Divergent Learning-Related Transcriptional States of Cortical Glutamatergic Neurons.

Experience-dependent gene expression reshapes neural circuits, permitting the learning of knowledge and skills. Most learning involves repetitive experiences during which neurons undergo multiple stages of functional and structural plasticity. Currently, the diversity of transcriptional responses underlying dynamic plasticity during repetition-based learning is poorly understood. To close this gap, we analyzed single-nucleus transcriptomes of L2/3 glutamatergic neurons of the primary motor cortex after 3 d motor skill training or home cage control in water-restricted male mice. "Train" and "control" neurons could be discriminated with high accuracy based on expression patterns of many genes, indicating that recent experience leaves a widespread transcriptional signature across L2/3 neurons. These discriminating genes exhibited divergent modes of coregulation, differentiating neurons into discrete clusters of transcriptional states. Several states showed gene expressions associated with activity-dependent plasticity. Some of these states were also prominent in the previously published reference, suggesting that they represent both spontaneous and task-related plasticity events. Markedly, however, two states were unique to our dataset. The first state, further enriched by motor training, showed gene expression suggestive of late-stage plasticity with repeated activation, which is suitable for expected emergent neuronal ensembles that stably retain motor learning. The second state, equally found in both train and control mice, showed elevated levels of metabolic pathways and norepinephrine sensitivity, suggesting a response to common experiences specific to our experimental conditions, such as water restriction or circadian rhythm. Together, we uncovered divergent transcriptional responses across L2/3 neurons, each potentially linked with distinct features of repetition-based motor learning such as plasticity, memory, and motivation.

Cell and tissue-specific glycosylation pathways informed by single-cell transcriptomics.

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, ~40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at ~200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

Single-cell transcriptional uncertainty landscape of cell differentiation.

Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.

Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.

When human cord blood-derived CD34+ cells are induced to differentiate, they undergo rapid and dynamic morphological and molecular transformations that are critical for fate commitment. In particular, the cells pass through a transitory phase known as "multilineage-primed" state. These cells are characterized by a mixed gene expression profile, different in each cell, with the coexpression of many genes characteristic for concurrent cell lineages. The aim of our study is to understand the mechanisms of the establishment and the exit from this transitory state. We investigated this issue using single-cell RNA sequencing and ATAC-seq. Two phases were detected. The first phase is a rapid and global chromatin decompaction that makes most of the gene promoters in the genome accessible for transcription. It results 24 h later in enhanced and pervasive transcription of the genome leading to the concomitant increase in the cell-to-cell variability of transcriptional profiles. The second phase is the exit from the multilineage-primed phase marked by a slow chromatin closure and a subsequent overall down-regulation of gene transcription. This process is selective and results in the emergence of coherent expression profiles corresponding to distinct cell subpopulations. The typical time scale of these events spans 48 to 72 h. These observations suggest that the nonspecificity of genome decompaction is the condition for the generation of a highly variable multilineage expression profile. The nonspecific phase is followed by specific regulatory actions that stabilize and maintain the activity of key genes, while the rest of the genome becomes repressed again by the chromatin recompaction. Thus, the initiation of differentiation is reminiscent of a constrained optimization process that associates the spontaneous generation of gene expression diversity to subsequent regulatory actions that maintain the activity of some genes, while the rest of the genome sinks back to the repressive closed chromatin state.

GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology.

MOTIVATION: The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways. RESULTS: We present GlycoEnzOnto, an ontology describing 403 human glycoEnzymes curated along 139 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching and capping/termination phases is described. IUPAC linear strings present systematic human/machine-readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses. AVAILABILITY AND IMPLEMENTATION: https://github.com/neel-lab/GlycoEnzOnto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deep learning for neural decoding in motor cortex.

Objective. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons.Approach. In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain.Main results. Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders.Significance. Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.

Metabolic adaptation of ovarian tumors in patients treated with an IDO1 inhibitor constrains antitumor immune responses.

