Single-cell multi-cohort dissection of the schizophrenia transcriptome.

The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

A multiresolution framework to characterize single-cell state landscapes.

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet's superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.

Cell Type-Specific Transcriptomics Reveals that Mutant Huntingtin Leads to Mitochondrial RNA Release and Neuronal Innate Immune Activation.

The mechanisms by which mutant huntingtin (mHTT) leads to neuronal cell death in Huntington's disease (HD) are not fully understood. To gain new molecular insights, we used single nuclear RNA sequencing (snRNA-seq) and translating ribosome affinity purification (TRAP) to conduct transcriptomic analyses of caudate/putamen (striatal) cell type-specific gene expression changes in human HD and mouse models of HD. In striatal spiny projection neurons, the most vulnerable cell type in HD, we observe a release of mitochondrial RNA (mtRNA) (a potent mitochondrial-derived innate immunogen) and a concomitant upregulation of innate immune signaling in spiny projection neurons. Further, we observe that the released mtRNAs can directly bind to the innate immune sensor protein kinase R (PKR). We highlight the importance of studying cell type-specific gene expression dysregulation in HD pathogenesis and reveal that the activation of innate immune signaling in the most vulnerable HD neurons provides a novel framework to understand the basis of mHTT toxicity and raises new therapeutic opportunities.

Apoptosis Induced by Prednisolone Occurs without Altering the Promoter Methylation of BAX and BCL-2 Genes in Acute Lymphoblastic Leukemia Cells CCRF-CEM.

OBJECTIVE: one of the main mechanisms in which cancer cells are resistant to chemotherapy drugs and therapeutic strategies is resistance to apoptosis due to these anticancer factors. Regulating the expression of genes through epigenetics, especially regulation through methylation, is one of the key aspects of regulating gene expression and the function of genes, which is also regulated by the pathways regulating the pathway of apoptosis. The epigenetic regulatory phenomenon in cancer cells can undergo a change in regulation and induces resistance to apoptosis against chemotherapy and anticancer factors. The purpose of the present scrutiny was defined to probe the effect of subtoxic prednisolone dose on the level of promoter methylation and gene expression of BAX and BCL2 in the CCRF-CEM cells. METHODS: The treated cells by prednisolone, cultured in RPMI 1640 medium in standard condition. Alteration in promoter DNA methylation was analyzed by use of methylation specific-PCR (MSP) technique after the defined intervened time of Prednisolone treatment with a subtoxic dose. RESULTS: Prednisolone can induce apoptosis via alteration in BAX and BCL2 genes, based on our previous scrutiny. This essay shows no varies in the Pattern of DNA methylation of examined genes; however, prednisolone changes the expression of examined genes. CONCLUSION: Lack of alteration through prednisolone treatment in DNA methylation template of BAX and BCL2 genes make this possible that Prednisolone affects apoptotic gene expression via different pathways, which need more research to be done about it.
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Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.

Genome-wide association studies (GWAS) have identified genetic variants associated with age-related macular degeneration (AMD), one of the leading causes of blindness in the elderly. However, it has been challenging to identify the cell types associated with AMD given the genetic complexity of the disease. Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the first single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures. Heterogeneity is observed within macroglia, suggesting that human retinal glia are more diverse than previously thought. Finally, GWAS-based enrichment analysis identifies glia, vascular cells, and cone photoreceptors to be associated with the risk of AMD. These data provide a detailed analysis of the human retina, and show how scRNA-seq can provide insight into cell types involved in complex, inflammatory genetic diseases.

Author Correction: Single-cell transcriptomic analysis of Alzheimer's disease.

Change history: In this Article, the Acknowledgements section should have included that the work was supported in part by the Cure Alzheimer's Fund (CAF), and the final NIH grant acknowledged should have been 'U01MH119509' instead of 'RF1AG054012'. In Supplementary Table 2, the column labels 'early.pathology.mean' and 'late.pathology.mean' were reversed in each worksheet (that is, columns Y and Z). These errors have been corrected online.

Single-cell transcriptomic analysis of Alzheimer's disease.

