Biology subjects

Saha, R.

Publications and source records attributed to Saha, R..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Hepatitis B virus x (HBx) protein increases cellular OCT3/4 and MYC and facilitates cellular reprogramming (of fibroblasts to iPSC)

HBx is a multifunctional protein coded by the Hepatitis B virus which is involved in various cellular processes such as proliferation, cell survival/apoptosis, and histone methylation. HBx was reported to be associated with liver cancer stem cells. The stemness inducing properties of HBx could also facilitate the generation of pluripotent stem cells from somatic cells. It is well established that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSC) using a cocktail of transcription factors called Yamanakas factors (OCT4, SOX2, KLF4, and MYC). The reprogramming process proceeds step-by-step with reprogramming factor chromatin interactions, transcription and chromatin states changing during transitions. HBx is a broad spectrum trans-activator and therefore could facilitate these transitions. We electroporated low passage and high passage (difficult to reprogram) fibroblasts using Yamanakas factors (YF) with and without HBx and evaluated the reprogramming efficiency. We also investigated the tri-lineage and terminal differentiation potential of iPSC derived using HBx. We found, addition of HBx to YF, improves iPSC derivation, and increases the efficiency of iPSC generation from difficult or hard-to-reprogram samples such as high passage/senescent fibroblasts. Furthermore, we show that HBx can substitute the key transcription factor MYC in the YF cocktail to generate iPSC. Our results have practical value in improving the efficiency of pluripotent stem cell derivation from difficult to reprogram somatic cells in addition to providing some insights into the mechanisms of liver carcinogenesis in chronic hepatitis B. To conclude, HBx improves the reprogramming efficiency of YFs. HBx increases the cellular levels of OCT3/4 and MYC. Lay summaryHBx is a protein made by hepatitis B virus. Yamanakas Factors (YF) are a mixture of master-proteins which are used to convert adult cells to embryonic stem cells. We found that HBx protein can augment the efficiency of YF in generating these stem cells (concept summary figure).

cell biology↗

bioRxiv2023

Multi-organ Metabolic Model of Zea mays Connects Temperature Stress with Thermodynamics-Reducing Power-Energy Generation Axis

Global climate change has severely impacted maize productivity. A holistic understanding of metabolic crosstalk among its organs is essential to address this issue. Thus, we reconstructed the first multi-organ maize genome-scale metabolic model, iZMA6517, and contextualized it with heat and cold stress-related transcriptomics data using the novel EXpression disTributed REAction flux Measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed fundamental differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. To tie these signatures, we performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, for global food security, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof of concept demonstrated its potential to alleviate temperature stress. In summary, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

plant biology↗

bioRxiv2023

Portable Arbitrary Pulse Generator for Driving μCoils for Micromagnetic Neurostimulation

Micromagnetic stimulation (MS) is a promising branch of neurostimulation technologies. Microcoil (coil) based magnetic stimulation uses micrometer sized coils that generate a time-varying magnetic field which as per Faradays Laws of Electromagnetic Induction induces an electric field on a conductive surface. This method of stimulation has the advantage of not requiring electrical contact with tissue, however these coils are not easy to operate. Large currents are required to generate the required magnetic field. These currents are too large for standard test equipment to provide, and additional power amplifiers are needed. To aid in the development and application of micromagnetic stimulation devices, we have created a compact single unit test setup for driving these devices called the Coil Driver. This unit is designed to drive small inductive loads up to {+/-}8 V at 5 A and 10 kHz.

neuroscience↗

bioRxiv2023

Micromagnetic Stimulation (μMS) Controls Dopamine Release: An in vivo Study Using WINCS Harmoni

