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bioRxiv2018

Virus and CTL dynamics in the extra-follicular and follicular tissue compartments in SIV-infected macaques

Data from SIV-infected macaques indicate that virus-specific cytotoxic T lymphocytes (CTL) are mostly present in the extrafollicular (EF) compartment of the lymphoid tissue, with reduced homing to the follicular (F) site. This contributes to the majority of the virus being present in the follicle and represents a barrier to virus control. Using mathematical models, we investigate these dynamics. Two models are analyzed. The first assumes that CTL can only become stimulated and expand in the extra-follicular compartment, with migration accounting for the presence of CTL in the follicle. In the second model, follicular CTL can also undergo antigen-induced expansion. Consistent with experimental data, both models predict increased virus compartmentalization in the presence of stronger CTL responses and lower virus loads, and a more pronounced rise of extra-follicular compared to follicular virus during CD8 cell depletion experiments. The models, however, differ in other aspects. The follicular expansion model results in dynamics that promote the clearance of productive infection in the extrafollicular site, with any productively infected cells found being the result of immigration from the follicle. This is not observed in the model without follicular CTL expansion. The models further predict different consequences of introducing engineered, follicular-homing CTL, which has been proposed as a therapeutic means to improve virus control. Without follicular CTL expansion, this is predicted to result in a reduction of virus load in both compartments. The follicular CTL expansion model, however, makes the counter-intuitive prediction that addition of F-homing CTL not only results in a reduction of follicular virus load, but also in an increase in extrafollicular virus replication. These predictions remain to be experimentally tested, which will be relevant for distinguishing between models and for understanding how therapeutic introduction of F-homing CTL might impact the overall dynamics of the infection.

systems biology↗

bioRxiv2018

Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis

BackgroundAlthough several studies have provided insights into the role of long non-coding RNAs (lncRNAs), the majority of them has unknown function. Recent evidence has shown the importance of both lncR-NAs and chromatin interactions in transcriptional regulation. Although network-based methods, mainly exploiting gene-lncRNA co-expression, have been applied to characterize lncRNA of unknown function by means of guilt-by-association strategies, no method exists which combines co-expression analysis with 3D chromatin interaction data.\n\nResultsTo better understand the function of chromatin interactions in the context of lncRNA-mediated gene regulation, we have developed a multi-step graph analysis approach to examine the RNA polymerase II ChIA-PET chromatin interaction network in the K562 human cell line. We have annotated the network with gene and lncRNA coordinates, and chromatin states from the ENCODE project. We used centrality measures, as well as an adaptation of our previously developed Markov State Models (MSM) clustering method, to gain a better understanding of lncRNAs in transcriptional regulation. The novelty of our approach resides into the detection of fuzzy regulatory modules based on network properties and their optimization based on co-expression analysis between genes and gene-lncRNA pairs. This results in our method returning more bona fide regulatory modules than other state-of-the art approaches for clustering on graphs.\n\nConclusionsInterestingly, we find that lncRNA network hubs tend to be significantly enriched in disease association, positional conservation and enhancer-like functions. We validated regulatory functions for well known lncRNAs, such as MALAT1 and the enhancer-like lncRNA FALEC. In addition, by investigating the modular structure of bigger components we show that we can propose regulatory functional mechanisms for uncharacterized lncRNAs, such FLJ37453, RP11442N24 B.1 and LINC00910.

systems biology↗

bioRxiv2018

MD-AD: Multi-task deep learning for Alzheimer’s disease neuropathology

Systematic modeling of Alzheimers Disease (AD) neuropathology based on brain gene expression would provide valuable insights into the disease. However, relative scarcity and regional heterogeneity of brain gene expression and neuropathology datasets obscure the ability to robustly identify expression markers. We propose MD-AD (Multi-task Deep learning for AD) to effectively combine heterogeneous AD datasets by simultaneously modeling multiple phenotypes with shared layers. MD-AD leads to an 8% and 5% reduction in mean squared error over MLP for predicting counts of two AD hallmarks: plaques and tangles. It also leads to a 40% and 30% reduction in classification error over MLP for two common staging systems for AD: CERAD score and Braak stage. Additionally, MD-ADs network representation tends to better capture known metabolic pathways, including some AD-related pathways. Together, these results indicate that MD-AD is particularly useful for learning expressive network representations from heterogeneous and sparsely labeled AD data.

