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Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep learning has shown superior performance over traditional machine learning approaches in image analysis. In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.

The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.

The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

White matter hyperintensities (WMHs) appear as regions of abnormally signal intensity on magnetic resonance imaging (MRI) images, that can be identified in MRI images of elderly people and ischemic stroke patients. However, manual segmentation and quantification of images with WMHs is laborious and time-consuming. Moreover, ischemic stroke lesion and WMHs appear as similar signals in MRI images, making it difficult to accurately segment the WMHs. Analysis of WMH-containing images is important for clinical diagnosis, and thus several segmentation methods have been proposed. However, these methods cannot accurately differentiate WMH and ischemic stroke lesions. We propose a deep convolutional neural network, M2DCNN, that can accurately segment WMHs and distinguish them from ischemic stroke lesions. M2DCNN consists of two subnets that rely on a set of novel multi-scale features and a novel architecture (inclusion of dense and dilated blocks). Our model is trained and evaluated on two public segmentation challenges with multi-modality MRI images. Empirical tests demonstrate that M2DCNN outperforms current segmentation methods. We empirically demonstrate that M2DCNN effectively separates WMHs from stroke lesions. Finally, ablation experiments reveal that both multi-scale features and architectural elements in our method contribute to the improved predictive performance.

Overlap of brain changes across mental disorders has reinforced transdiagnostic models. However, the developmental basis for this overlap is unclear as are neural differences among internalizing, externalizing and thought disorders. These issues are critical to inform the theoretical framework for hierarchical transdiagnostic psychiatric taxonomy. We examined cortical thickness (CT) difference between healthy controls (n=4041) and patients with externalizing (n=1182), internalizing (n=1959) and thought (n=347) disorders in preadolescents (9-10 years) from the Adolescent Brain and Cognitive Development Study using linear mixed models. Genome-wide association analysis and cell type specificity analysis were performed on regional CT across 4,716 unrelated European youth. We found that youth with externalizing or internalizing disorders, but not thought disorders, exhibited significantly thicker cortex than controls. Externalizing and internalizing disorders shared thicker CT in left pars opercularis and caudal middle frontal gyrus related to lower cognitive performance. Somatosensory and primary auditory cortex were uniquely affected in externalizing disorders; primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only group of externalizing disorders demonstrated significant CT increase than controls at 2-year follow-up and decelerated cortical thinning from 10 to 12 years old. At genetic level, genes associated with CT in common and disorders-specific regions were also implicated in related diagnostic families. Microglia were the cell-type associated with CT for both externalizing/internalizing while dopaminergic/glutamatergic/GABAergic cells related only to externalizing-specific regions. These results showed that distinct anatomical trajectories relevant to internalizing/externalizing phenotypes may result from unique genetic and cell-type changes, but these occur in the background of significantly shared morphological variance.

INTRODUCTIONCurrent findings of the relative influence of social isolation and loneliness on dementia are contradictory, and the potential neurobiological mechanisms are unclear.METHODSWe utilized the UK Biobank to investigate the relationships of social isolation and loneliness with dementia (n = 462,619). Neuroanatomical correlates were identified in a subset of participants (n = 32,263). The transcriptomic signatures of related brain changes were characterized by gene enrichment analysis.RESULTSAfter full adjustment, social isolation but not loneliness was associated with dementia (hazard ratio: 1.28, 95% confidence interval: 1.17-1.39). Isolated individuals had reduced gray matter volumes in temporal, frontal, occipital and subcortical regions (e.g., hippocampus and amygdala). Relevant brain changes were spatially correlated with genes involved in mitochondrial dysfunction and oxidative phosphorylation, and down-regulated Alzheimer’s disease-related genes.DISCUSSIONSocial isolation is an independent risk factor for dementia, which could be partly explained by related structural changes coupling with altered molecular functions.

Many of the gut-microbiome-derived signatures for liver cirrhosis, especially the important ones, were likely under the influence of proton pump inhibitors (PPIs). Wu et al. suggest that drug usage is a confounding factor in metagenomics analysis that should be controlled for.