Alzheimer's disease is a pervasive neurodegenerative disorder, the molecular complexity of which remains poorly understood. Here, we analysed 80,660 single-nucleus transcriptomes from the prefrontal cortex of 48 individuals with varying degrees of Alzheimer's disease pathology. Across six major brain cell types, we identified transcriptionally distinct subpopulations, including those associated with pathology and characterized by regulators of myelination, inflammation, and neuron survival. The strongest disease-associated changes appeared early in pathological progression and were highly cell-type specific, whereas genes upregulated at late stages were common across cell types and primarily involved in the global stress response. Notably, we found that female cells were overrepresented in disease-associated subpopulations, and that transcriptional responses were substantially different between sexes in several cell types, including oligodendrocytes. Overall, myelination-related processes were recurrently perturbed in multiple cell types, suggesting that myelination has a key role in Alzheimer's disease pathophysiology. Our single-cell transcriptomic resource provides a blueprint for interrogating the molecular and cellular basis of Alzheimer's disease.

A geometric approach to characterize the functional identity of single cells.

Single-cell transcriptomic data has the potential to radically redefine our view of cell-type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma subtypes with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

Triangular Alignment (TAME): A Tensor-Based Approach for Higher-Order Network Alignment.

Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provides a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we identify a closely related surrogate function whose maximization results in a tensor eigenvector problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. Using a case study on the NAPAbench dataset, we show that triangular alignment is capable of producing mappings with high node correctness. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods in terms of conserved triangles. In addition, we show that the number of conserved triangles is more significantly correlated, compared to the conserved edge, with node correctness and co-expression of edges. Our formulation and resulting algorithms can be easily extended to arbitrary motifs.

A convex optimization approach for identification of human tissue-specific interactomes.

MOTIVATION: Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/tissue-selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes. RESULTS: We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (Edge Set Enrichment Analysis, Gene Ontology Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state-of-the-art techniques. Finally, using case studies of Alzheimer's and Parkinson's diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology and demonstrate the use of these pathways in identifying novel targets. AVAILABILITY AND IMPLEMENTATION: http://www.cs.purdue.edu/homes/mohammas/projects/ActPro.html CONTACT: mohammadi@purdue.edu.

Scope and limitations of yeast as a model organism for studying human tissue-specific pathways.

BACKGROUND: Budding yeast, S. cerevisiae, has been used extensively as a model organism for studying cellular processes in evolutionarily distant species, including humans. However, different human tissues, while inheriting a similar genetic code, exhibit distinct anatomical and physiological properties. Specific biochemical processes and associated biomolecules that differentiate various tissues are not completely understood, neither is the extent to which a unicellular organism, such as yeast, can be used to model these processes within each tissue. RESULTS: We present a novel framework to systematically quantify the suitability of yeast as a model organism for different human tissues. To this end, we develop a computational method for dissecting the global human interactome into tissue-specific cellular networks. By individually aligning these networks with the yeast interactome, we simultaneously partition the functional space of human genes, and their corresponding pathways, based on their conservation both across species and among different tissues. Finally, we couple our framework with a novel statistical model to assess the conservation of tissue-specific pathways and infer the overall similarity of each tissue with yeast. We further study each of these subspaces in detail, and shed light on their unique biological roles in the human tissues. CONCLUSIONS: Our framework provides a novel tool that can be used to assess the suitability of the yeast model for studying tissue-specific physiology and pathophysiology in humans. Many complex disorders are driven by a coupling of housekeeping (universally expressed in all tissues) and tissue-selective (expressed only in specific tissues) dysregulated pathways. While tissue-selective genes are significantly associated with the onset and development of a number of tissue-specific pathologies, we show that the human-specific subset has even higher association. Consequently, they provide excellent candidates as drug targets for therapeutic interventions.

miRNAs: a new method for erythroid differentiation of hematopoietic stem cells without the presence of growth factors.