ObjectiveResearch into the role of neurotransmitters in regulating normal and pathologic brain functions has made significant progress. Yet, clinical trials that aim to improve therapeutic interventions do not take advantage of the in vivo changes in the neurochemistry that occur in real time during disease progression, drug interactions or response to pharmacological, cognitive, behavioral, and neuromodulation therapies. In this work, we used the WINCS Harmoni tool to study the real time in vivo changes in dopamine release in rodent brains for the micromagnetic neuromodulation therapy. ApproachAlthough still in its infancy, micromagnetic stimulation (MS) using micro-meter sized coils or microcoils (coils) has shown incredible promise in spatially selective, galvanic contact free and highly focal neuromodulation. These coils are powered by a time-varying current which generates a magnetic field. As per Faradays Laws of Electromagnetic Induction, this magnetic field induces an electric field in a conducting medium (here, the brain tissues). We used a solenoidal-shaped coil to stimulate the medial forebrain bundle (MFB) of the rodent brain in vivo. The evoked in vivo dopamine releases in the striatum were tracked in real time by carbon fiber microelectrodes (CFM) using fast scan cyclic voltammetry (FSCV). ResultsOur experiments report that coils can successfully activate the MFB in rodent brains, triggering dopamine release in vivo. We further show that the successful release of dopamine upon micromagnetic stimulation is dependent on the orientation of the coil. Furthermore, varied intensities of MS can control the concentration of dopamine releases in the striatum. SignificanceThis work helps us better understand the brain and its conditions arising from a new therapeutic intervention, like MS, at the level of neurotransmitter release. Despite its early stage, this study potentially paves the path for MS to enter the clinical world as a precisely controlled and optimized neuromodulation therapy.

neuroscience↗

bioRxiv2023

Anthelmintic efficacy of Holarrhena pubescens against Raillietina spp. in domestic fowl through ultrastructural, histochemical, biochemical and GLCM analysis

Holarrhena pubescens is a well-known medicinal plant used in ethno-traditional medicine and tribal folklore medicine in West Bengal, India. This article focuses on depicting the anthelmintic efficacy of ethanolic extract and Ethyl acetate fraction of the stem bark of Holarrhena pubescens against the cestode Raillietina spp. through efficacy testing, ultra-structural observations, histochemical and biochemical studies on some tegumental enzymes i.e., Acid Phosphatase (AcPase), Alkaline Phosphatase (AlkPase), Adenosine Triphosphatase (ATPase) and 5-Nucleotidase (5-Nu) along with Gray Level Co-occurrence Matrix (GLCM) analysis of histochemistry images. Praziquantel was used as the reference drug. Investigations revealed 10mg/ml of crude extract was determined as the most efficacious dose and the Ethyl acetate fraction showed the most anthelmintic property. Ultrastructural studies through Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) clearly showed the damage in head, sucker, proglottids, proximal and distal cytoplasm (DC), microtriches (MT), basal lamina (BL), nuclear membrane (NM), and, nucleolus (NL) in the treated worms. Histochemical studies revealed decrease in staining intensity for all the tegumental enzymes in the treated worms compared to control. The GLCM analysis strongly supported the result of histochemical studies. Biochemical studies revealed marked reduction in enzyme activity in the treated worms with maximum reduction in the activity of 5-Nu (77.8%) followed by ATPase (63.17%).

zoology↗

bioRxiv2022

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among many existing methods and tools for analysis of brain anatomy based on structural magnetic resonance imaging (sMRI) data, data-driven source based morphometry (SBM) focuses on the exploratory detection of such patterns. Constrained source-based morphometry (constrained SBM) is a widely used semi-blind extension of SBM that enables extracting maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. In order to operate, constrained SBM needs the data to be locally accessible. However, there exist many reasons (e.g., the concerns of revealing identifiable rare disease information, or violating strict IRB policies) that may preclude access to data from different sites. In this scenario, constrained SBM fails to leverage the benefits of decentralized data. To mitigate this problem, we present a novel approach: decentralized constrained source-based morphometry (dcSBM). In dcSBM, the original data never leaves the local site. Each site operates constrained ICA on their private local data while using a common distributed computation platform. Then, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to find out the significant sources. In our approach, we first use UK Biobank sMRI data to investigate the reliability of our dcSBM algorithm. Finally, we utilize two additional multi-site patient datasets to validate our model by comparing the resulting group difference estimates from both centralized and decentralized constrained SBM.

