Biology subjects

Gunawan, R.

Publications and source records attributed to Gunawan, R..

6 recordsLinked to original sources

bioRxiv2018

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

Disruption of mitochondrial metabolism and loss of mitochondrial DNA (mtDNA) integrity are widely considered as evolutionarily conserved (public) mechanisms of ageing (Lopez-Otin et al. 2013). Human ageing is associated with loss in skeletal muscle mass and function (Sarcopenia), contributing significantly to morbidity and mortality. Muscle ageing is associated with loss of mtDNA integrity. In humans, clonally expanded mtDNA deletions co-localize 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 ageing. Here we report identification of 266 novel mtDNA deletions in ageing nematodes. Analysis of the mtDNA mutation spectrum and quantification of mutation burden indicates that (1) mtDNA deletions in nematode is extremely rare, (2) there is no significant age-dependent increase in mtDNA deletions and (3) 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 modelling 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 ageing predominantly relevant in long-lived animals such as humans and rhesus monkey and possibly in rodents.

genetics↗

bioRxiv2018

CALISTA: Clustering And Lineage Inference in Single-Cell Transcriptional Analysis

We present CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis), 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 pseudotemporal cell ordering. 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 evaluated the performance of CALISTA by analyzing single-cell gene expression datasets from in silico simulations and various single-cell transcriptional profiling technologies, comprising a few hundreds to tens of thousands of cells. A comparison with existing single-cell expression analyses, including MONOCLE 2 and SCANPY, demonstrated the superiority of CALISTA in reconstructing cell lineage progression and ordering cells along cell differentiation paths. CALISTA is freely available on https://www.cabselab.com/calista.

bioinformatics↗

bioRxiv2018

Thermodynamic analysis of mitochondrial DNA breakpoints reveals mechanistic details of deletion mutagenesis

Broad evidence support double-strand breaks (DSBs) as initiators of mitochondrial DNA (mtDNA) deletion mutations. But the mechanism of DSB-induced deletions, including the DSB repair pathway(s) involved, remains to be established. Here, we used DNA hybridization thermodynamics to analyze misalignment lengths surrounding deletion breakpoints. Our analysis of 9,655 previously reported mammalian mtDNA deletions and 1,307 novel Caenorhabditis elegans mtDNA deletions, indicates a significant role of 0-25bp misalignments, supporting the role of erroneous non-homologous and micro-homology dependent DSB repair in deletion formation. Based on these insights we propose that DSB-induced mtDNA deletions occur via the misjoining of DSB ends and/or strand invasion of open mtDNA regions by DSB ends.

systems biology↗

bioRxiv2017

SALMON: inferring protein targets of compounds by network-based analysis of gene transcriptional profiles

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 networks 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.

bioinformatics↗

bioRxiv2017

Glycosylation Flux Analysis Reveals Dynamic Changes Of Intracellular Glycosylation Flux Distribution In Chinese Hamster Ovary Fed-Batch Cultures

N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this method to CHO fed-batch monoclonal antibody (mAb) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modelling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalyzed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the mAb production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, the fluxes of galactosylation decreased with the cultivation time in comparison to the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.

systems biology↗

bioRxiv2016

SINCERITIES: Inferring gene regulatory networks fromtime-stamped single cell transcriptional expression profiles

Recent advances in single cell transcriptional profiling open up a new avenue in studying the functional role of cell-to-cell variability in physiological processes such as stem cell differentiation. In this work, we developed a novel algorithm called SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS), for the inference of gene regulatory networks (GRNs) from single cell transcriptional expression data. In particular, we focused on time-stamped cross-sectional expression data, a common type of dataset generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers the regulatory (causal) relationships among genes by employing regularized linear regression, particularly ridge regression, using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using simulated time-stamped in silico single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell differentiation. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Meanwhile, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality.

systems biology↗