Neurosyphilis, A True Chameleon of Neurology.

BACKGROUND: Neurosyphilis (NS) is a rarely encountered scenario today. Manifestations are heterogeneous, and their characteristics have changed in the antibiotic era. A differential diagnosis of NS is not commonly thought of even with relevant clinical-radiological features, as it mimics many common neurological syndromes. OBJECTIVES: To study the manifestations of NS in the present era and the process of diagnosis. METHOD: The data of ten patients with NS was collected and analyzed. Their background data, clinical features, investigations, the process of reaching the diagnosis, management and outcomes were recorded. OBSERVATIONS AND RESULTS: The manifestations of NS in our cohort included six patients with cognitive decline/encephalopathy and one each with meningitis with cranial nerve palsies, cerebellar ataxia, myelitis and asymptomatic NS. The presence of Argyll Robertson pupil helped to clinch diagnosis in one patient. Treponemal tests were ordered in two patients only after alternative etiologies were looked at, to begin with, whereas in six patients treponemal test was requested as a part of standard workup for dementia/ataxia. CONCLUSIONS: NS dementia and behavior changes are mistaken for degenerative, vascular, nutritional causes, autoimmune encephalitis or prion disease. Meningitis has similarities with infective (tubercular), granulomatous (sarcoidosis, Wegener's), collagen vascular disease and neoplastic meningitis, and myelitis simulates demyelination or nutritional myelopathy (B12 deficiency). Rarely, NS can also present with cerebellar ataxia. Contemplate NS as one of the rare causes for such syndromes, and its early treatment produces good outcomes.

Acute haemorrhagic leukoencephalitis (AHLE) - our experience and a short review.

BACKGROUND: Acute haemorrhagic leukoencephalitis (AHLE), a rare variant of acute disseminated encephalomyelitis (ADEM), often presents differently from classical ADEM, thereby posing a diagnostic challenge to the clinician. AIM: To report AHLE, its clinic-radiological manifestations, process of diagnosis and prognosis. METHOD AND RESULTS: Eight patients presented with altered sensorium, acute focal deficits with or without seizures. Initial workup showed evidence of haemorrhagic lobar or thalamic lesions in seven patients. All patients underwent extensive evaluation for collagen vascular disease and vasculitis profile, autoimmune encephalitis panel and aquaporin-4 antibody, which were found to be normal. Cerebrospinal fluid (CSF) biochemistry and microscopy was non-contributory and CSF viral PCRs, toxoplasma antibodies, cryptococcal antigen were also negative. All patients had progressively worsening sensorium and neurological deficits. Repeat MRIs showed increase in oedema in the lesions and appearance/expansion of haemorrhage in the thalamic/hemispherical lesions. All patients received intravenous methylprednisolone (IVMP) without any benefit. Four patients underwent plasmapheresis (PLEX), one received intravenous immunoglobulin (IVIG) and one received both second line immunotherapies, without significant improvement. Brain biopsy (performed in three patients) showed inflammatory demyelination and areas of haemorrhage, thus confirming the diagnosis. Six patients succumbed in 7-30 days of the illness, despite aggressive treatment and only two survived, albeit with a significant disability. CONCLUSION: AHLE is a rare, yet very severe variant of ADEM. MRI shows lesions with haemorrhages, oedema and mass effect and histology findings reveal inflammatory infiltrates, haemorrhagic foci and fibrinoid necrosis of vessel walls. Prognosis is worse as compared to the classic ADEM, with a high mortality rate. To the best of our knowledge, this is one of the largest series of AHLE to have been reported anywhere in the world. KEYMESSAGE: Acute encephalopathy, multifocal deficits accompanied by haemorrhagic CNS demyelinating lesions with oedema and mass effect are the key features of AHLE. It is a rare, yet very severe form of ADEM with very high morbidity and mortality.

Myelin Oligodendrocyte Glycoprotein (MOG)-IgG Associated Demyelinating Disease: Our Experience with this Distinct Syndrome.