neuroscience↗

bioRxiv2022

The multiple lncRNAs encoding hsrω gene is essential for oogenesis in Drosophila

In the background of limited studies on noncoding RNAs in Drosophila oogenesis, we show developmentally active hsr{omega} lncRNA gene to be essential in oogenesis and ovulation. The near-null hsr{omega}66 females, and ovaries with down- or up-regulated hsr{omega} display varyingly perturbed oogenesis including fewer ovarioles, high apoptosis, poor actin nuclear-cage (stage 10), low Cut levels in late chambers and, finally ovulation block. Restoration of normal oogenesis following targeted expression of hsr{omega}-RH transcript in hsr{omega}66 confirmed hsr{omega} mis-function to underlie these defects. Genetic interaction studies showed varying modulation of ovarian defects following mis-expression of Cut, and TBPH/TDP-43 or Caz/dFUS hnRNPs by altered hsr{omega} transcript levels. Dietary supplement of ecdysone to hsr{omega}66 females, which have reduced ecdysone titer, substantially restored normal oogenesis. Our results show for the first time that the multiple lncRNA producing hsr{omega} gene, which interacts with diverse hnRNPs and other regulatory molecules, As expected of a gene with key roles in dynamics of various hnRNPs, interactions between down or upregulated hsr{omega} transcripts and various oogenesis regulators are not linear. Summary StatementThe multiple lncRNA producing hsr{omega} gene critically impacts Drosophila oogenesis at multiple steps through intra- and inter-organ signaling.

developmental biology↗

bioRxiv2022

Micromagnetic Stimulation (μMS) Dose-Response of the Rat Sciatic Nerve

ObjectiveThe objective of this study was to investigate the effects of micromagnetic stimuli strength and frequency from the Magnetic Pen (MagPen) on the rat right sciatic nerve. The nerves response would be measured by recording muscle activity and movement of the right hind limb. ApproachThe MagPen was custom-built such that it can be held over the sciatic nerve in a stable manner. Rat leg muscle twitches were captured on video and movements were extracted using image processing algorithms. EMG recordings were also used to measure muscle activity. Main resultsThe MagPen prototype when driven by alternating current, generates time-varying magnetic field which as per Faradays Law of Electromagnetic Induction, induces an electric field for neuromodulation. The orientation dependent spatial contour maps for the induced electric field from the MagPen prototype has been numerically simulated. Furthermore, in this in vivo work on MS, a dose-response relationship has been reported by experimentally studying how the varying amplitude (Range: 25 mVp-p through 6 Vp-p) and frequency (Range: 100 Hz through 5 kHz) of the MagPen stimuli alters the hind limb movement. The primary highlight of this dose-response relationship is that at a higher frequency of the MS stimuli, significantly smaller amplitudes can trigger hind limb muscle twitch. This frequency-dependent activation can be justified following directly from the Faradays Law as the magnitude of the induced electric field is directly proportional to frequency. SignificanceThis work reports that MS can successfully activate the sciatic nerve in a dose-dependent manner. The MagPen probe, unlike electrodes, does not have a direct electrochemical interface with tissues rendering it much safer than an electrode. Magnetic fields create more precise activation than electrodes because they induce smaller volumes of activation. Finally, unique features of MS such as orientation dependence, directionality and spatial selectivity have been demonstrated.

bioengineering↗

bioRxiv2022

Characterizing the interplay of rubisco and nitrogenase enzymes in anaerobic-photoheterotrophically grown Rhodopseudomonas palustris CGA009 through a genome-scale metabolic and expression model

