bMycobacteriophages infect mycobacteria, resulting in their death. Therefore, the possibility of using them as therapeutic agents against the deadly mycobacterial disease tuberculosis (TB) is of great interest. To obtain better insight into the dynamics of mycobacterial inactivation by mycobacteriophages, this study was initiated using mycobacteriophage D29 and Mycobacterium smegmatis as the phage-host system. Here, we implemented a goal-oriented iterative cycle of experiments on one hand and mathematical modeling combined with Monte Carlo simulations on the other. This integrative approach lends valuable insight into the detailed kinetics of bacterium-phage interactions. We measured time-dependent changes in host viability during the growth of phage D29 in M. smegmatis at different multiplicities of infection (MOI). The predictions emerging out of theoretical analyses were further examined using biochemical and cell biological assays. In a phage-host interaction system where multiple rounds of infection are allowed to take place, cell counts drop more rapidly than expected if cell lysis is considered the only mechanism for cell death. The phenomenon could be explained by considering a secondary factor for cell death in addition to lysis. Further investigations reveal that phage infection leads to the increased production of superoxide radicals, which appears to be the secondary factor. Therefore, mycobacteriophage D29 can function as an effective antimycobacterial agent, the killing potential of which may be amplified through secondary mechanisms.

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SUMMARYIntercellular (between-cells) signals must be converted into an intracellular (within-cell) signal before it can trigger a proportionate response. How cells mount such proportionate responses within their interior remains unknown. Here we unravel the role of a coupled GTPase circuit on the Golgi membranes which enables the intracellular secretory machinery to respond proportionately to the growth factors in the extracellular space. The circuit, comprised of two species of biological switches, the Ras-superfamily monomeric GTPase Arf1, and the heterotrimeric GTPase, Giαβγ and their corresponding GAPs and GEFs, is coupled via at least one a forward and two key negative feedback loops. Interrogation of the circuit featuring such closed-loop control (CLC) using an integrated systems-based and experimental approach showed that CLC allows the two GTPases to mutually control each other and convert the expected switch-like behavior of Arf1 into an unexpected dose response aligned (DoRA) linear behavior. Such behavior translates into growth factor stimulated Giαβγ activity on Golgi membranes, temporal finiteness of Arf1 activity, and cellular secretion that is proportional to the stimuli. Findings reveal the importance of the coupled GTPase circuit in rendering concordant cellular responses via the faithful transmission of growth signals to the secretory machinery.GRAPHIC ABSTRACTHIGHLIGHTSEndo- (mono) and ectomembrane (trimeric) GTPase systems are believed to function independently.Their coupling in a closed loop system at the Golgi makes cell secretion proportionate to stimuli.Coupling enables closed-loop mutual control of both GTPases and dose response alignment (DoRA).Uncoupling creates an open loop which generates misaligned and discordant responses.