Micro RNAs (miRNAs) are a novel class of non-coding regulatory RNA molecules that contribute to post-transcriptional gene regulation. Recent studies have demonstrated that specific miRNAs such as miR-150, miR-154, and miR-451 have key roles in erythropoiesis. To date, stimulatory cytokines are considered as unique effectors for in vitro differentiation of HSCs to erythropoietic lineage. However, the use of these factors is not cost-effective for clinical applications and therapeutic strategies. Here, we present a novel and cost-effective strategy in which miRNAs expression modulation promotes erythroid differentiation in HSCs in the absence of any extrinsic factors. Thus, CD133(+) hematopoietic stem cells purified from human umbilical cord blood were treated with pre-miR-451 containing lentiviruses, anti-miR-150 and anti-miR-154 in the absence of growth factors and cytokines. Obtained results indicated that miR-451 upregulation and miR-150 downregulation have positive effect on GATA-1, FOG-1, and EKLF, CD71 and CD235a genes expression and induce hemoglobinization efficiently. However, downregulation of miR-154 had no effect on erythropoiesis indexes compared to that observed in the control group. In conclusion, the data presented here for the first time demonstrate that expression modulation of miR-451 and miR-150 could be an efficient alternative to stimulatory cytokines for CD133(+) differentiation into erythroid lineage. Modulation of erythropoiesis in stem cells via miRNA holds promising potential for vascular tissue engineering and regenerative medicine applications.

Inferring the effective TOR-dependent network: a computational study in yeast.

BACKGROUND: Calorie restriction (CR) is one of the most conserved non-genetic interventions that extends healthspan in evolutionarily distant species, ranging from yeast to mammals. The target of rapamycin (TOR) has been shown to play a key role in mediating healthspan extension in response to CR by integrating different signals that monitor nutrient-availability and orchestrating various components of cellular machinery in response. Both genetic and pharmacological interventions that inhibit the TOR pathway exhibit a similar phenotype, which is not further amplified by CR. RESULTS: In this paper, we present the first comprehensive, computationally derived map of TOR downstream effectors, with the objective of discovering key lifespan mediators, their crosstalk, and high-level organization. We adopt a systematic approach for tracing information flow from the TOR complex and use it to identify relevant signaling elements. By constructing a high-level functional map of TOR downstream effectors, we show that our approach is not only capable of recapturing previously known pathways, but also suggests potential targets for future studies.Information flow scores provide an aggregate ranking of relevance of proteins with respect to the TOR signaling pathway. These rankings must be normalized for degree bias, appropriately interpreted, and mapped to associated roles in pathways. We propose a novel statistical framework for integrating information flow scores, the set of differentially expressed genes in response to rapamycin treatment, and the transcriptional regulatory network. We use this framework to identify the most relevant transcription factors in mediating the observed transcriptional response, and to construct the effective response network of the TOR pathway. This network is hypothesized to mediate life-span extension in response to TOR inhibition. CONCLUSIONS: Our approach, unlike experimental methods, is not limited to specific aspects of cellular response. Rather, it predicts transcriptional changes and post-translational modifications in response to TOR inhibition. The constructed effective response network greatly enhances understanding of the mechanisms underlying the aging process and helps in identifying new targets for further investigation of anti-aging regimes. It also allows us to identify potential network biomarkers for diagnosis and prognosis of age-related pathologies.

Neuropathological and neuroprotective features of vitamin B12 on the dorsal spinal ganglion of rats after the experimental crush of sciatic nerve: an experimental study.