To uncover underlying mechanisms associated with failure of indoleamine 2,3-dioxygenase 1 (IDO1) blockade in clinical trials, we conducted a pilot, window-of-opportunity clinical study in 17 patients with newly diagnosed advanced high-grade serous ovarian cancer before their standard tumor debulking surgery. Patients were treated with the IDO1 inhibitor epacadostat, and immunologic, transcriptomic, and metabolomic characterization of the tumor microenvironment was undertaken in baseline and posttreatment tumor biopsies. IDO1 inhibition resulted in efficient blockade of the kynurenine pathway of tryptophan degradation and was accompanied by a metabolic adaptation that shunted tryptophan catabolism toward the serotonin pathway. This resulted in elevated nicotinamide adenine dinucleotide (NAD+), which reduced T cell proliferation and function. Because NAD+ metabolites could be ligands for purinergic receptors, we investigated the impact of blocking purinergic receptors in the presence or absence of NAD+ on T cell proliferation and function in our mouse model. We demonstrated that A2a and A2b purinergic receptor antagonists, SCH58261 or PSB1115, respectively, rescued NAD+-mediated suppression of T cell proliferation and function. Combining IDO1 inhibition and A2a/A2b receptor blockade improved survival and boosted the antitumor immune signature in mice with IDO1 overexpressing ovarian cancer. These findings elucidate the downstream adaptive metabolic consequences of IDO1 blockade in ovarian cancers that may undermine antitumor T cell responses in the tumor microenvironment.

Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease.

Podocyte injury plays a crucial role in the progression of diabetic kidney disease (DKD). Injured podocytes demonstrate variations in nuclear shape and chromatin distribution. These morphometric changes have not yet been quantified in podocytes. Furthermore, the molecular mechanisms underlying these variations are poorly understood. Recent advances in omics have shed new lights into the biological mechanisms behind podocyte injury. However, there currently exists no study analyzing the biological mechanisms underlying podocyte morphometric variations during DKD. First, to study the importance of nuclear morphometrics, we performed morphometric quantification of podocyte nuclei from whole slide images of renal tissue sections obtained from murine models of DKD. Our results indicated that podocyte nuclear textural features demonstrate statistically significant difference in diabetic podocytes when compared to control. Additionally, the morphometric features demonstrated the existence of multiple subpopulations of podocytes suggesting a potential cause for their varying response to injury. Second, to study the underlying pathophysiology, we employed single cell RNA sequencing data from the murine models. Our results again indicated five subpopulations of podocytes in control and diabetic mouse models, validating the morphometrics-based results. Additionally, gene set enrichment analysis revealed epithelial to mesenchymal transition and apoptotic pathways in a subgroup of podocytes exclusive to diabetic mice, suggesting the molecular mechanism behind injury. Lastly, our results highlighted two distinct lineages of podocytes in control and diabetic cases suggesting a phenotypical change in podocytes during DKD. These results suggest that textural variations in podocyte nuclei may be key to understanding the pathophysiology behind podocyte injury.

FARCI: Fast and Robust Connectome Inference.

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling.

ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.

A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis.

Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. In this work, we performed data mining of TF-glycogene relationships from the Cistrome Cancer database (DB), which integrates chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq data to constitute regulatory relationships. In total, we observed 22,654 potentially significant TF-glycogene relationships, which include interactions involving 526 unique TFs and 341 glycogenes that span 29 the Cancer Genome Atlas (TCGA) cancer types. Here, TF-glycogene interactions appeared in clusters or so-called communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fisher's exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the article presents a computational approach to describe TF-glycogene relationships, the starting point for experimental system-wide validation.

Stochastic and Age-Dependent Proteostasis Decline Underlies Heterogeneity in Heat-Shock Response Dynamics.