BACKGROUND: Discovery of serum myelin oligodendrocyte glycoprotein (MOG) antibody testing in demyelination segregated MOG-IgG disease from AQ-4-IgG positive NMOSD. AIMS: To study clinico-radiological manifestations, pattern of laboratory and electrophysiological investigations and response to treatment through follow up in MOG-IgG positive patients. METHOD: Retrospective data of MOG-IgG positive patients was collected. Demographics, clinical manifestations at onset and at follow up and relapses, anti AQ-4-IgG status, imaging and all investigations were performed, treatment of relapses and further immunomodulatory therapy were captured. RESULTS: In our 30 patients, F: M ratio was 2.75:1 and adult: child ratio 4:1. Relapses at presentation were optic neuritis {ON}(60%), longitudinally extensive transverse myelitis {LETM}(20%), acute disseminated encephalomyelitis {ADEM}(13.4%), simultaneous ON with myelitis (3.3%) and diencephalic Syndrome (3.3%). Salient MRI features were ADEM-like lesions, middle cerebellar peduncle fluffy infiltrates, thalamic and pontine lesions and longitudinally extensive ON {LEON} as well as non-LEON. Totally, 50% patients had a relapsing course. Plasma exchange and intravenous immunoglobulin worked in patients who showed a poor response to intravenous methylprednisolone. Prednisolone, Azathioprine, Mycophenolate and Rituximab were effective attack preventing agents. CONCLUSIONS: MOG-IgG related manifestations in our cohort were monophasic/recurrent/simultaneous ON, myelitis, recurrent ADEM, brainstem encephalitis and diencephalic Syndrome. MRI features suggestive of MOG-IgG disease were confluent ADEM-like lesions, middle cerebellar peduncle fluffy lesions, LETM, LEON and non-LEON. Where indicated, patients need to go on immunomodulation as it has a relapsing course and can accumulate significant disability. Because of its unique manifestations, it needs to be considered as a distinct entity. To the best of our knowledge, this is the largest series of MOG-IgG disease reported from India.

Constraining the complexity of promoter dynamics using fluctuations in gene expression.

Gene expression is an inherently stochastic process with transcription of mRNAs often occurring in bursts: short periods of activity followed by typically longer periods of inactivity. While a simple model involving switching between two promoter states has been widely used to analyze transcription dynamics, recent experimental observations have provided evidence for more complex kinetic schemes underlying bursting. Specifically, experiments provide evidence for complexity in promoter dynamics during the switch from the transcriptionally inactive to the transcriptionally active state. An open question in the field is: what is the minimal complexity needed to model promoter dynamics and how can we determine this? Here, we show that measurements of mRNA fluctuations can be used to set fundamental bounds on the complexity of promoter dynamics. We study models wherein the switching time distribution from transcriptionally inactive to active states is described by a general waiting-time distribution. Using approaches from renewal theory and queueing theory, we derive analytical expressions which connect the Fano factor of mRNA distributions to the waiting-time distribution for promoter switching between inactive and active states. The results derived lead to bounds on the minimal number of promoter states and thus allow us to derive bounds on the minimal complexity of promoter dynamics based on single-cell measurements of mRNA levels.

Stochastic modeling of phenotypic switching and chemoresistance in cancer cell populations.

Phenotypic heterogeneity in cancer cells is widely observed and is often linked to drug resistance. In several cases, such heterogeneity in drug sensitivity of tumors is driven by stochastic and reversible acquisition of a drug tolerant phenotype by individual cells even in an isogenic population. Accumulating evidence further suggests that cell-fate transitions such as the epithelial to mesenchymal transition (EMT) are associated with drug resistance. In this study, we analyze stochastic models of phenotypic switching to provide a framework for analyzing cell-fate transitions such as EMT as a source of phenotypic variability in drug sensitivity. Motivated by our cell-culture based experimental observations connecting phenotypic switching in EMT and drug resistance, we analyze a coarse-grained model of phenotypic switching between two states in the presence of cytotoxic stress from chemotherapy. We derive analytical results for time-dependent probability distributions that provide insights into the rates of phenotypic switching and characterize initial phenotypic heterogeneity of cancer cells. The results obtained can also shed light on fundamental questions relating to adaptation and selection scenarios in tumor response to cytotoxic therapy.

Guidelines versus ground lines: Tuberculosis of the central nervous system.