BACKGROUND: Spinal motoneuron neuroprotection by vitamin B12 was previously reported; the present study was carried out to evaluate neuroprotectivity in the dorsal root ganglion sensory neuron. METHODS: In present study thirty-six Wister-Albino rats (aged 8-9 weeks and weighing 200-250 g) were tested. The animals were randomly divided into 6 groups which every group contained 6 rats. Group A: received normal saline (for 42 days); Group B: vitamin B12 was administered (0.5 mg/kg/day for 21 days); Group C: received vitamin B12 (1 mg/kg/day for 21 days); Group D: received vitamin B12 (0.5 mg/kg/day for 42 days); Group E; received vitamin B12 (1 mg/kg/day for 42 days); Group F; received no treatment. The L5 Dorsal Root Ganglion (DRG) neurons count compared to the number of left and right neurons .Furthermore, DRG sensory neurons for regeneration were evaluated 21 or 42 days after injury (each group was analyzed by One-Way ANOVA test). RESULTS: (1): The comparison of left crushed neurons (LCN) number with right non-crushed neurons in all experimental groups (B, C, D and C), indicating a significant decline in their neurons enumeration (p<0/05). (2): The comparison of test group's LCN with the control group's LCN revealed a significant rise in the number of experimental group neurons (p<0/05). (3): Moreover, comparing the number of right neurons in experimental groups with the number of neurons in crushed neurons indicated that the average number of right neurons showed a significant increase in experimental groups (p<0/05). CONCLUSION: Consequently, the probability of nerve regeneration will be increased by the increment of the administered drug dosage and duration. On the other hand, the regeneration and healing in Dorsal Spinal Ganglion will be improved by increase of administration time and vitamin B12 dose, indicating that such vitamin was able to progress recovery process of peripheral nerves damage in experimental rats. Finally, our results have important implications for elucidating the mechanisms of nerve regeneration. Moreover, the results showed that vitamin B12 had a proliferative effect on the dorsal root ganglion sensory neuron. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/7395141841009256.

A fast approach to global alignment of protein-protein interaction networks.

BACKGROUND: Global network alignment has been proposed as an effective tool for computing functional orthology. Commonly used global alignment techniques such as IsoRank rely on a two-step process: the first step is an iterative diffusion-based approach for assigning similarity scores to all possible node pairs (matchings); the second step applies a maximum-weight bipartite matching algorithm to this similarity score matrix to identify orthologous node pairs. While demonstrably successful in identifying orthologies beyond those based on sequences, this two-step process is computationally expensive. Recent work on computation of node-pair similarity matrices has demonstrated that the computational cost of the first step can be significantly reduced. The use of these accelerated methods renders the bipartite matching step as the dominant computational cost. This motivates a critical assessment of the tradeoffs of computational cost and solution quality (matching quality, topological matches, and biological significance) associated with the bipartite matching step. In this paper we utilize the state-of-the-art core diffusion-based step in IsoRank for similarity matrix computation, and couple it with two heuristic bipartite matching algorithms - a matrix-based greedy approach, and a tunable, adaptive, auction-based matching algorithm developed by us. We then compare our implementations against the performance and quality characteristics of the solution produced by the reference IsoRank binary, which also implements an optimal matching algorithm. RESULTS: Using heuristic matching algorithms in the IsoRank pipeline exhibits dramatic speedup improvements; typically ×30 times faster for the total alignment process in most cases of interest. More surprisingly, these improvements in compute times are typically accompanied by better or comparable topological and biological quality for the network alignments generated. These measures are quantified by the number of conserved edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms. CONCLUSIONS: We have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure. Our heuristic matching techniques maintain comparable - if not better - quality in resulting alignments. A consequence of our work is that network-alignment based orthologies can be computed within minutes (as compared to hours) on typical protein interaction networks, enabling a more comprehensive tuning of alignment parameters for refined orthologies.

Role of synthetic genetic interactions in understanding functional interactions among pathways.

Synthetic genetic interactions reveal buffering mechanisms in the cell against genetic perturbations. These interactions have been widely used by researchers to predict functional similarity of gene pairs. In this paper, we perform a comprehensive evaluation of various methods for predicting co-pathway membership of genes based on their neighborhood similarity in the genetic network. We clearly delineate the scope of these methods and use it to motivate a rigorous statistical framework for quantifying the contribution of each pathway to the functional similarity of gene pairs. We then use our model to infer interdependencies among KEGG pathways. The resulting KEGG crosstalk map yields significant insights into the high-level organization of the genetic network and is used to explain the effective scope of genetic interactions for predicting co-pathway membership of gene pairs. A direct byproduct of this effort is that we are able to identify subsets of genes in each pathway that act as 'ports' for interaction across pathways.

Kavosh: a new algorithm for finding network motifs.

BACKGROUND: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. RESULTS: We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network. CONCLUSION: The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.