systems biology↗

bioRxiv2018

Effective Variations of related Physicochemical Properties of Nucleotides Leading to Amino Acids for Characterizing Genes and Proteins

The aim of this paper is to make quantitative analysis of the properties which is really being carried from DNA sequence and finally landing up to the properties of a protein structure through its primary protein sequence. Thus, the paper has a theory which is applicable for any arbitrary DNA sequence whether it is of various species or mutated data or a bunch of genes responsible for a function to be occurred. Irrespective to genes of any families, species, wild type or mutated, our paper here gives a standard model which defines a mapping between physicochemical properties of any arbitrary DNA sequence and physicochemical properties of its amino acid sequence. Experiments have been carried out with PPCA protein family and its four homologs PPC(B E) which establishes that DNA sequence keeps its signature even after its translation into the corresponding amino acid sequence.

systems biology↗

bioRxiv2018

The structure of the genetic code as an optimal graph clustering problem

The standard genetic code (SGC) is the set of rules by which genetic information is translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The questions about the origin and the main factor responsible for the present structure of the code are still under a hot debate. Various methodologies have been used to study the features of the code and assess the level of its potential optimality. Here, we introduced a new general approach to evaluate the quality of the genetic code structure. This methodology comes from graph theory and allows us to describe new properties of the genetic code in terms of conductance. This parameter measures the robustness of codon groups against the potential changes in translation of the protein-coding sequences generated by single nucleotide substitutions. We described the genetic code as a partition of an undirected and unweighted graph, which makes the model general and universal. Using this approach, we showed that the structure of the genetic code is a solution to the graph clustering problem. We presented and discussed the structure of the codes that are optimal according to the conductance. Despite the fact that the standard genetic code is far from being optimal according to the conductance, its structure is characterised by many codon groups reaching the minimum conductance for their size. The SGC represents most likely a local minimum in terms of errors occurring in protein-coding sequences and their translation.

systems biology↗

bioRxiv2018

Properties of cell signaling pathways and gene expression systems operating far from steady-state

Ligand-receptor systems, covalent modification cycles, and transcriptional networks are basic units of signaling systems and their steady-state properties are well understood. However, the behavior of such systems before steady-state is poorly characterized. Here, we analyzed the properties of the input-output curves for each of these systems as they approach steady-state. In ligand-receptor systems, the EC50 (concentration of the ligand that occupies 50% of the receptors) is higher before the system reaches steady-state. Based on this behavior, we have previously defined PRESS (for pre-equilibrium sensing and signaling), a general \"systems level\" mechanism cells may use to overcome input saturation. Originally, we showed that, given a step stimulation, PRESS operates when the kinetics of ligand-receptor binding are slower than the downstream signaling steps. Now, we show that, provided the input increases slowly, it is not essential for the ligand binding reaction itself to be slow. In addition, we demonstrate that covalent modification cycles and gene expression systems may also operate in PRESS mode. Thus, nearly all biochemical processes may operate in PRESS mode, suggesting that this mechanism may be ubiquitous in cell signaling systems.