Motivation Aligning single-cell transcriptomes is important for the joint analysis of multiple single-cell RNA sequencing datasets, which in turn is vital to establishing a holistic cellular landscape of certain biological processes. Although numbers of approaches have been proposed for this problem, most of which only consider mutual neighbors when aligning the cells without taking into account known cell type annotations. Results In this work, we present MAT2 that aligns cells in the manifold space with a deep neural network employing contrastive learning strategy. Compared with other manifold-based approaches, MAT2 has two-fold advantages. Firstly, with cell triplets defined based on known cell type annotations, the consensus manifold yielded by the alignment procedure is more robust especially for datasets with limited common cell types. Secondly, the batch-effect-free gene expression reconstructed by MAT2 can better help annotate cell types. Benchmarking results on real scRNA-seq datasets demonstrate that MAT2 outperforms existing popular methods. Moreover, with MAT2, the hematopoietic stem cells are found to differentiate at different paces between human and mouse. Availability and implementation MAT2 is publicly available at https://github.com/Zhang-Jinglong/MAT2. Supplementary information Supplementary data are available at Bioinformatics online.

BackgroundComorbidities greatly increase global health burdens, but the landscapes of their genetic factors have not been systematically investigated.MethodsWe used the hospital inpatient data of 385,335 patients in UK Biobank to investigate the comorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify comorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for interpretable comorbidity networks to detect the hub diseases and the involved molecules in comorbidity modules.Results11,285 comorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture level. The comorbidities affecting the same and different physiological systems showed different patterns at the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by comorbidities mainly converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable comorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the comorbidity modules could help provide useful insights into the genetic contributors for comorbiditities.ConclusionsOur results provide a systematic resource for understanding the genetic predispositions of comorbidity, and indicate that hub diseases and converged molecules and functions may be the key for treating comorbidity. We have created an online database to facilitate researchers and physicians to browse, search or download these comorbidities (https://comorbidity.comp-sysbio.org).

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value Availability: All scripts are written in R. Scripts for the TCA method and related statistics result can be freely accessed at https://github.com/Soulnature/TCA. Additional data related to this paper may be requested from the authors.

The human cerebral cortex undergoes profound structural and functional dynamic variations across the lifespan, whereas the underlying molecular mechanisms remain unclear. Here, with a novel method TCA (Transcriptome-connectome Correlation Analysis), which integrates the brain functional MR magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood, and late adulthood. The ASC gene signatures are significantly correlated with the cortical thickness (P-value P-value ARNT2 and its target gene ETF1 involved in Schizophrenia. We further validate the ASC gene signatures with published gene sets associated with the adult cortex, and confirm the robustness of TCA on other brain image datasets.

Background Compromised intestinal barrier (CIB) has been associated with many enteropathies, including colorectal cancer (CRC) and inflammatory bowel disease (IBD). We hypothesized that CIB could lead to increased host-derived contents including epithelial cells into the gut, change its physio-metabolic properties, and globally alter microbial community and metabolic capacities. Results Consistently, we found host DNA contents (HDCs), calculated as the percentage of metagenomic sequencing reads mapped to the host genome, were significantly elevated in patients of CRC and Crohn’s disease (CD). Consistent with our hypothesis, we found that HDC correlated with microbial- and metabolic-biomarkers of these diseases, contributed significantly to machine-learning models for patient stratification and was consequently ranked as a top contributor. CD patients with treatment could partially reverse the changes of many CD-signature species over time, with reduced HDC and fecal calprotectin (FCP) levels. Strikingly, HDC showed stronger correlations with the reversing changes of the CD-related species than FCP, and contributed greatly in classifying treatment responses, suggesting that it was also a biomarker for effective treatment. Conclusions Together, we revealed that association between HDCs and gut dysbiosis, and identified HDC as a novel biomarker from fecal metagenomics for diagnosis and effective treatment of intestinal diseases; our results also suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated.

With the ever increasing cost and time required for drug development, new strategies for drug development are highly demanded, whereas repurposing old drugs has attracted much attention in drug discovery. In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases. Benchmark results on FDA approved drugs have shown the superiority of PINA over traditional computational approaches in identifying new indications of old drugs. We further extend PINA to predict the novel indications of Traditional Chinese Medicines (TCMs) with Liuwei Dihuang Wan (LDW) as a case study. The predicted indications, including immune system disorders and tumor, are validated by expert knowledge and evidences from literature, demonstrating the effectiveness of our proposed computational approach.

Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.