Rhodopseudomonas palustris CGA009 (R. palustris) is a gram negative purple non-sulfur bacteria that grows phototrophically or chemotrophically by fixing or catabolizing a wide array of substrates including lignin breakdown products (e.g., p-coumarate) for its carbon and nitrogen requirements. It can grow aerobically or anaerobically and can use light, inorganic, and organic compounds for energy production. Due to its ability to convert different carbon sources into useful products in anaerobic mode, this study, for the first time, reconstructed a metabolic and expression (ME-) model of R. palustris to investigate its anaerobic-photoheterotrophic growth. Unlike metabolic (M-) models, ME-models include transcription and translation reactions along with macromolecules synthesis and couple these reactions with growth rate. This unique feature of the ME-model led to nonlinear growth curve predictions which matched closely with experimental growth rate data. At the theoretical maximum growth rate, the ME-model suggested a diminishing rate of carbon fixation and predicted malate dehydrogenase and glycerol-3 phosphate dehydrogenase as alternate electron sinks. Moreover, the ME-model also identified ferredoxin as a key regulator in distributing electrons between major redox balancing pathways. Since ME-models include turnover rate for each metabolic reaction, it was used to successfully capture experimentally observed temperature regulation of different nitrogenases. Overall, these unique features of the ME-model demonstrated the influence of nitrogenases and rubiscos on R. palustris growth and predicted a key regulator in distributing electrons between major redox balancing pathways, thus establishing a platform for in silico investigation of R. palustris metabolism from a multi-omics perspective. IMPORTANCEIn this work, we reconstructed the first ME-model for a purple non-sulfur bacterium (PNSB). Using the ME-model, different aspects of R. palustris metabolism were examined. First, the ME-model was used to analyze how reducing power entering the R. palustris cell through organic carbon sources gets partitioned into biomass, carbon dioxide fixation, and nitrogen fixation. Furthermore, the ME-model predicted electron flux through ferredoxin as a major bottleneck in distributing electrons to nitrogenase enzymes. Next, the ME-model characterized different nitrogenase enzymes and successfully recapitulated experimentally observed temperature regulations of those enzymes. Identifying the bottleneck responsible for transferring electron to nitrogenase enzymes and recapitulating the temperature regulation of different nitrogenase enzymes can have profound implications in metabolic engineering, such as hydrogen production from R. palustris. Another interesting application of this ME-model can be to take advantage of its redox balancing strategy to gain understanding on regulatory mechanism of biodegradable plastic production precursors, such as polyhydroxybutyrate (PHB).

systems biology↗

bioRxiv2021

Strength-frequency curve for micromagnetic neurostimulation through EPSPs on rat hippocampal neurons and numerical modeling of magnetic microcoil (μcoil)

ObjectiveThe objective of this study was to measure the effect of micromagnetic stimulation (MS) on hippocampal neurons, by using single microcoil (coil) prototype, Magnetic Pen (MagPen). MagPen will be used to stimulate the CA3 region magnetically and excitatory post synaptic potential (EPSP) response measurements will be made from the CA1 region. The threshold for micromagnetic neurostimulation as a function of stimulation frequency of the current driving the coil will be demonstrated. Finally, the optimal stimulation frequency of the current driving the coil to minimize power will be estimated. ApproachA biocompatible, watertight, non-corrosive prototype, MagPen was built, and customized such that it is easy to adjust the orientation of the coil and its distance over the hippocampal tissue in an in vitro recording setting. Finite element modeling (FEM) of the coil design was performed to estimate the spatial profiles of the magnetic flux density (in T) and the induced electric fields (in V/m). The induced electric field profiles generated at different values of current applied to the coil can elicit a neuron response, which was validated by numerical modeling. The modeling settings for the coil were replicated in experiments on rat hippocampal neurons. Main resultsThe preferred orientation of MagPen over the Schaffer Collateral fibers was demonstrated such that they elicit a neuron response. The recorded EPSPs from CA1 region due to MS at CA3 region were validated by applying tetrodotoxin (TTX). Application of TTX to the hippocampal slice blocked the EPSPs from MS while after prolonged TTX washout, a partial recovery of the EPSP from MS was observed. Finally, it was interpreted through numerical analysis that increasing frequency of the current driving the coil, led to a decrease in the current amplitude threshold for micromagnetic neurostimulation. SignificanceThis work reports that micromagnetic neurostimulation can be used to evoke population EPSP responses in the CA1 region of the hippocampus. It demonstrates the strengthfrequency curve for MS and its unique features related to orientation dependence of the coils, spatial selectivity and stimulation threshold related to distance dependence. Finally, the challenges related to MS experiments were studied including ways to overcome them.