AIM: This questionnaire-based national survey is aimed at understanding the patterns of practice of various aspects of central nervous system (CNS) tuberculosis (TB) among neurologists. SETTINGS AND DESIGN: Neurology department of a tertiary medical college. MATERIALS AND METHODS: A questionnaire was sent through email to all practicing neurologists in India. The responses were analyzed. STATISTICAL ANALYSIS: Inferential statistics. RESULTS: In all, 144 responses were received (out of the 853 questionnaires sent). The major discrepancies were in the primary antitubercular drug regimen (HRZE + HR), duration for tubercular meningitis (TBM) [12 months] and tuberculoma (12-18 months) to develop, follow-up (varied), linezolid use (varied), proportion of drug-resistant cases (<25%), and not taking histological aids (91%). The cerebrospinal fluid (CSF) TB polymerase chain reaction (PCR) utility (75%), not using CSF adenosine deaminase [ADA] (58%), the strategy to stop antitubercular drugs, and the use of steroids (77%) were according to guidelines. CONCLUSION: The present survey, for the first time, provides ground-level evidence of various aspects of CNS TB as practiced by neurologists in India. The major diversity was observed in therapeutics such as the choice of antitubercular drugs, its duration, linezolid use beyond the recommended duration, and knowledge of drug resistance. The monitoring aspects of CNS TB also showed variations. The investigational aspects of CNS TB such as using TB PCR, not using CSF ADA, and regular neuroimaging revealed a good clinical practice. Other CSF parameters require uniformity. This survey thus helps to identify areas of future work in CNS TB in India.

A stochastic model for post-transcriptional regulation of rare events in gene expression.

Post-transcriptional regulation of gene expression is often a critical component of cellular processes involved in cell-fate decisions. Correspondingly, considerable efforts have focused on modeling post-transcriptional regulation of stochastic gene expression and on quantifying its impact on the mean and variance of protein levels. However, the impact of post-transcriptional regulation on rare events corresponding to large deviations in gene expression is less well understood. Here, we study a simple model involving post-transcriptional control of gene expression and characterize the impact of regulation on large deviations in activity fluctuations. We derive analytical results for the large deviation function for protein production rate and for the corresponding driven process which characterizes system fluctuations conditional on the rare event. Our results suggest that fluctuations in burst size rather than frequency play a dominant role in controlling rare events. The results derived also provide insight into how post-transcriptional regulation can be used to fine-tune the probability of rare fluctuations and to thereby control phenotypic variation in a population of cells.

A Free Energy Based Approach for Distance Metric Learning.

We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. This formulation leads to a mapping to statistical mechanics such that the metric learning optimization problem becomes equivalent to free energy minimization. Correspondingly, our approach leads to an analytical solution of the optimization problem based on the Boltzmann distribution. The mapping established in this work suggests new approaches for dimensionality reduction and provides insights into determination of optimal parameters for the penalty term. Furthermore, we demonstrate that the metric projects the data onto direction of maximum dissimilarity with optimal and tunable separation between classes and thus the transformation can be used for high dimensional data visualization, classification, and clustering tasks. We benchmark our method against previous distance learning methods and provide an efficient implementation in an R package available to download at: https://github.com/kouroshz/fenn.

Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.

Bioinformatic Approach for Prediction of CsrA/RsmA-Regulating Small RNAs in Bacteria.

CsrA/RsmA is a RNA-binding protein that functions as a global regulator controlling important processes such as virulence, secondary metabolism, motility, and biofilm formation in diverse bacterial species. The activity of CsrA/RsmA is regulated by small RNAs that contain multiple binding sites for the protein. The expression of these noncoding RNAs effectively sequesters the protein and reduces free cellular levels of CsrA/RsmA. While multiple bacterial small RNAs that bind to and regulate CsrA/RsmA levels have been discovered, it is anticipated that there are several such small RNAs that remain undiscovered. To assist in the discovery of these small RNAs, we have developed a bioinformatics approach that combines sequence- and structure-based features to predict small RNA regulators of CsrA/RsmA. This approach analyzes structural motifs in the ensemble of low energy secondary structures of known small RNA regulators of CsrA/RsmA and trains a binary classifier on these features. The proposed machine learning approach leads to several testable predictions for small RNA regulators of CsrA/RsmA, thereby complementing and accelerating experimental efforts aimed at discovery of noncoding RNAs in the CsrA/RsmA pathway.

Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach.

Regulation by microRNAs (miRNAs) and modulation of miRNA activity are critical components of diverse cellular processes. Recent research has shown that miRNA-based regulation of the tumor suppressor gene PTEN can be modulated by the expression of other miRNA targets acting as competing endogenous RNAs (ceRNAs). However, the key sequence-based features enabling a transcript to act as an effective ceRNA are not well understood and a quantitative model associating statistical significance to such features is currently lacking. To identify and assess features characterizing target recognition by PTEN-regulating miRNAs, we analyze multiple datasets from PAR-CLIP experiments in conjunction with RNA-Seq data. We consider a set of miRNAs known to regulate PTEN and identify high-confidence binding sites for these miRNAs on the 3' UTR of protein coding genes. Based on the number and spatial distribution of these binding sites, we calculate a set of probabilistic features that are used to make predictions for novel ceRNAs of PTEN. Using a series of experiments in human prostate cancer cell lines, we validate the highest ranking prediction (TNRC6B) as a ceRNA of PTEN. The approach developed can be applied to map ceRNA networks of critical cellular regulators and to develop novel insights into crosstalk between different pathways involved in cancer.

Stochastic gene expression conditioned on large deviations.

The intrinsic stochasticity of gene expression can give rise to large fluctuations and rare events that drive phenotypic variation in a population of genetically identical cells. Characterizing the fluctuations that give rise to such rare events motivates the analysis of large deviations in stochastic models of gene expression. Recent developments in non-equilibrium statistical mechanics have led to a framework for analyzing Markovian processes conditioned on rare events and for representing such processes by conditioning-free driven Markovian processes. We use this framework, in combination with approaches based on queueing theory, to analyze a general class of stochastic models of gene expression. Modeling gene expression as a Batch Markovian Arrival Process (BMAP), we derive exact analytical results quantifying large deviations of time-integrated random variables such as promoter activity fluctuations. We find that the conditioning-free driven process can also be represented by a BMAP that has the same form as the original process, but with renormalized parameters. The results obtained can be used to quantify the likelihood of large deviations, to characterize system fluctuations conditional on rare events and to identify combinations of model parameters that can give rise to dynamical phase transitions in system dynamics.

Frequency modulation of stochastic gene expression bursts by strongly interacting small RNAs.

The sporadic nature of gene expression at the single-cell level-long periods of inactivity punctuated by bursts of mRNA or protein production-plays a critical role in diverse cellular processes. To elucidate the cellular role of bursting in gene expression, synthetic biology approaches have been used to design simple genetic circuits with bursty mRNA or protein production. Understanding how such genetic circuits can be designed with the ability to control burst-related parameters requires the development of quantitative stochastic models of gene expression. In this work, we analyze stochastic models for the regulation of gene expression bursts by strongly interacting small RNAs. For the parameter range considered, results based on mean-field approaches are significantly inaccurate and alternative analytical approaches are needed. Using simplifying approximations, we obtain analytical results for the corresponding steady-state distributions that are in agreement with results from stochastic simulations. These results indicate that regulation by small RNAs, in the strong interaction limit, can be used to effectively modulate the frequency of bursting. We explore the consequences of such regulation for simple genetic circuits involving feedback effects and switching between promoter states.

Transcriptional Bursting in Gene Expression: Analytical Results for General Stochastic Models.

Gene expression in individual cells is highly variable and sporadic, often resulting in the synthesis of mRNAs and proteins in bursts. Such bursting has important consequences for cell-fate decisions in diverse processes ranging from HIV-1 viral infections to stem-cell differentiation. It is generally assumed that bursts are geometrically distributed and that they arrive according to a Poisson process. On the other hand, recent single-cell experiments provide evidence for complex burst arrival processes, highlighting the need for analysis of more general stochastic models. To address this issue, we invoke a mapping between general stochastic models of gene expression and systems studied in queueing theory to derive exact analytical expressions for the moments associated with mRNA/protein steady-state distributions. These results are then used to derive noise signatures, i.e. explicit conditions based entirely on experimentally measurable quantities, that determine if the burst distributions deviate from the geometric distribution or if burst arrival deviates from a Poisson process. For non-Poisson arrivals, we develop approaches for accurate estimation of burst parameters. The proposed approaches can lead to new insights into transcriptional bursting based on measurements of steady-state mRNA/protein distributions.