Common intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC) and colorectal cancer (CRC), share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multi-disease models. Here, we performed meta-analyses on thirteen fecal metagenome datasets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple datasets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and -common clusters, and revealed their distinct phylogenetic relationships: species in CD-specific cluster are phylogenetically related, while those in CRC-specific cluster are more distant; strikingly, UC-specific species are phylogenetically closer to CRC, likely because UC-patients have higher risk of CRC. Consistent to their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. There were part of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection to gut microbes. With the marker species and pathways, we constructed four high-performance (including multi-disease) models with AUROC of 0.87 and true positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multi-disease diagnosis.ImportanceGut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multi-disease classifications. To the best of our knowledge, this is the first study that constructed multi-class models in these common intestinal diseases.

Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way. In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available. The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of biomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.

The post-translational modifications (PTMs) are important to protein activities and play key roles in various kinds of biological processes. It has been well recognized that the alteration of PTMs may lead to diseases. However, it is still not clear how the PTMs are related to diseases. In this work, we systematically investigated the associations between PTM residues and disease related mutations. In particular, the common PTMs, including phosphorylation, ubiquitylation and acetylation, were considered here. By analyzing the PTM sites and the amino acid mutations, we found that the amino acid mutations co-occurring with PTM sites and PTM cross-talks tend to be deleterious mutations in diseases. Specifically, the deleterious single amino acid variants (SAVs) associated with cancer and muscular diseases tend to co-occur with phosphorylation residues. The PTM sites of proteins from nuclear envelope, protein complex and lysosome were found to be more likely adjacent to the deleterious amino acid variations. These findings provide insights into the associations between PTMs and diseases, and can help identify novel disease genes in the future.

The natural products containing aristolochic acid (AA) have been widely used for acne, gastritis and so on. Recently, it is becoming accepted that AA may be responsible for acute and chronic renal failures as the side effects of Chinese herbs. However, it is unclear what happens in the cells after the AA treatment. In this study, the authors built a gene regulatory network as well as a microRNA-gene regulatory network to investigate the molecular dynamics induced by AA from a systematic perspective. With the regulatory networks, they detected some important pathways and biological processes that were affected by AA treatment, which can help explain the nephrotoxicity and carcinogenicity of AA. They found some important regulators and genes responding to AA treatment, and these genes have been reported to be related to the kidney functions, indicating their important roles in the toxicity of AA.

Drug resistance is becoming a serious problem that leads to the failure of standard treatments, which is generally developed because of genetic mutations of certain molecules. Here, we present GEAR (A database of G enomic E lements A ssociated with drug R esistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance. Right now, GEAR contains 1631 associations between 201 human drugs and 758 genes, 106 associations between 29 human drugs and 66 miRNAs, and 44 associations between 17 human drugs and 22 SNPs. These relationships are firstly extracted from primary literature with text mining and then manually curated. The drug resistome deposited in GEAR provides insights into the genetic factors underlying drug resistance. In addition, new indications and potential drug combinations can be identified based on the resistome. The GEAR database can be freely accessed through http://gear.comp-sysbio.org.

Cell state transition is one of the fundamental events in the development of multicellular organisms, and the transition trajectory path has recently attracted much attention. With the accumulation of large amounts of “-omics” data, it is becoming possible to get insights into the molecule mechanisms underlying the transitions between cell states. Here, we present CSTEA (Cell State Transition Expression Atlas), a webserver that organizes, analyzes and visualizes the time-course gene expression data during cell differentiation, cellular reprogramming and trans-differentiation in human and mouse. In particular, CSTEA defines gene signatures for uncharacterized stages during cell state transitions, thereby enabling both experimental and computational biologists to better understand the mechanisms of cell fate determination in mammals. To our best knowledge, CSTEA is the first webserver dedicated to the analysis of time-series gene expression data during cell state transitions. CSTEA is freely available at http://comp-sysbio.org/cstea/.

It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p0.000001), indicating that by incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets, and seemingly unrelated proteins can be revealed.

OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http://ogee.medgenius.info.

Although human and macaque share a lot of genes, their brains have different functions. In this study, we compare human and macaque brains from a systematic perspective by assuming that the difference between human and macaque brains is due to the difference of molecular circuits underlying brains instead of certain individual genes. Specially, we construct a gene interaction network for each brain region and investigate the difference between human and macaque brains based on those region-specific gene networks. We found that the genes in human brain networks tend to interact with each other compared with those of macaque brain networks. In addition, the human genes are more likely to form functional modules compared against macaque genes. Moreover, the human specific genes tend to interact with hub genes, while both the hub genes and human specific genes are enriched in brain functions. These findings can help better understand the functional divergence between human and macaque brains.