STRUCTURED ABSTRACTBackgroundIn the aftermath of Covid-19, a long-haul form of mysterious and progressive fibrotic lung disease has emerged, i.e., post-COVID-19 lung disease (PCLD), for which we currently lack insights into pathogenesis, disease models, or treatment options.MethodUsing an AI-guided approach, we analyzed > 1000 human lung transcriptomic datasets associated with various lung conditions using two viral pandemic (ViP and sViP) and one covid lung gene signatures. Upon identifying similarities between COVID-19 and idiopathic pulmonary fibrosis (IPF), we subsequently dissected the basis for such similarity from molecular, cytopathic, and immunologic perspectives using a panel of IPF-specific gene signatures, alongside signatures of alveolar type II (AT2) cytopathies and of prognostic monocyte-driven processes that are known drivers of IPF. To pinpoint the AT2 processes that are shared points of convergence between COVID-19 and IPF, transcriptome-derived findings were used to construct protein – protein interaction (PPI) network. Key findings were validated in hamster and human adult lung organoid (ALO) pre-clinical models of COVID-19 using immunohistochemistry and qPCR.FindingsWe found that COVID-19 resembles IPF at a fundamental level; it recapitulates the gene expression patterns (ViP and IPF signatures), cytokine storm (IL15-centric) and the AT2 cytopathic changes, e.g., injury, DNA damage, arrest in a transient, damage-induced progenitor state, and senescence-associated secretory phenotype (SASP). These immunocytopathic features were induced in pre-clinical COVID models (ALO and hamster) and reversed with effective anti-CoV-2 therapeutics in hamsters. PPI-network analyses pinpointed ER stress as one of the shared early triggers of both diseases, and IHC studies validated the same in the lungs of deceased subjects with COVID-19 and SARS-CoV-2-challenged hamster lungs. Lungs from tg-mice, in which ER stress is induced specifically in the AT2 cells, faithfully recapitulate the host immune response and alveolar cytopathic changes that are induced by SARS-CoV-2.InterpretationLike IPF, COVID-19 may be driven by injury-induced ER stress that culminates into progenitor state arrest and SASP in AT2 cells. The ViP signatures in monocytes may be key determinants of prognosis. The insights, signatures, disease models identified here are likely to spur the development of therapies for patients with IPF and other fibrotic interstitial lung disease.FundingThis work was supported by the National Institutes for Health grants R01-GM138385 and AI155696 and funding from the Tobacco-Related disease Research Program (R01RG3780).One Sentence SummarySevere COVID-19 triggers cellular processes seen in fibrosing Interstitial Lung DiseasePANEL: RESEARCH IN CONTEXTEvidence before this studyIn its aftermath, the COVID-19 pandemic has left many survivors, almost a third of those who recovered, with a mysterious long-haul form of the disease which culminates in a fibrotic form of interstitial lung disease (post-COVID-19 ILD). Post-COVID-19 ILD remains a largely unknown entity. Currently we lack insights into the core cytopathic features that drives this condition.Added value of this studyUsing an AI-guided approach, which involves the use of a sets of gene signatures, protein-protein network analysis, and a hamster model of COVID-19, we have revealed here that COVID-19 -lung fibrosis resembles IPF, the most common form of ILD, at a fundamental level—showing similar gene expression patterns in the lungs and blood, and dysfunctional AT2 processes (ER stress, telomere instability, progenitor cell arrest and senescence). These findings are insightful because AT2 cells are known to contain an elegant quality control network to respond to intrinsic or extrinsic stress; a failure of such quality control results in diverse cellular phenotypes, of which ER stress appears to be a point of convergence, which appears to be sufficient to drive downstream fibrotic remodeling in the lung.Implications of all the available evidenceBecause unbiased computational methods identified the shared fundamental aspects of gene expression and cellular processes between COVID-19 and IPF, the impact of our findings is likely to go beyond COVID-19 or any viral pandemic. The insights, tools (disease models, gene signatures, and biomarkers), and mechanisms identified here are likely to spur the development of therapies for patients with IPF and other fibrotic interstitial lung disease, all of whom have limited or no treatment options. to dissect the validate prognostic biomarkers to assess and track the risk of pulmonary fibrosis and develop therapeutics to halt fibrogenic progression.

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Motivation A rigorous yet general mathematical approach to mutagenesis, especially one capable of delivering systems-level perspectives would be invaluable. Such systems-level understanding of phage resistance is also highly desirable for phage-bacteria interactions and phage therapy research. Independently, the ability to distinguish between two graphs with a set of common or identical nodes and identify the implications thereof, is important in network science. Results Herein we propose a measure called shortest path alteration fraction (SPAF) to compare any two networks by shortest paths, using sets. When SPAF is one, it can identify node pairs connected by at least one shortest path, which are present in either network but not both. Similarly, SPAF equaling zero identifies identical shortest paths, which are simultaneously present between a node pair in both networks. We study the utility of our measure theoretically in five diverse microbial species, to capture reported effects of well-studied mutations and predict new ones. We also scrutinise the effectiveness of our procedure through theoretical and experimental tests on Mycobacterium smegmatis mc2155 and by generating a mutant of mc2155, which is resistant to mycobacteriophage D29. This mutant of mc2155, which is resistant to D29 exhibits significant phenotypic alterations. Whole-genome sequencing identifies mutations, which cannot readily explain the observed phenotypes. Exhaustive analyses of protein-protein interaction network of the mutant and wild-type, using the machinery of topological metrics and differential networks does not yield a clear picture. However, SPAF coherently identifies pairs of proteins at the end of a subset of shortest paths, from amongst hundreds of thousands of viable shortest paths in the networks. The altered functions associated with the protein pairs are strongly correlated with the observed phenotypes.

Cancers represent complex autonomous systems, displaying self‐sufficiency in growth signaling. Autonomous growth is fueled by a cancer cell's ability to “secrete‐and‐sense” growth factors (GFs): a poorly understood phenomenon. Using an integrated computational and experimental approach, here we dissect the impact of a feedback‐coupled GTPase circuit within the secretory pathway that imparts secretion‐coupled autonomy. The circuit is assembled when the Ras‐superfamily monomeric GTPase Arf1, and the heterotrimeric GTPase Giαβγ and their corresponding GAPs and GEFs are coupled by GIV/Girdin, a protein that is known to fuel aggressive traits in diverse cancers. One forward and two key negative feedback loops within the circuit create closed‐loop control, allow the two GTPases to coregulate each other, and convert the expected switch‐like behavior of Arf1‐dependent secretion into an unexpected dose–response alignment behavior of sensing and secretion. Such behavior translates into cell survival that is self‐sustained by stimulus‐proportionate secretion. Proteomic studies and protein–protein interaction network analyses pinpoint GFs (e.g., the epidermal GF) as key stimuli for such self‐sustenance. Findings highlight how the enhanced coupling of two biological switches in cancer cells is critical for multiscale feedback control to achieve secretion‐coupled autonomy of growth factors.

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.