Significant non-genetic stochastic factors affect aging, causing lifespan differences among individuals, even those sharing the same genetic and environmental background. In Caenorhabditis elegans, differences in heat-shock response (HSR) are predictive of lifespan. However, factors contributing to the heterogeneity of HSR are still not fully elucidated. Here, the authors characterized HSR dynamics in isogenic C. elegans expressing GFP reporter for hsp-16.2 for identifying the key contributors of HSR heterogeneity. Specifically, microfluidic devices that enable cross-sectional and longitudinal measurements of HSR dynamics in C. elegans at different scales are developed: in populations, within individuals, and in embryos. The authors adapted a mathematical model of HSR to single C. elegans and identified model parameters associated with proteostasis-maintenance of protein homeostasis-more specifically, protein turnover, as the major drivers of heterogeneity in HSR dynamics. It is verified that individuals with enhanced proteostasis fidelity in early adulthood live longer. The model-based comparative analysis of protein turnover in day-1 and day-2 adult C. elegans revealed a stochastic-onset of age-related proteostasis decline that increases the heterogeneity of HSR capacity. Finally, the analysis of C. elegans embryos showed higher HSR and proteostasis capacity than young adults and established transgenerational contribution to HSR heterogeneity that depends on maternal age.

Thermodynamic analysis of DNA hybridization signatures near mitochondrial DNA deletion breakpoints.

Broad evidence in the literature supports double-strand breaks (DSBs) as initiators of mitochondrial DNA (mtDNA) deletion mutations. While DNA misalignment during DSB repair is commonly proposed as the mechanism by which DSBs cause deletion mutations, details such as the specific DNA repair errors are still lacking. Here, we used DNA hybridization thermodynamics to infer the sequence lengths of mtDNA misalignments that are associated with mtDNA deletions. We gathered and analyzed 9,921 previously reported mtDNA deletion breakpoints in human, rhesus monkey, mouse, rat, and Caenorhabditis elegans. Our analysis shows that a large fraction of mtDNA breakpoint positions can be explained by the thermodynamics of short ≤ 5-nt misalignments. The significance of short DNA misalignments supports an important role for erroneous non-homologous and micro-homology-dependent DSB repair in mtDNA deletion formation. The consistency of the results of our analysis across species further suggests a shared mode of mtDNA deletion mutagenesis.

DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks.

BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .

CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis.

We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We illustrate the efficacy of CALISTA using single-cell gene expression datasets from different single-cell transcriptional profiling technologies and from a few hundreds to tens of thousands of cells. CALISTA is freely available on https://www.cabselab.com/calista.

Metabolic stress is a primary pathogenic event in transgenic Caenorhabditis elegans expressing pan-neuronal human amyloid beta.

Alzheimer's disease (AD) is the most common neurodegenerative disease affecting the elderly worldwide. Mitochondrial dysfunction has been proposed as a key event in the etiology of AD. We have previously modeled amyloid-beta (Aß)-induced mitochondrial dysfunction in a transgenic Caenorhabditis elegans strain by expressing human Aß peptide specifically in neurons (GRU102). Here, we focus on the deeper metabolic changes associated with this Aß-induced mitochondrial dysfunction. Integrating metabolomics, transcriptomics and computational modeling, we identify alterations in Tricarboxylic Acid (TCA) cycle metabolism following even low-level Aß expression. In particular, GRU102 showed reduced activity of a rate-limiting TCA cycle enzyme, alpha-ketoglutarate dehydrogenase. These defects were associated with elevation of protein carbonyl content specifically in mitochondria. Importantly, metabolic failure occurred before any significant increase in global protein aggregate was detectable. Treatment with an anti-diabetes drug, Metformin, reversed Aß-induced metabolic defects, reduced protein aggregation and normalized lifespan of GRU102. Our results point to metabolic dysfunction as an early and causative event in Aß-induced pathology and a promising target for intervention.

Constraints on Human CD34+ Cell Fate due to Lentiviral Vectors Can Be Relieved by Valproic Acid.

The initial stages following the in vitro cytokine stimulation of human cord blood CD34+ cells overlap with the period when lentiviral gene transfer is typically performed. Single-cell transcriptional profiling and time-lapse microscopy were used to investigate how the vector-cell crosstalk impacts on the fate decision process. The single-cell transcription profiles were analyzed using a new algorithm, and it is shown that lentiviral transduction during the early stages of stimulation modifies the dynamics of the fate choice process of the CD34+ cells. The cells transduced with a lentiviral vector are biased toward the common myeloid progenitor lineage. Valproic acid, a histone deacetylase inhibitor known to increase the grafting potential of the CD34+ cells, improves the transduction efficiency to almost 100%. The cells transduced in the presence of valproic acid can subsequently undergo normal fate commitment. The higher gene transfer efficiency did not alter the genomic integration profile of the vector. These observations open the way to substantially improving lentiviral gene transfer protocols.