Despite the explosion in the numbers of cancer genomic studies, metastasis is still the major cause of cancer mortality. In breast cancer, approximately one-fifth of metastatic patients survive 5 years. Therefore, detecting the patients at a high risk of developing distant metastasis at first diagnosis is critical for effective treatment strategy. We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequencing of our collected 78 normal-paired breast cancers. Unlike driver mutations occurring commonly in cancers as reported in the literature, the mutations detected here are relatively rare mutations occurring in less than half metastatic samples. By supposing that the driver mutations should affect the metastasis gene signatures, we develop a novel computational pipeline to identify the driver mutations that affect transcription factors regulating metastasis gene signatures. We identify driver mutations in ADPGK, NUP93, PCGF6, PKP2 and SLC22A5, which are verified to enhance cancer cell migration and prompt metastasis with in vitro experiments. The discovered somatic mutations may be helpful for identifying patients who are likely to develop distant metastasis.

The timely and accurate diagnosis of Alzheimer's Disease (AD) is important for preventing the progress of the irreversible disease. Recently, various types of imaging techniques, e.g. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), have been widely used for the diagnosis of AD. Despite their usefulness, the image data generated are high-dimensional and noisy which make it difficult to give accurate diagnosis. In this paper, we propose a novel feature selection approach to detect the informative features from MRI data, which improves both the diagnosis accuracy and reduces the computational cost. Benchmarking results on the AD challenge datasets show that our proposed approach outperforms other popular feature selection methods. Furthermore, compared with the top winners of the challenge, our approach performs best in diagnosis of AD and has comparable performance with others in the detection of mild cognitive impairments (MCIs). The good performance on the real data demonstrate that our proposed approach is effective in prediction of ADs.

Objective: A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs. Materials and Methods: In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods. Results and Discussion: As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method. Conclusion: We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.

Pollen mother cells (PMCs) represent a critical early stage in plant sexual reproduction in which the stage is set for male gamete formation. Understanding the global molecular genetics of this early meiotic stage has so far been limited to whole stamen or floret transcriptome studies, but since PMCs are a discrete population of cells in developmental synchrony, they provide the potential for precise transcriptome analysis and for enhancing our understanding of the transition to meiosis. As a step toward identifying the premeiotic transcriptome, we performed microarray analysis on a homogenous population of rice (Oryza sativa) PMCs isolated by laser microdissection and compared them with those of tricellular pollen and seedling. Known meiotic genes, including OsSPO11-1, PAIR1, PAIR2, PAIR3, OsDMC1, OsMEL1, OsRAD21-4, OsSDS, and ZEP1, all showed preferential expression in PMCs. The Kyoto Encyclopedia of Genes and Genomes pathways significantly enriched in PMC-preferential genes are DNA replication and repair pathways. Our genome-wide survey showed that, in the buildup to meiosis, PMCs accumulate the molecular machinery for meiosis at the mRNA level. We identified 1,158 PMC-preferential genes and suggested candidate genes and pathways involved in meiotic recombination and meiotic cell cycle control. Regarding the developmental context for meiosis, the DEF-like, AGL2-like, and AGL6-like subclades of MADS box transcription factors are PMC-preferentially expressed, the trans-zeatin type of cytokinin might be preferentially synthesized, and the gibberellin signaling pathway is likely active in PMCs. The ubiquitin-mediated proteolysis pathway is enriched in the 127 genes that are expressed in PMCs but not in tricellular pollen or seedling.

Adverse drug reactions (ADRs) are the leading factors of drug attrition in drug development and post-market drug withdrawal. The identification of potential ADRs can help prevent the failure of drug discovery and improve development efficiency. Furthermore, elucidating possible ADRs for known drugs can help better understand the mechanism of drug actions and even find new indications of old drugs. Unfortunately, only the ADRs of some well-studied drugs are available and our knowledge about ADRs of available drugs is far from complete. Recently, with more structural and omics data available, some computational approaches have been developed for predicting drug ADRs. In this review, we present a survey on the recent progresses on computational methodologies that have been developed for ADR prediction based on various kinds of data, and some ADR related resources are also introduced.

