Rasmussen’s Encephalitis

Introduction: Rasmussen’s encephalitis (RE) is an inflammatory encephalopathy characterized by progressive refractory focal seizures, cognitive deterioration and focal neurological deficit that occur with gradual atrophy of one brain hemisphere. Case presentation: We report a case of an 18-year-old male with a history of abnormal body movements involving the right half of the body without loss of consciousness for the last 15 years. Noncontrast computed tomography (NCCT) head and magnetic resonance imaging (MRI) revealed hemiatrophy of the left cerebral hemisphere. Conclusion: RE is a rare disease; hence, diagnosing and managing such patients may be challenging. Our aim is to draw attention of the treating physicians towards this disease with the help of this case report

Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction

Background\ud Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work.\ud \ud Results\ud We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted.\ud \ud Conclusions\ud Rapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms.\ud \ud Availability\ud The predictions for all Mtb and CD proteins are made available at: http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD respectively for browsing as well as for download

SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding

Climate Change and COP26: Are Digital Technologies and Information Management Part of the Problem or the Solution? An Editorial Reflection and Call to Action

The UN COP26 2021 conference on climate change offers the chance for world leaders to take action and make urgent and meaningful commitments to reducing emissions and limit global temperatures to 1.5 °C above pre-industrial levels by 2050. Whilst the political aspects and subsequent ramifications of these fundamental and critical decisions cannot be underestimated, there exists a technical perspective where digital and IS technology has a role to play in the monitoring of potential solutions, but also an integral element of climate change solutions. We explore these aspects in this editorial article, offering a comprehensive opinion based insight to a multitude of diverse viewpoints that look at the many challenges through a technology lens. It is widely recognized that technology in all its forms, is an important and integral element of the solution, but industry and wider society also view technology as being part of the problem. Increasingly, researchers are referencing the importance of responsible digitalization to eliminate the significant levels of e-waste. The reality is that technology is an integral component of the global efforts to get to net zero, however, its adoption requires pragmatic tradeoffs as we transition from current behaviors to a more climate friendly society

Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action

The UN COP26 2021 conference on climate change offers the chance for world leaders to take action and make urgent and meaningful commitments to reducing emissions and limit global temperatures to 1.5 °C above pre-industrial levels by 2050. Whilst the political aspects and subsequent ramifications of these fundamental and critical decisions cannot be underestimated, there exists a technical perspective where digital and IS technology has a role to play in the monitoring of potential solutions, but also an integral element of climate change solutions. We explore these aspects in this editorial article, offering a comprehensive opinion based insight to a multitude of diverse viewpoints that look at the many challenges through a technology lens. It is widely recognized that technology in all its forms, is an important and integral element of the solution, but industry and wider society also view technology as being part of the problem. Increasingly, researchers are referencing the importance of responsible digitalization to eliminate the significant levels of e-waste. The reality is that technology is an integral component of the global efforts to get to net zero, however, its adoption requires pragmatic tradeoffs as we transition from current behaviors to a more climate friendly society.</p

Dyke-Davidoff-Masson Syndrome: A Rare Presentation

Dyke-Davidoff-Masson syndrome (DDMS) is characterized by seizures, facial asymmetry, contralateralhemiplegia and mental retardation. The characteristic radiological features are cerebral hemiatrophy with homolateralhypertrophy of skull and sinuses. Case report: We report a case of DDMS in a 41-year-old female who presented withgeneralized tonic-clonic seizures, hemiparesis of the right upper and lower limb with deviation of the mouth to left.Noncontrast computed tomography (NCCT) head and magnetic resonance imaging (MRI) revealed hemiatrophy of the rightcerebral hemisphere. Conclusion: DDMS is a rare disease; hence, diagnosing and managing such patients may be challenging.Our aim is to draw attention of the treating physicians towards this disease with the help of this case report

Modelling metabolic rewiring during melanoma progression using Flux Balance Analysis

Improvements in melanoma diagnosis, treatment and prognosis are urgently warranted, given that it causes 3 out of 4 skin cancer deaths. A large amount of genomic and molecular data indicate that alterations occur at multiple scales in different stages of melanoma. Metabolic rewiring is a characteristic feature of progressive cancers that facilitates sustenance of tumors, and caters to the changing energy requirements. Since such rewiring involves multiple variations in the metabolic network that are orchestrated, a systems perspective is necessary to understand the nature, significance, mechanisms and identification of the key steps. To address this, we integrate patient transcriptome data with a prior human reference metabolic model and construct stage-specific genome-scale metabolic models. Using flux balance analysis, we simulate the metabolic flows and compute the reaction fluxes specific to normal skin, primary melanoma and metastatic melanoma, from which the reactions with flux differences between conditions were identified. Reactions related to Warburg effect, as anticipated and in addition, ROS detoxification and tyrosine metabolism were largely altered in all stages of melanoma. Vitamin A and vitamin C sub-systems are identified to be involved in different stages, consistent with experimental studies from literature that indicate their support to cancer progression. Gene essentiality studies using the melanoma model identified 5 important genes NME2, CMPK1, HSD17B4, DTYMK and PRODH for the proliferation of melanoma cells, which can be explored as potential drug targets

Modelling metabolic rewiring during melanoma progression using flux balance analysis

Improvements in melanoma diagnosis, treatment and prognosis are urgently warranted, given that it causes 3 out of 4 skin cancer deaths. A large amount of genomic and molecular data indicate that alterations occur at multiple scales in different stages of melanoma. Metabolic rewiring is a characteristic feature of progressive cancers that facilitates sustenance of tumors, and caters to the changing energy requirements. Since such rewiring involves multiple variations in the metabolic network that are orchestrated, a systems perspective is necessary to understand the nature, significance, mechanisms and identification of the key steps. To address this, we integrate patient transcriptome data with a prior human reference metabolic model and construct stage-specific genome-scale metabolic models. Using flux balance analysis, we simulate the metabolic flows and compute the reaction fluxes specific to normal skin, primary melanoma and metastatic melanoma, from which the reactions with flux differences between conditions were identified. Reactions related to Warburg effect, as anticipated and in addition, ROS detoxification and tyrosine metabolism were largely altered in all stages of melanoma. Vitamin A and vitamin C sub-systems are identified to be involved in different stages, consistent with experimental studies from literature that indicate their support to cancer progression. Gene essentiality studies using the melanoma model identified 5 important genes NME2, CMPK1, HSD17B4, DTYMK and PRODH for the proliferation of melanoma cells, which can be explored as potential drug targets

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions

<div><p>Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue–human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue–human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (<a href="http://proline.biochem.iisc.ernet.in/DenHunt/" target="_blank">http://proline.biochem.iisc.ernet.in/DenHunt/</a>).</p></div