bioengineering↗

bioRxiv2021

Synthetic Biology Tool Development Advances Predictable Gene Expression in the Metabolically Versatile Soil Bacterium Rhodopseudomonas palustris

Harnessing the unique biochemical capabilities of non-model microorganisms would expand the array of biomanufacturing substrates, process conditions, and products. There are non-model microorganisms that fix nitrogen and carbon dioxide, derive energy from light, catabolize methane and lignin-derived aromatics, are tolerant to physiochemical stresses and harsh environmental conditions, store lipids in large quantities, and produce hydrogen. Model microorganisms often only break down simple sugars and require low stress conditions, but they have been engineered for the sustainable manufacture of numerous products, such as fragrances, pharmaceuticals, cosmetics, surfactants, and specialty chemicals, often by using tools from synthetic biology. Transferring complex pathways with all of the needed cofactors, energy sources, and cellular conditions from a non-model microorganism to a common chassis has proven to be exceedingly difficult. Utilization of unique biochemical capabilities could also be achieved by engineering the host; although, synthetic biology tools developed for model microbes often do not perform as designed in other microorganisms. The metabolically versatile Rhodopseudomonas palustris CGA009, a purple non-sulfur bacterium, catabolizes aromatic compounds derived from lignin in both aerobic and anaerobic conditions and can use light, inorganic, and organic compounds for its source of energy. R. palustris utilizes three nitrogenase isozymes to fulfill its nitrogen requirements while also generating hydrogen. Furthermore, the bacterium produces two forms of RuBisCo in response to carbon dioxide/bicarbonate availability. While this potential chassis harbors many beneficial traits, stable heterologous gene expression has been problematic due to its intrinsic resistance to many antibiotics and the lack of synthetic biology parts investigated in this microbe. To address these problems, we have characterized gene expression and plasmid maintenance for different selection markers, started a synthetic biology toolbox specifically for the photosynthetic R. palustris, including origins of replication, fluorescent reporters, terminators, and 5 untranslated regions, and employed the microbes endogenous plasmid for exogenous protein production. This work provides essential synthetic biology tools for engineering R. palustris many unique biochemical processes and has helped define the principles for expressing heterologous genes in this promising microbe through a methodology that could be applied to other non-model microorganisms.

bioengineering↗

bioRxiv2021

Insights into Early Word Comprehension: Tracking the Neural Representations of Word Semantics in Infants.

Infants develop foundational language skills and can understand simple words well before their first birthday. This developmental milestone has been studied primarily using looking time paradigms and Event-Related Potential (ERP) techniques, which provide evidence of word comprehension in the infant brain. While prior research validates the presence of semantic representations of words (word meaning) in infants, it does not tell us about the mental processes involved in retrieving these semantic representations or the content of the representations. To this end, we use a machine learning approach to predict the semantic representations of words using Electroencephalograms of infant brain activity. We explore semantic representations in two groups of infants (9-month-old and 12-month-old) and find significantly above chance decodability of overall word semantics, word animacy, and word phonetics. We compare decodability between the age groups and find significant differences later in the analysis window (around 700-900 ms after word onset), with higher decoding performance in 9-month-olds. Through our analysis, we also observe strong neural representations of word phonetics in the brain data for both age groups, some possibly correlated to word decoding accuracy and others not. Finally, we use the Temporal Generalization Method to show that neural representation of word semantics generalizes across the two infant age groups. Our results on word semantics, phonetics, and animacy provide insights into the evolution of neural representation of single word meaning in infants.