Amiloride-sensitive epithelial Na+channels (ENaC) regulate fluid balance in the alveoli and are regulated by oxidative stress. Since glutathione (GSH) is the predominant antioxidant in the lungs, we proposed that changes in glutathione redox potential (Eh) would alter cell signaling and have an effect on ENaC open probability ( Po). In the present study, we used single channel patch-clamp recordings to examine the effect of oxidative stress, via direct application of glutathione disulfide (GSSG), on ENaC activity. We found a linear decrease in ENaC activity as the GSH/GSSG Ehbecame less negative ( n = 21; P < 0.05). Treatment of 400 μM GSSG to the cell bath significantly decreased ENaC Pofrom 0.39 ± 0.06 to 0.13 ± 0.05 ( n = 8; P < 0.05). Likewise, back-filling recording electrodes with 400 μM GSSG reduced ENaC Pofrom 0.32 ± 0.08 to 0.17 ± 0.05 ( n = 10; P < 0.05), thus implicating GSSG as an important regulatory factor. Biochemical assays indicated that oxidizing potentials promote S-glutathionylation of ENaC and irreversible oxidation of cysteine residues with N-ethylmaleimide blocked the effects of GSSG on ENaC Po. Additionally, real-time imaging studies showed that GSSG impairs alveolar fluid clearance in vivo as opposed to GSH, which did not impair clearance. Taken together, these data show that glutathione Ehis an important determinant of alveolar fluid clearance in vivo.

Motivation: MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs. Despite integrative analysis of matched gene and miRNA expression data can help identify cancer-associated miRNAs, such kind of data is not commonly available. On the other hand, there are huge amount of gene expression data that are publicly accessible. It will significantly improve the efficiency of characterizing miRNA’s function in cancer if we can identify cancer miRNAs directly from gene expression data. Results: We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data. The results on multiple cancer datasets show that our proposed method can effectively identify cancer-related miRNAs with higher precision compared with other popular approaches. Furthermore, some of our novel predictions are validated by both differentially expressed miRNAs and evidences from literature, implying the predictive power of our proposed method. In addition, we construct a cancer-miRNA-pathway network, which can help explain how miRNAs are involved in cancer. Availability and implementation: The R code and data files for the proposed method are available at http://comp-sysbio.org/miR_Path/ Contact: liukeq@gmail.com Supplementary information: supplementary data are available at Bioinformatics online.

Mendelian disorders are often caused by mutations in genes that are not lethal but induce functional distortions leading to diseases. Here we study the extent of gene duplicates that might compensate genes causing monogenic diseases. We provide evidence for pervasive functional redundancy of human monogenic disease genes (MDs) by duplicates by manifesting 1) genes involved in human genetic disorders are enriched in duplicates and 2) duplicated disease genes tend to have higher functional similarities with their closest paralogs in contrast to duplicated non-disease genes of similar age. We propose that functional compensation by duplication of genes masks the phenotypic effects of deleterious mutations and reduces the probability of purging the defective genes from the human population; this functional compensation could be further enhanced by higher purification selection between disease genes and their duplicates as well as their orthologous counterpart compared to non-disease genes. However, due to the intrinsic expression stochasticity among individuals, the deleterious mutations could still be present as genetic diseases in some subpopulations where the duplicate copies are expressed at low abundances. Consequently the defective genes are linked to genetic disorders while they continue propagating within the population. Our results provide insight into the molecular basis underlying the spreading of duplicated disease genes., Author Summary Duplicated genes, as opposed to singletons, are genes that have additional copies in a genome due to evolutionary mechanisms such as whole genome duplication, homologous recombination or retrotransposition events. Duplicates can have similar functions and thus mask the phenotypic consequences when one copy is mutated. Conversely, the corresponding phenotypes would manifest themselves when mutations occur in singletons, since functional compensation is rare among non-duplicated genes. It would thus be expected that the primary source of monogenic diseases, diseases caused by mutations within a single gene, is singletons. However, the opposite was found to be true. Additionally, we found the functional similarity of duplicated disease genes to be greater than that of duplicated non-disease genes of an equivalent duplication age. So how could the stronger functional compensation among duplicates increase their likelihood to associate with diseases? We propose that due to functional compensation in duplicates, disease-causing mutations are less likely to be removed from a human population in large scale since the phenotypes are masked; however, the functional compensation could be lost in a subpopulation, perhaps due to intrinsic variation in gene expression, and therefore lead to diseases. As a result, the duplicated disease genes are linked to genetic diseases, yet they continue to spread within the human population.

