presenilin-1 (PSEN1) Glu318Gly mutations with four possible risk variants, including sortilin-related receptor 1 (SORL1) Glu270Lys, ATP-binding cassette subfamily A member 7 (ABCA7) Val1946Met, translocase of outer mitochondrial membrane 40 (TOMM40) Arg239Trp, and granulin (GRN) Ala505Gly. The patient started to present memory decline and behavioral dysfunction in his early 60s. His brain imaging presented amyloid deposits by positron emission tomography (PET-CT). The multimer detection system (MDS) screening test for plasma for amyloid oligomers was also positive, which supported the AD diagnosis. It was verified that PSEN1 Glu318Gly itself may not impact amyloid production. However, additional variants were found in other AD and non-AD risk genes, as follows: SORL1 Glu270Lys was suggested as a risk mutation for AD and could increase amyloid peptide production and impair endosome functions. ABCA7 Val1946Met was a novel variant that was predicted to be damaging. The GRN Ala505Gly was a variant with uncertain significance; however, it may reduce the granulin levels in the plasma of dementia patients. Pathway analysis revealed that PSEN1 Glu318Gly may work as a risk factor along with the SORL1 and ABCA7 variants since pathway analysis revealed that PSEN1 could directly interact with them through amyloid-related and lipid metabolism pathways. TOMM40 and PSEN1 could have common mechanisms through mitochondrial dysfunction. It may be possible that PSEN1 Glu318Gly and GRN Ala505Gly would impact disease by impairing immune-related pathways, including microglia and astrocyte development, or NFkB-related pathways. Taken together, the five risk factors may contribute to disease-related pathways, including amyloid and lipid metabolism, or impair immune mechanisms.

Evaluation of the Possible Pathways Involved in the Protective Effects of Quercetin, Naringenin, and Rutin at the Gene, Protein and miRNA Levels Using In-Silico Multidimensional Data Analysis.

Flavonoids are secondary metabolites that are non-essential for plant growth or survival, and they also provide numerous health benefits to humans. They are antioxidants that shield plants from the ill effects of ultraviolet light, pests, and diseases. They are beneficial to health for several reasons, including lowering inflammation, boosting cardiovascular health, and lowering cancer risk. This study looked into the physicochemical features of these substances to determine the potential pharmacological pathways involved in their protective actions. Potential targets responsible for the protective effects of quercetin, naringenin, and rutin were identified with SwissADME. The associated biological processes and protein-protein networks were analyzed by using the GeneMANIA, Metascape, and STRING servers. All the flavonoids were predicted to be orally bioavailable, with more than 90% targets as enzymes, including kinases and lyases, and with common targets such as NOS2, CASP3, CASP9, CAT, BCL2, TNF, and HMOX1. TNF was shown to be a major target in over 250 interactions. To extract the "biological meanings" from the MCODE networks' constituent parts, a GO enrichment analysis was performed on each one. The most important transcription factors in gene regulation were RELA, NFKB1, PPARG, and SP1. Treatment with quercetin, naringenin, or rutin increased the expression and interaction of the microRNAs' hsa-miR-34a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, and hsa-miR-26a-1-3p. The anticancer effects of hsa-miR-34a-5p have been experimentally confirmed. It also plays a critical role in controlling other cancer-related processes such as cell proliferation, apoptosis, EMT, and metastasis. This study's findings might lead to a deeper comprehension of the mechanisms responsible for flavonoids' protective effects and could present new avenues for exploration.

The missing heritability in type 1 diabetes.

Type 1 diabetes (T1D) is a complex autoimmune disease characterized by an absolute deficiency of insulin. It affects more than 20 million people worldwide and imposes an enormous financial burden on patients. The underlying pathogenic mechanisms of T1D are still obscure, but it is widely accepted that both genetics and the environment play an important role in its onset and development. Previous studies have identified more than 60 susceptible loci associated with T1D, explaining approximately 80%-85% of the heritability. However, most identified variants confer only small increases in risk, which restricts their potential clinical application. In addition, there is still a so-called 'missing heritability' phenomenon. While the gap between known heritability and true heritability in T1D is small compared with that in other complex traits and disorders, further elucidation of T1D genetics has the potential to bring novel insights into its aetiology and provide new therapeutic targets. Many hypotheses have been proposed to explain the missing heritability, including variants remaining to be found (variants with small effect sizes, rare variants and structural variants) and interactions (gene-gene and gene-environment interactions; e.g. epigenetic effects). In the following review, we introduce the possible sources of missing heritability and discuss the existing related knowledge in the context of T1D.