neuroscience↗

bioRxiv2021

Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling

Pancreatic ductal adenocarcinoma (PDAC) is a major research focus due to its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism efficiently to the environment to which they are exposed, often relying on diverse fuel sources depending on availability. Since traditional experimental techniques appear exhaustive in the search for a viable therapeutic strategy against PDAC, in this study, a highly curated and omics-informed genome-scale metabolic model of PDAC was reconstructed using patient-specific transcriptomic data. From the analysis of the model-predicted metabolic changes, several new metabolic functions were explored as potential therapeutic targets against PDAC in addition to the already known metabolic hallmarks of pancreatic cancer. Significant downregulation in the peroxisomal fatty acid beta oxidation pathway reactions, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were then correlated with potential drug combinations that can be repurposed for targeting genes with poor prognosis in PDAC. Overall, these studies provide a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies. Author summaryPancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, with late diagnosis, early metastasis, insufficient therapy response, and very low survival rates. Due to these challenges associated with the diagnosis and treatment of PDAC, it has been a research area of interest. With the goal of understanding the metabolic reprogramming in proliferating PDAC cells, we reconstructed healthy and PDAC models by incorporating patient transcriptomic data into a genome-scale global human metabolic model. Comparing the metabolic flux space for the reactions in the two context-specific models, we identified significantly divergent pathways in PDAC. These results allowed us to further investigate growth-limiting genes in PDAC and identify potential drug combinations that can be repositioned for treatment of PDAC.

systems biology↗

bioRxiv2021

Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N-) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. SummaryO_LIThe growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. C_LIO_LIA genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. C_LIO_LITranscriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. C_LIO_LIOverall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. C_LI

systems biology↗

bioRxiv2020

One step forward, two steps back: transcriptional advancements and fermentation phenomena in Actinobacillus succinogenes 130Z

Within the field of bioproduction, non-model organisms offer promise as bio-platform candidates. Non-model organisms can possess natural abilities to consume complex feedstocks, produce industrially useful chemicals, and withstand extreme environments that can be ideal for product extraction. However, non-model organisms also come with unique challenges due to lack of characterization. As a consequence, developing synthetic biology tools, predicting growth behavior, and building computational models can be difficult. There have been many advancements that have improved work with non-model organisms to address broad limitations, however each organism can come with unique surprises. Here we share our work in the non-model bacterium Actinobacillus succinognes 130Z, which includes both advancements in synthetic biology toolkit development and pitfalls in unpredictable fermentation behaviors. To develop a synthetic biology "tool kit" for A. succinogenes, information gleaned from a growth study and antibiotic screening was used to characterize 22 promoters which demonstrated a 260-fold range of fluorescence protein expression. The strongest of the promoters was incorporated into an inducible system for tunable gene control in A. succinogenes using the promoter for the lac operon as a template. This system flaunted a 481-fold range of expression and no significant basal expression. These findings were accompanied by unexpected changes in fermentation products characterized by a loss of succinic acid and increase in lactic acid after approximately 10 months in the lab. Contamination and mutation were ruled out as causes and further testing is needed to elucidate the driving factors. The significance of this work is to share tools developed in A. succinogenes while simultaneously serving as a cautionary tale. In sharing our findings, we seek to provide necessary information for further development of A. succinogenes as a platform for bioproduction of succinic acid. Additionally, we hope to illustrate the importance of diligent and long-term observation when working with non-model bacteria.

bioengineering↗

bioRxiv2020

Dissecting the regulatory roles of ORM proteins in the sphingolipid pathway of plants