systems biology↗

bioRxiv2018

A network-based model for drug repurposing in Rheumatoid Arthritis

BackgroundThe identification of drug repositioning targets through computational methods has the potential to provide a fast, inexpensive alternative to traditional drug discovery process. Diseases where complicated pathophysiology evolves over time are excellent targets for such methods. One such disease is Rheumatoid Arthritis (RA), a chronic inflammatory autoimmune disease, where the drug survival at 5-years is less than 50% for patients treated with disease-modifying anti-rheumatic drugs (DMARDs).\n\nMethods and FindingsWe have developed a network-based approach for drug repositioning that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, potential side-effects, genome-wide gene expression and disease modules that emerge through pertinent analysis. We found that all DMARDs, except for hydroxychloroquine (HCQ), were found to be significantly proximal to RA-related genes. Application of the method on anti-diabetic agents, statins and H2 receptor blockers identified anti-diabetic agents - gliclazide, sitagliptin and metformin - that have similar network signatures with the DMARDs. Subsequent in-vitro experiments on mouse fibroblast NIH-3T3 cells validated the findings and the down regulation of six key RA-related inflammatory genes. Our analysis further argues that the combination of HCQ and/or sulfasalazine with methotrexate (MTX) is predicted to have an additive synergistic effect in treatment based on network complementarity. Similarly, leflunomide and tofacitinib were found to be suitable alternatives upon chemoresistance to MTX-based double/triple therapy, given the complementary network signatures and overlapping critical target hubs.\n\nConclusionsOur results corroborate that computational methods that are based on network proximity, among other contextual information can help narrow down the drug candidates for drug repositioning, as well as support decisions for combinatorial drug treatment that is tailored to patients needs.\n\nAuthor summaryThe network-based proximity between drug targets and disease genes can provide novel insights on the repercussion, interplay, and reposition of drugs in the context of disease. Disease-modifying anti-rheumatic drugs (DMARDs) located significantly close to rheumatoid arthritis (RA)-associated genes and RA-relevant pathways. Three anti-diabetic agents were identified to have an anti-inflammatory effect like DMARDs. We built RA disease module encompassing the emerging small-molecule targets and functional neighbors, which better explained the RA pathophysiology. By proximity and network robustness, tofacitinib and tocilizumab were the most potent for RA disease module as a single agent, and this is consistent with clinical observation. Side effects of clinical importance are predictable by measuring network-based proximity between drug targets and side effect protein. Network-based drug-disease proximity offers a novel and clinically actionable information about drugs and opens the new possibility to drug combination and reposition.

systems biology↗

bioRxiv2018

Using composite phenotypes to reveal heterogeneity and model SpO2 of altitude acclimatization

Altitude acclimatization is the physiological process of the human body adjusting to the decreased availability of oxygen. Since several physiological processes are involved and the relation among them is complicated, analyses of single-traits is insufficient in revealing the complex mechanism of altitude acclimatization. In this study, we examined whether these physiological responses could be studied as composite phenotypes which are represented by a linear combination of physiological traits. We developed a strategy which combines both spectral clustering and PLSPM to define composite phenotypes. We captured 14 composite phenotypes from 28 physiological traits of altitude acclimatization. Using these composite phenotypes, we applied k-means to reveal hidden physiological heterogeneity in altitude acclimatization. Furthermore, we employed linear regression to systematically model oxygen saturation (SpO2) changes in altitude acclimatization and evaluated the model fitness performance. And composite phenotypes based Model 2 has better fitness than single-traits based Model 1 in all measurement indices. Therefore, this new strategy of defining and applying composite phenotypes can be considered as a general strategy of complex traits.

systems biology↗

bioRxiv2018

Online parallel accumulation - serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, while mass spectra are acquired in about 100 microseconds with time-of-fight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive due to its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 800,000 fragmentation spectra in standard 120 min LC runs are easily achievable, which can be used for near exhaustive precursor selection in complex mixtures or re-sequencing weak precursors. MaxQuant identified more than 6,400 proteins in single run HeLa analyses without matching to a library, and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,900 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.

systems biology↗

bioRxiv2018

Developing A Programmable, Self-Assembling Squash Leaf Curl China Virus (SLCCNV) Capsid Proteins Into \"Nano-Cargo\"-Like Architecture: A Next-Generation \"Nanotool\" For Biomedical Applications

A new era has begun in which pathogens have become useful scaffolds for nanotechnology applications. In this research/study, an attempt has been made to generate an empty cargo-like architecture from a high-profile plant pathogen of Squash leaf curl China virus (SLCCNV). In this approach, SLCCNV coat protein monomers are obtained efficiently by using a yeast Pichia pastoris expression system. Further, dialysis of purified SLCCNV-CP monomers against various pH strengthenened (5-10) disassembly and assembly buffers produced a self-assembled \"Nanocargo\"-like architecture, which also exhibited an ability to encapsulate the magnetic nanoparticles at in vitro. Bioinformatics tools were also utilized to predict the possible self-assembly kinetics and bioconjugation sites as well. The biocompatibility of \"SLCNNV-CP-Nanocargo\" particles was also evaluated by in vitro cancer cells, which eventually proved the particles to be versatile material for the next generation \"nanotool\" capable of housing various therapeutic or imaging agents.

systems biology↗

bioRxiv2018

Analysis of cell size homeostasis at the single-cell and population level

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

systems biology↗

bioRxiv2018

Dynamics and spatial genomics of the nascent transcriptome in single mESCs by intron seqFISH

