Biology subjects

Ahmad, S.

Publications and source records attributed to Ahmad, S..

98 records · Page 6Linked to original sources

bioRxiv2018

Prokaryotic Diversity from Extreme Environments of Pakistan and its Potential Applications at Regional Levels

Extremophiles, the microorganisms thriving in extreme environments, provide valuable resources for practicing novel biotechnological processes. Pakistan homes a wide spectrum of extreme environments which harbor various biotechnologically significant microorganisms. This review gauges the structural and functional bacterial diversity of several extreme environments, emphasizing their potentials as a source of extremozymes, and in bioleaching, bioremediation, and bioenergy production at regional level. Further, this review highlights a panoramic account of the local natural conservatories of extremophiles. The inadequacies of current fragmental research are discussed with suggestions to quantitatively define the structural and functional diversity of unexplored extreme localities.

microbiology↗

bioRxiv2018

Altered Bile Acid Profile in Mild Cognitive Impairment and Alzheimer’s Disease: Relationship to Neuroimaging and CSF Biomarkers

IntroductionBile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimers disease (AD) including neuroinflammation and amyloid-{beta} deposition.\n\nMethodSerum levels of 20 primary and secondary BA metabolites from the AD Neuroimaging Initiative (n=1562) were measured using targeted metabolomic profiling. We assessed the association of BAs with the \"A/T/N\" (Amyloid, Tau and Neurodegeneration) biomarkers for AD: CSF biomarkers, atrophy (MRI), and brain glucose metabolism ([18F]FDG-PET).\n\nResultsOf 23 BA and relevant calculated ratios, three BA signatures were associated with CSF A{beta}1-42 (\"A\") and three with CSF p-tau181 (\"T\") (corrected p<0.05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy (\"N\"), respectively (corrected p<0.05).\n\nConclusionThis is the first study to show serum-based BA metabolites are associated with \"A/T/N\" AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.

neuroscience↗

bioRxiv2018

Altered Bile Acid Profile Associates with Cognitive Impairment in Alzheimer’s Disease – An Emerging Role for Gut Microbiome

IntroductionIncreasing evidence suggests a role for the gut microbiome in central nervous system disorders and specific role for the gut-brain axis in neurodegeneration. Bile acids (BA), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer disease (AD).\n\nMethodsSerum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1,464 subjects including 370 cognitively normal older adults (CN), 284 with early mild cognitive impairment (MCI), 505 with late MCI, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for cofounders and multiple testing.\n\nResultsIn AD compared to CN, we observed significantly lower serum concentrations of a primary BA (cholic acid CA) and increased levels of the bacterially produced, secondary BA, deoxycholic acid (DCA), and its glycine and taurine conjugated forms. An increased ratio of DCA:CA, which reflects 7-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response related genes implicated in AD showed associations with BA profiles.\n\nConclusionWe report for the first time an association between altered BA profile, genetic variants implicated in AD and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut liver brain axis in the pathogenesis of AD.

neuroscience↗

bioRxiv2018

An origin of the immunogenicity of in vitro transcribed RNA

The emergence of RNA-based therapeutics demands robust and economical methods to produce RNA with few byproducts from aberrant activity. While in vitro transcription using the bacteriophage T7 RNA polymerase is one such popular method, its transcripts are known to display an immune-stimulatory activity that is often undesirable and uncontrollable. We here showed that the immune-stimulatory activity of T7 transcript is contributed by its aberrant activity to initiate transcription from a promoter-less DNA end. This activity results in the production of an antisense RNA that is fully complementary to the intended sense RNA product, and consequently a long double-stranded RNA (dsRNA) that can robustly stimulate a cytosolic pattern recognition receptor, MDA5. This promoter-independent transcriptional activity of the T7 RNA polymerase was observed for a wide range of DNA sequences and lengths, but can be suppressed by altering the transcription reaction with modified nucleotides or by reducing the Mg concentration. The current work thus not only offers a previously unappreciated mechanism by which T7 transcripts stimulate the innate immune system, but also shows that the immune-stimulatory activity can be readily regulated.

molecular biology↗

bioRxiv2017

Untangling Sequences: Behavior vs. External Causes

There are two fundamental reasons why sensory inputs to the brain change over time. Sensory inputs can change due to external factors or they can change due to our own behavior. Interpreting behavior-generated changes requires knowledge of how the body is moving, whereas interpreting externally-generated changes relies solely on the temporal sequence of input patterns. The sensory signals entering the neocortex change due to a mixture of both behavior and external factors. The neocortex must disentangle them but the mechanisms are unknown. In this paper, we show that a single neural mechanism can learn and recognize both types of sequences. In the model, cells are driven by feedforward sensory input and are modulated by contextual input. If the contextual input includes information derived from efference motor copies, the cells learn sensorimotor sequences. If the contextual input consists of nearby cellular activity, the cells learn temporal sequences. Through simulation we show that a network containing both types of contextual input automatically separates and learns both types of input patterns. We review experimental data that suggests the upper layers of cortical regions contain the anatomical structure required to support this mechanism.

neuroscience↗

bioRxiv2017

Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D Structure of the World

Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.

neuroscience↗

bioRxiv2017

Mathematical Model For Plant-Insect Interaction With Dynamic Response To PAD4-BIK1 Interaction And Effect Of BIK1 Inhibition

Plant-insect interaction system has been a widely studied model of the ecosystem. Attempts have long been made to understand the numerical behaviour of this counter system and make improvements in it from initial simple analogy based approach with predator-prey model to the recently developed mathematical interpretation of plant-insect interaction including concept of plant immune interventions Caughley and Lawton (1981). In our current work, we propose an improvement in the model, based on molecular interactions behind plant defense mechanism and its effect on the plant growth and insect herbivory. Motivated from an interaction network of plant biomolecules given by Louis and Shah (2014) and extending the model of Chattopadhyay, et al (2001), we propose here a mathematical model to show how plant insect interaction system is governed by the molecular components inside. Insect infestation mediated induction of Botrytis Induced Kinase-1 (BIK-1) protein causes inhibition of Phyto Alexin Deficient-4 (PAD4) protein. Lowered PAD4, being responsible for initiating plant defense mechanism, results in degraded plant immune potential and thus causes loss of plant quality. We adapt these interactions in our model to show how they influence the plant insect interaction system and also to reveal how silencing BIK-1 may aid in enhanced production of plant biomass by increasing plant immunity mediated by increase in PAD4 and associated antixenotic effects. We hypothesize the significance of BIK-1 inhibition which could result in the improvement of the plant quality. We explain the interaction system in BIK-1 inhibition using mathematical model. Further, we adopted the plethora of computational modeling and simulations techniques to identify the mechanisms of molecular inhibition.

systems biology↗

bioRxiv2016

The HTM Spatial Pooler: a neocortical algorithm for online sparse distributed coding

1.Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

neuroscience↗