Nutrigenomics in crustaceans: Current status and future prospects.

In aquaculture, nutrigenomics or "nutritional genomics" is concerned with studying the impacts of nutrients and food ingredients on gene expressions and understanding the interactions that may occur between nutrients and dietary bioactive ingredients with the genome and cellular molecules of the treated aquatic animals at the molecular levels that will, in turn, mediate gene expression. This concept will throw light on or provide important information to recognize better how specific nutrients may influence the overall health status of aquatic organisms. In crustaceans, it is well known that the nutritional requirements vary among different species. Thus, studying the nutrigenomics in different crustacean species is of significant importance. Of interest, recognition of the actual mechanisms that may be associated with the effects of the nutrients on the immune responses of crustaceans will provide clear outstanding protection, build a solid immune system, and also decrease the possibilities of the emergence of infectious diseases in the culture systems. Similarly, the growth, molting, lipid metabolism, antioxidant capacity, and reproduction could be effectively enhanced by using specific nutrients. In the area of crustacean research, nutrigenomics has been rapidly grown for addressing several aspects related to the influences of nutrients on crustacean development. Several researchers have studied the relationships between several functional genes and their expression profile with several physiological functions of crustaceans. They found a close association between the effects of optimal feeding with efficient production, growth, reproduction development, and health status of several crustacean species. Moreover, they illustrated that regulation of the gene expression in individual cells by different nutrients and formulated feeds could improve the growth development and immunity-boosting of several crustacean species. The present review will spotlight on such relationships between the dietary nutrients and expression of genes linked with growth, metabolism, molting, antioxidant, reproduction, and immunity of several crustacean species. The literature included in this review article will provide references and future outlooks for the upcoming research plans. This will contribute positively for maintaining the sustainability of the sector of the crustacean industry.

Deep learning for inferring gene relationships from single-cell expression data.

Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.

The Relationship Between Chemokine and Chemokine Receptor Genes Polymorphisms and Chronic Obstructive Pulmonary Disease Susceptibility in Tatar Population from Russia: A Case Control Study.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease affecting primarily distal respiratory pathways and lung parenchyma. This study aimed to determine possible genetic association of chemokine and chemokine receptor genes polymorphisms with COPD in a Tatar population from Russia. SNPs of CCL20, CCR6, CXCL8, CXCR1, CXCR2, CCL8, CCL23, CCR2, and CX3CL1 genes and their gene-gene interactions were analyzed for association with COPD in cohort of 601 patients and 617 controls. As a result statistically significant associations with COPD in the study group under the biologically plausible assumption of additive genetic model were identified in CCL20 (rs6749704) (P = 0.00001, OR 1.55), CCR6 (rs3093024) (P = 0.0003, OR 0.74), CCL8 (rs3138035) (P = 0.0001, OR 0.67), CX3CL1 (rs170364) (P = 0.023, OR 1.21), CXCL8 (rs4073) (P = 0.007, OR 1.23), CXCR2 (rs2230054) (P = 0.0002, OR 1.32). Following SNPs CCL20 (rs6749704), CX3CL1 (rs170364), CCL8 (rs3138035), CXCL8 (rs4073), CXCR2 (rs2230054) showed statistically significant association with COPD only in smokers. The association of CCR6 (rs3093024) with COPD was confirmed both in smokers and in non-smokers. A relationship between smoking index and CCL20 (rs6749704) (P = 0.04), CCR6 (rs3093024) (P = 0.007), CCL8 (rs3138035) (P = 0.0043), and CX3CL1 (rs170364) (P = 0.04) was revealed. A significant genotype-dependent variation of Forced Vital Capacity was observed for CCL23 (rs854655) (P = 0.04). Forced Expiratory Volume in 1 s / Forced Vital Capacity ratio was affected by CCL23 (rs854655) (P = 0.05) and CXCR2 (rs1126579) (P = 0.02). Using the APSampler algorithm, we obtained nine gene-gene combinations that remained significantly associated with COPD; loci CCR2 (rs1799864) and CCL8 (rs3138035) were involved in the largest number of the combinations. Our results indicate that CCL20 (rs6749704), CCR6 (rs3093024), CCR2 (rs1799864), CCL8 (rs3138035), CXCL8 (rs4073), CXCR1 (rs2234671), CXCR2 (rs2230054), and CX3CL1 (rs170364) polymorphisms are strongly associated with COPD in Tatar population from Russia, alone and in combinations. For the first time combination of the corresponding SNPs were considered and as a result 8 SNP patterns were associated with increased risk of COPD.

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.