Recent single cell experiments have revealed significant heterogeneities at the levels of transcription, DNA methylation and chromosome organization in individual cells. However, existing method of profiling mRNAs effectively averages transcriptional dynamics over many hours due to hours-long life time of most mRNAs. To capture the instantaneous activity of the transcriptome that reflects the rapid regulatory changes in cells, we imaged up to 10,421 nascent transcription active sites (TAS) in single mouse embryonic stem cells using seqFISH followed by multiple rounds of single molecule FISH and immunofluorescence. We observed that nascent transcription active sites appear to be distributed on the surface of individual chromosome territories and are dispersed throughout the nucleus. In addition, there are significant variability in the number of active transcription sites in single cells, representing globally more active to quiescent states. These states interconverted on the time scale of 2 hours as determined by a single cell pulse-chase experiment. Thus, transcriptome level seqFISH experiments provide an unprecedented spatial and dynamic view of chromosome organization and global nascent transcription activity in single cells.

systems biology↗

bioRxiv2018

Pinetree: a step-wise gene expression simulator with codon-specific translation rates

MotivationStochastic gene expression simulations often assume steady-state transcript levels, or they model transcription in more detail than translation. Moreover, they lack accessible programming interfaces, which limits their utility.\n\nResultsWe present Pinetree, a step-wise gene expression simulator with codon-specific translation rates. Pinetree models both transcription and translation in a stochastic framework with individual polymerase and ribosome-level detail. Written in C++ with a Python front-end, any user familiar with Python can specify a genome and simulate gene expression. Pinetree was designed to be efficient and scale to simulate large plasmids or viral genomes.\n\nAvailabilityPinetree is available on GitHub (https://github.com/benjaminjack/pinetree) and the Python Package Index (https://pypi.org/project/pinetree/).

systems biology↗

bioRxiv2018

Metaproteomics Reveals Potential Mechanisms by which Dietary Resistant Starch Supplementation Attenuates Chronic Kidney Disease Progression in Rats

BackgroundResistant starch is a prebiotic metabolized by the gut bacteria. It has been shown to attenuate chronic kidney disease (CKD) progression in rats. Previous studies employed taxonomic analysis using 16S rRNA sequencing and untargeted metabolomics profiling. Here we expand these studies by metaproteomics, gaining new insight into the host-microbiome interaction.\n\nMethodsDifferences between cecum contents in CKD rats fed a diet containing resistant starch with those fed a diet containing digestible starch were examined by comparative metaproteomics analysis. Taxonomic information was obtained using unique protein sequences. Our methodology results in quantitative data covering both host and bacterial proteins.\n\nResults5,834 proteins were quantified, with 947 proteins originating from the host organism. Taxonomic information derived from metaproteomics data surpassed previous 16S RNA analysis, and reached species resolutions for moderately abundant taxonomic groups. In particular, the Ruminococcaceae family becomes well resolved - with butyrate producers and amylolytic species such as R. bromii clearly visible and significantly higher while fibrolytic species such as R. flavefaciens are significantly lower with resistant starch feeding. The observed changes in protein patterns are consistent with fiber-associated improvement in CKD phenotype. Several known host CKD-associated proteins and biomarkers of impaired kidney function were significantly reduced with resistant starch supplementation. Data are available via ProteomeXchange with identifier PXD008845.\n\nConclusions- Metaproteomics analysis of cecum contents of CKD rats with and without resistant starch supplementation reveals changes within gut microbiota at unprecedented resolution, providing both functional and taxonomic information. Proteins and organisms differentially abundant with RS supplementation point toward a shift from mucin degraders to butyrate producers.

systems biology↗

bioRxiv2018

Pan-cancer analysis of RNA binding proteins (RBPs) reveals the involvement of the core pre-mRNA splicing and translation machinery in tumorigenesis