Acoustic Compressibility of Caenorhabditis elegans.

The acoustic compressibility of Caenorhabditis elegans is a necessary parameter for further understanding the underlying physics of acoustic manipulation techniques of this widely used model organism in biological sciences. In this work, numerical simulations were combined with experimental trajectory velocimetry of L1 C. elegans larvae to estimate the acoustic compressibility of C. elegans. A method based on bulk acoustic wave acoustophoresis was used for trajectory velocimetry experiments in a microfluidic channel. The model-based data analysis took into account the different sizes and shapes of L1 C. elegans larvae (255 ± 26 µm in length and 15 ± 2 µm in diameter). Moreover, the top and bottom walls of the microfluidic channel were considered in the hydrodynamic drag coefficient calculations, for both the C. elegans and the calibration particles. The hydrodynamic interaction between the specimen and the channel walls was further minimized by acoustically levitating the C. elegans and the particles to the middle of the measurement channel. Our data suggest an acoustic compressibility κCe of 430 TPa-1 with an uncertainty range of ±20 TPa-1 for C. elegans, a much lower value than what was previously reported for adult C. elegans using static methods. Our estimated compressibility is consistent with the relative volume fraction of lipids and proteins that would mainly make up for the body of C. elegans. This work is a departing point for practical engineering and design criteria for integrated acoustofluidic devices for biological applications.

Clonal expansion of mitochondrial DNA deletions is a private mechanism of aging in long-lived animals.

Disruption of mitochondrial metabolism and loss of mitochondrial DNA (mtDNA) integrity are widely considered as evolutionarily conserved (public) mechanisms of aging (López-Otín et al., Cell, 153, 2013 and 1194). Human aging is associated with loss in skeletal muscle mass and function (Sarcopenia), contributing significantly to morbidity and mortality. Muscle aging is associated with loss of mtDNA integrity. In humans, clonally expanded mtDNA deletions colocalize with sites of fiber breakage and atrophy in skeletal muscle. mtDNA deletions may therefore play an important, possibly causal role in sarcopenia. The nematode Caenorhabditis elegans also exhibits age-dependent decline in mitochondrial function and a form of sarcopenia. However, it is unclear if mtDNA deletions play a role in C. elegans aging. Here, we report identification of 266 novel mtDNA deletions in aging nematodes. Analysis of the mtDNA mutation spectrum and quantification of mutation burden indicates that (a) mtDNA deletions in nematode are extremely rare, (b) there is no significant age-dependent increase in mtDNA deletions, and (c) there is little evidence for clonal expansion driving mtDNA deletion dynamics. Thus, mtDNA deletions are unlikely to drive the age-dependent functional decline commonly observed in C. elegans. Computational modeling of mtDNA dynamics in C. elegans indicates that the lifespan of short-lived animals such as C. elegans is likely too short to allow for significant clonal expansion of mtDNA deletions. Together, these findings suggest that clonal expansion of mtDNA deletions is likely a private mechanism of aging predominantly relevant in long-lived animals such as humans and rhesus monkey and possibly in rodents.

Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection.

Genome-wide transcriptional profiling provides a global view of cellular state and how this state changes under different treatments (e.g. drugs) or conditions (e.g. healthy and diseased). Here, we present ProTINA (Protein Target Inference by Network Analysis), a network perturbation analysis method for inferring protein targets of compounds from gene transcriptional profiles. ProTINA uses a dynamic model of the cell-type specific protein-gene transcriptional regulation to infer network perturbations from steady state and time-series differential gene expression profiles. A candidate protein target is scored based on the gene network's dysregulation, including enhancement and attenuation of transcriptional regulatory activity of the protein on its downstream genes, caused by drug treatments. For benchmark datasets from three drug treatment studies, ProTINA was able to provide highly accurate protein target predictions and to reveal the mechanism of action of compounds with high sensitivity and specificity. Further, an application of ProTINA to gene expression profiles of influenza A viral infection led to new insights of the early events in the infection.