A sequence-based approach for prediction of CsrA/RsmA targets in bacteria with experimental validation in Pseudomonas aeruginosa.

CsrA/RsmA homologs are an extensive family of ribonucleic acid (RNA)-binding proteins that function as global post-transcriptional regulators controlling important cellular processes such as secondary metabolism, motility, biofilm formation and the production and secretion of virulence factors in diverse bacterial species. While direct messenger RNA binding by CsrA/RsmA has been studied in detail for some genes, it is anticipated that there are numerous additional, as yet undiscovered, direct targets that mediate its global regulation. To assist in the discovery of these targets, we propose a sequence-based approach to predict genes directly regulated by these regulators. In this work, we develop a computer code (CSRA_TARGET) implementing this approach, which leads to predictions for several novel targets in Escherichia coli and Pseudomonas aeruginosa. The predicted targets in other bacteria, specifically Salmonella enterica serovar Typhimurium, Pectobacterium carotovorum and Legionella pneumophila, also include global regulators that control virulence in these pathogens, unraveling intricate indirect regulatory roles for CsrA/RsmA. We have experimentally validated four predicted RsmA targets in P. aeruginosa. The sequence-based approach developed in this work can thus lead to several testable predictions for direct targets of CsrA homologs, thereby complementing and accelerating efforts to unravel global regulation by this important family of proteins.

Exact distributions for stochastic gene expression models with bursting and feedback.

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play important roles in controlling noise in gene expression and phenotypic variation. A quantitative understanding of the impact of these factors requires analysis of the corresponding stochastic models. However, for stochastic models of gene expression with feedback and bursting, exact analytical results for protein distributions have not been obtained so far. Here, we analyze a model of gene expression with bursting and feedback regulation and obtain exact results for the corresponding protein steady-state distribution. The results obtained provide new insights into the role of bursting and feedback in noise regulation and optimization. Furthermore, for a specific choice of parameters, the system studied maps on to a two-state biochemical switch driven by a bursty input noise source. The analytical results derived provide quantitative insights into diverse cellular processes involving noise in gene expression and biochemical switching.

Cancer: tilting at windmills?

One of the striking characteristics of cancer cells is their phenotypic diversity and ability to switch phenotypes in response to environmental fluctuations. Such phenotypic changes (e.g. from drug-sensitive to drug-resistant), which are critical for survival and proliferation, are widely believed to arise due to mutations in the cancer cell's genome. However, there is growing concern that such a deterministic view is not entirely consistent with multiple lines of evidence which indicate that cancer can arise in the absence of mutations and can even be reversed to normalcy despite the mutations. In this Commentary, we wish to present an alternate view that highlights how stochasticity in protein interaction networks (PINs) may play a key role in cancer initiation and progression. We highlight the potential role of intrinsically disordered proteins (IDPs) and submit that targeting IDPs can lead to new insights and treatment protocols for cancer.

Structural rearrangement in an RsmA/CsrA ortholog of Pseudomonas aeruginosa creates a dimeric RNA-binding protein, RsmN.

In bacteria, the highly conserved RsmA/CsrA family of RNA-binding proteins functions as global posttranscriptional regulators acting on mRNA translation and stability. Through phenotypic complementation of an rsmA mutant in Pseudomonas aeruginosa, we discovered a family member, termed RsmN. Elucidation of the RsmN crystal structure and that of the complex with a hairpin from the sRNA, RsmZ, reveals a uniquely inserted α helix, which redirects the polypeptide chain to form a distinctly different protein fold to the domain-swapped dimeric structure of RsmA homologs. The overall ß sheet structure required for RNA recognition is, however, preserved with compensatory sequence and structure differences, allowing the RsmN dimer to target binding motifs in both structured hairpin loops and flexible disordered RNAs. Phylogenetic analysis indicates that, although RsmN appears unique to P. aeruginosa, homologous proteins with the inserted α helix are more widespread and arose as a consequence of a gene duplication event.

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes.

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.