Sphingolipids are a vital component of plant cellular endomembranes and carry out multiple functional and regulatory roles. Different sphingolipid species confer rigidity to the membrane structure, facilitate trafficking of secretory proteins, and initiate programmed cell death. Although the regulation of the sphingolipid pathway is yet to be uncovered, increasing evidence has pointed to orosomucoid proteins (ORMs) playing a major regulatory role and potentially interacting with a number of components in the pathway, including both enzymes and sphingolipids. However, experimental exploration of new regulatory interactions is time consuming and often infeasible. In this work, a computational approach was taken to address this challenge. A metabolic network of the sphingolipid pathway in plants was reconstructed. The steady-state rates of reactions in the network were then determined through measurements of growth and cellular composition of the different sphingolipids in Arabidopsis seedlings. The Ensemble modeling framework was modified to accurately account for activation mechanisms and subsequently used to generate sets of kinetic parameters that converge to the measured steady-state fluxes in a thermodynamically consistent manner. In addition, the framework was appended with an additional module to automate screening the parameters and to output models consistent with previously reported network responses to different perturbations. By analyzing the networks response in the presence of different combinations of regulatory mechanisms, the model captured the experimentally observed repressive effect of ORMs on SPT. Furthermore, predictions point to a second regulatory role of ORM proteins, namely as an activator of class II (or LOH1 and LOH3) ceramide synthases. This activating role was found to be modulated by the concentration of free ceramides, where an accumulation of these sphingolipid species dampened the activating effect of ORMs on ceramide synthase. The predictions pave the way for future guided experiments and have implications in engineering crops with higher biotic stress tolerance. Author summaryDue to their vital functional and regulatory roles in plant cells, increasing interest has gone into obtaining a complete understanding of the regulatory behavior of the sphingolipid pathway. However, the process of identifying new regulatory interactions is time consuming and often infeasible. To address this issue, ensemble modeling was used as an in silico method to test the ability of different regulatory schemes to predict all known pathway responses in a thermodynamically consistent manner. The analysis resulted in a significant reduction in the number of possible regulatory interactions. Mainly, the model predicts regulatory interactions between ceramides, ORMs, and ceramide synthases (especially class II). This framework can pave the way for biochemists to systematically identify plausible regulatory networks in understudied metabolic networks where knowledge on the underlying regulatory mechanisms is often missing. As future experimental works explore these predictions, an iterative cycle can begin wherein model predictions allow for targeted experiments which in turn generate results that can be reincorporated into the model to further increase prediction accuracy. Such a model-driven approach will significantly reduce the solution space traversed by the experimentalist.

systems biology↗

bioRxiv2020

In silico approach for designing of a multi-epitope based vaccine against novel Coronavirus (SARS-COV-2)

A novel Coronavirus (SARS-COV-2) has now become a global pandemic. Considering the severity of infection and the associated mortalities, there is an urgent need to develop an effective preventive measure against this virus. In this study, we have designed a novel vaccine construct using computational strategies. Spike (S) glycoprotein is the major antigenic component that trigger the host immune responses. Detailed investigation of S protein with various immunoinformatics tools enabled us to identify 5 MHC I and 5 MHC II B-cell derived T-cell epitopes with VaxiJen score > 1 and IC50 value < 100nM. These epitopes were joined with a suitable adjuvant and appropriate linkers to form a multi-epitope based vaccine construct. Further, in silico testing of the vaccine construct for its antigenicity, allergenicity, solubility, and other physicochemical properties showed it to be safe and immunogenic. Suitable tertiary structure of the vaccine protein was generated using 3Dpro of SCRATCH suite, refined with GalaxyRefine, and validated with ProSA, PROCHECK, and ERRAT server. Finally, molecular docking studies were performed to ensure a favorable binding affinity between the vaccine construct and TLR3 receptor. The designed multi-epitope vaccine showed potential to elicit specific immune responses against the SARS-COV-2. However, further wet lab validation is necessary to confirm the actual effectiveness, safety and immunogenic potency of the vaccine construct against derived in this study.

bioinformatics↗

bioRxiv2020

Investigation of microbial community interactions between lake Washington methanotrophs using genome-scale metabolic modeling

BackgroundThe role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. ResultsLake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. The major microbial members include methanotrophs such as Methylobacter Tundripaludum 21/22 and Methylomonas sp. LW13. In this work, these methanotrophs are studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The metabolic interactions for the community were also characterized. While both organisms are competitors for methane, Methylobacter was found to display altruistic behavior in consuming formaldehyde produced by Methylomonas that inhibits its growth. The community was next tested under carbon, oxygen, and nitrogen limited conditions to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variable differences within the methane oxidation pathway, serine cycle, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide from the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior of the community in synthetic and natural communities. The simulated community demonstrated a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in natural vs. synthetic condition. ConclusionIn this work, we attempted to reveal the response of a simplified methane recycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in natural community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues.

systems biology↗