RNA binding proteins (RBPs) are key regulators of posttranscriptional processes such as RNA maturation, transport, localization, turnover and translation. Despite their dysregulation in various diseases including cancer, the landscape of RBP expression and regulatory mechanisms in human cancer has not been well characterized. Here, we analyzed mRNA expression of 1487 RBPs in ~6700 clinical samples across 16 human cancer types and found that there were significantly more upregulated RBPs than downregulated ones in tumors when compared to their adjacent normal tissues. Across almost all of the 16 cancer types, 109 RBPs were consistently upregulated (cuRBPs) while only 41 RBPs were consistently downregulated (cdRBPs). Integrating expression with the copy number and DNA methylation data, we found that the overexpression of cuRBPs is largely associated with the amplification of copy number, whereas the downregulation of cdRBPs may be a result of epigenetic silencing mediated by DNA methylation. Furthermore, our results indicated that cuRBPs could work together to promote cancer progression potentially through the involvement of splicing and translation machinery, while cdRBPs might function independently to suppress tumorigenesis. Additionally, we focused on colon cancer and identified several novel potential oncogenic RBPs, such as PABPC1L which might promote cancer development via regulating the core splicing machinery. In summary, we showed distinct expression landscapes, regulatory mechanisms and characteristics of cuRBPs and cdRBPs and implicated several novel RBPs in cancer pathogenesis. Moreover, our results suggest that the involvement of the core pre-mRNA splicing and translation machinery could be critical in tumorigenesis.

systems biology↗

bioRxiv2018

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

systems biology↗

bioRxiv2018

Multiple optimal phenotypes overcome redox and glycolytic intermediate metabolite imbalances in Escherichia coli pgi knockout evolutions

A mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory function of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth optimized Escherichia coli K-12 MG1655 strain. The knock-out (KO) strain exhibited an 80% drop in growth rate, that was largely recovered in eight replicate, but phenotypically distinct, cultures after undergoing adaptive laboratory evolution (ALE). Multi omic data sets showed that the loss of pgi substantially shifted pathway usage leading to a redox and sugar phosphate stress response. These stress responses were overcome by unique combinations of innovative mutations selected for by ALE. Thus, we show the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after loss of a major gene product.\n\nImportanceA mechanistic understanding of how new phenotypes develop to overcome the loss of a gene product provides valuable insight on both the metabolic and regulatory function of the lost gene. The pgi gene, whose product catalyzes the second step in glycolysis, was deleted in a growth optimized Escherichia coli K-12 MG1655 strain. Eight replicate adaptive laboratory evolution (ALE) resulted in eight phenotypically distinct endpoints that were able to overcome the gene loss. Utilizing multi-omics analysis, we show the coordinated mechanisms from genome to metabolome that lead to multiple optimal phenotypes after loss of a major gene product.

systems biology↗

bioRxiv2018

Modeling large fluctuations of thousands of clones during hematopoiesis: the role of stem cell self-renewal and bursty progenitor dynamics in rhesus macaque

In a recent clone-tracking experiment, millions of uniquely tagged hematopoietic stem cells (HSCs) were autologously transplanted into rhesus macaques and peripheral blood containing thousands of tags were sampled and sequenced over 14 years to quantify the abundance of hundreds to thousands of tags or \"clones.\" Two major puzzles of the data have been observed: consistent differences and massive temporal fluctuations of clone populations. The large sample-to-sample variability can lead clones to occasionally go \"extinct\" but \"resurrect\" themselves in subsequent samples. Although heterogeneity in HSC differentiation rates, potentially due to tagging, and random sampling of the animals blood and cellular demographic stochasticity might be invoked to explain these features, we show that random sampling cannot explain the magnitude of the temporal fluctuations. Moreover, we show through simpler neutral mechanistic and statistical models of hematopoiesis of tagged cells that a broad distribution in clone sizes can arise from stochastic HSC self-renewal instead of tag-induced heterogeneity. The very large clone population fluctuations that often lead to extinctions and resurrections can be naturally explained by a generation-limited proliferation constraint on the progenitor cells. This constraint leads to bursty cell population dynamics underlying the large temporal fluctuations. We analyzed experimental clone abundance data using a new statistic that counts clonal disappearances and provide least-squares estimates of two key model parameters in our model, the total HSC differentiation rate and the maximum number of progenitor-cell divisions.\n\nAuthor summaryHematopoiesis of virally tagged cells in rhesus macaques is analyzed in the context of a mechanistic and statistical model. We find that the clone size distribution and the temporal variability in the abundance of each clone (viral tag) in peripheral blood are consistent with (i) stochastic HSC self-renewal during bone marrow repair, (ii) clonal aging that restricts the number of generations of progenitor cells, and (iii) infrequent and small-size samples. By fitting data, we infer two key parameters that control the level of fluctuations of clone sizes in our model: the total HSC differentiation rate and the maximum proliferation capacity of progenitor cells. Our analysis provides insight into the mechanisms of hematopoiesis and a framework to guide future multiclone barcoding/lineage tracking measurements.

systems biology↗