Motivation: Caspases and granzyme B (GrB) are important proteases involved in fundamental cellular processes and play essential roles in programmed cell death, necrosis and inflammation. Although a number of substrates for both types have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remained to be fully characterized. Accordingly, systematic bioinformatics studies of known cleavage sites may provide important insights into their substrate specificity and facilitate the discovery of novel substrates. Results: We develop a new bioinformatics tool, termed Cascleave 2.0, which builds on previous success of the Cascleave tool for predicting generic caspase cleavage sites. It can be efficiently used to predict potential caspase-specific cleavage sites for the human caspase-1, 3, 6, 7, 8 and GrB. In particular, we integrate heterogeneous sequence and protein functional information from various sources to improve the prediction accuracy of Cascleave 2.0. During classification, we use both maximum relevance minimum redundancy and forward feature selection techniques to quantify the relative contribution of each feature to prediction and thus remove redundant as well as irrelevant features. A systematic evaluation of Cascleave 2.0 using the benchmark data and comparison with other state-of-the-art tools using independent test data indicate that Cascleave 2.0 outperforms other tools on protease-specific cleavage site prediction of caspase-1, 3, 6, 7 and GrB. Cascleave 2.0 is anticipated to be used as a powerful tool for identifying novel substrates and cleavage sites of caspases and GrB and help understand the functional roles of these important proteases in human proteolytic cascades. Availability and implementation: http://www.structbioinfor.org/cascleave2/. Contact: Jiangning.Song@monash.edu or James.Whisstock@monash.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Background Type 1 diabetes (T1D) is a complex disease and harmful to human health, and most of the existing biomarkers are mainly to measure the disease phenotype after the disease onset (or drastic deterioration). Until now, there is no effective biomarker which can predict the upcoming disease (or pre-disease state) before disease onset or disease deterioration. Further, the detail molecular mechanism for such deterioration of the disease, e.g., driver genes or causal network of the disease, is still unclear. Methods In this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D. Results Two dynamical network biomarkers were obtained to signal the emergence of two critical deteriorations for the disease, and could be used to predict the upcoming sudden changes during the disease progression. We found that the two critical transitions led to peri-insulitis and hyperglycemia in NOD mices, which are consistent with other independent experimental results from literature. Conclusions The identified dynamical network biomarkers can be used to detect the early-warning signals of T1D and predict upcoming disease onset before the drastic deterioration. In addition, we also demonstrated that the leading biomolecular networks are causally related to the initiation and progression of T1D, and provided the biological insight into the molecular mechanism of T1D. Experimental data from literature and functional analysis on DNBs validated the computational results.

The identification of drug-target interactions is a crucial step in the drug-discovery process. It has been suggested that drug-target interactions are driven by drug-domain interactions. Based on the integration of two recently published datasets, i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was constructed. The functional similarity between domains based on their Gene Ontology (GO) annotations was estimated. The association between domains and therapeutic effects was investigated. Lists of GO annotations and Anatomical Therapeutic Chemical classification (ATC) codes highly enriched in My-DDome were identified. We show that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p < 0.000001). Top enriched GO terms include GO:0003824 (catalytic activity), GO:0008152 (metabolic process) and GO:0030170 (pyridoxal phosphate binding). By incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets and seemingly unrelated proteins are provided.

Prostate cancer is one of the most important public health problems in developed countries. To date, a systematic understanding of the pathogenesis of prostate cancer is still lacking. In this work, we identified differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) between normal and cancer tissues based on a recent RNA-seq study from Caucasian population. We then investigated the relationship between differentially expressed genes and lncRNAs. Furthermore, based on a recently published prostate cancer study on Chinese population, we identified differentially expressed genes between Caucasian and Chinese populations to investigate racial difference. Moreover, for the first time, we compared the correlation of lncRNA-gene across populations. In the end, a lot of differentially expressed genes and lncRNAs were identified. Our results revealed that most of the lncRNA-gene pairs were positively correlated especially for the lncRNA-host gene pairs, indicating the probable mechanism of lncRNA. And 320 genes were differentially expressed in prostate cancer across populations, which may help us to investigate the ethnic differences of prostate cancer. In addition, our results suggested that lncRNAs regulate genes in different manners across populations. Our findings may help understand molecular events underlying prostate cancer development.

Identifying dysregulation modules for complex diseases, such as B-cell lymphomas, can provide insights into the mechanisms of diseases and help to identify novel drug targets. In this work, based on molecular interaction network, we applied a network flow model to identify the dysregulation modules for three subtypes of non-Hodgkin's lymphomas, including Burkitt's lymphoma (BL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). In our identified dysregulation modules, there are multiple genes that were reported in literature to be related to B-cell lymphomas, which demonstrate that our presented method is really effective for identifying dysregulation modules related to diseases.

Background Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive. Results In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes. Conclusions The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.