Unravelling the genetic basis of retinal dystrophies in Pakistani consanguineous families.

BACKGROUND: Retinitis Pigmentosa (RP) is a clinically and genetically progressive retinal dystrophy associated with severe visual impairments and sometimes blindness, the most common syndromic form of which is Usher syndrome (USH). This study aimed to further increase understanding of the spectrum of RP in the Khyber Pakhtunkhwa region of Pakistan. METHODOLOGY: Four consanguineous families of Pashtun ethnic group were investigated which were referred by the local collaborating ophthalmologists. In total 42 individuals in four families were recruited and investigated using whole exome and dideoxy sequencing. Among them, 20 were affected individuals including 6 in both family 1 and 2, 5 in family 3 and 3 in family 4. RESULT: Pathogenic gene variants were identified in all four families, including two in cone dystrophy and RP genes in the same family (PDE6C; c.480delG, p.Asn161ThrfsTer33 and TULP1; c.238 C > T, p.Gln80Ter) with double-homozygous individuals presenting with more severe disease. Other pathogenic variants were identified in MERTK (c.2194C > T, p.Arg732Ter), RHO (c.448G > A, p.Glu150Lys) associated with non-syndromic RP, and MYO7A (c.487G > A, p.Gly163Arg) associated with USH. In addition, the reported variants were of clinical significance as the PDE6C variant was detected novel, whereas TULP1, MERTK, and MYO7A variants were detected rare and first time found segregating with retinal dystrophies in Pakistani consanguineous families. CONCLUSIONS: This study increases knowledge of the genetic basis of retinal dystrophies in families from Pakistan providing information important for genetic testing and diagnostic provision particularly from the Khyber Pakhtunkhwa region.

A Novel Intronic Deletion in PDE6B Causes Autosomal Recessive Retinitis Pigmentosa by Interfering with RNA Splicing.

INTRODUCTION: Retinitis pigmentosa (RP) is a rare degenerative retinal disease caused by mutations in approximately seventy genes. Currently, despite the availability of large-scale DNA sequencing technologies, ∼30-40% of patients still cannot be diagnosed at the molecular level. In this study, we investigated a novel intronic deletion of PDE6B, encoding the beta subunit of phosphodiesterase 6 in association with recessive RP. METHODS: Three unrelated consanguineous families were recruited from the northwestern part of Pakistan. Whole exome sequencing was performed for the proband of each family, and the data were analyzed according to an in-house computer pipeline. Relevant DNA variants in all available members of these families were assessed through Sanger sequencing. A minigene-based splicing assay was also performed. RESULTS: The clinical phenotype for all patients was compatible with rod cone degeneration, with the onset during childhood. Whole exome sequencing revealed a homozygous 18 bp intronic deletion (NM_000283.3:c.1921-20_1921-3del) in PDE6B, which co-segregated with disease in 10 affected individuals. In vitro splicing tests showed that this deletion causes aberrant RNA splicing of the gene, leading to the in-frame deletion of 6 codons and, likely, to disease. CONCLUSION: Our findings further expand the mutational spectrum of the PDE6B gene.

A novel homozygous missense substitution p.Thr313Ile in the PDE6B gene underlies autosomal recessive retinitis pigmentosa in a consanguineous Pakistani family.

BACKGROUND: Retinitis pigmentosa (RP) is one of the most frequent hereditary retinal diseases that often starts with night blindness and eventually leads to legal blindness. Our study aimed to identify the underlying genetic cause of autosomal recessive retinitis pigmentosa (arRP) in a consanguineous Pakistani family. METHODS: Following a detailed ophthalmological examination of the patients by an ophthalmologist, whole-exome sequencing was performed on the proband's DNA to delineate the genetic cause of RP in the family. In-depth computational methods, in-silico analysis, and familial co-segregation study were performed for variant detection and validation. RESULTS: We studied an inbred Pakistani family with two siblings affected by retinitis pigmentosa. The proband, a 32 years old female, was clinically diagnosed with RP at the age of 6 years. A classical night blindness symptom was reported in the proband since her early childhood. OCT report showed a major reduction in the outer nuclear layer and the ellipsoid zone width, leading to the progression of the disease. Exome sequencing revealed a novel homozygous missense mutation (c.938C > T;p.Thr313Ile) in exon 12 of the PDE6B gene. The mutation p.Thr313Ile co-segregated with RP phenotype in the family. The altered residue (p.Thr313) was super conserved evolutionarily across different vertebrate species, and all available in silico tools classified the mutation as highly pathogenic. CONCLUSION: We present a novel homozygous pathogenic mutation in the PDE6B gene as the underlying cause of arRP in a consanguineous Pakistani family. Our findings highlight the importance of missense mutations in the PDE6B gene and expand the known mutational repertoire of PDE6B-related RP.

A Hybrid Preprocessor DE-ABC for Efficient Skin-Lesion Segmentation with Improved Contrast.

Rapid advancements and the escalating necessity of autonomous algorithms in medical imaging require efficient models to accomplish tasks such as segmentation and classification. However, there exists a significant dependency on the image quality of datasets when using these models. Appreciable improvements to enhance datasets for efficient image analysis have been noted in the past. In addition, deep learning and machine learning are vastly employed in this field. However, even after the advent of these advanced techniques, a significant space exists for new research. Recent research works indicate the vast applicability of preprocessing techniques in segmentation tasks. Contrast stretching is one of the preprocessing techniques used to enhance a region of interest. We propose a novel hybrid meta-heuristic preprocessor (DE-ABC), which optimises the decision variables used in the contrast-enhancement transformation function. We validated the efficiency of the preprocessor against some state-of-the-art segmentation algorithms. Publicly available skin-lesion datasets such as PH2, ISIC-2016, ISIC-2017, and ISIC-2018 were employed. We used Jaccard and the dice coefficient as performance matrices; at the maximum, the proposed model improved the dice coefficient from 93.56% to 94.09%. Cross-comparisons of segmentation results with the original datasets versus the contrast-stretched datasets validate that DE-ABC enhances the efficiency of segmentation algorithms.

Role of Cell Morphology in Classical Delta-Notch Pattern Formation.

Notch signaling is responsible for creating contrasting states of differentiation among neighboring cells during organism's early development. Various factors can affect this highly conserved intercellular signaling pathway, for the formation of fine-grained pattern in cell tissues. As cells undergo dramatic structural changes during development, one of the factors that can influence cell-cell communication is cell morphology. In this study, we elucidate the role of cell morphology on mosaic pattern formation in a realistic epithelial layer cell model. We discovered that cell signaling strength is inversely related to the cell area, such that smaller cells have higher probability/tendency of becoming signal producing cells as compared to larger cells during early embryonic days. In a nutshell, our work highlights the role of cell morphology on the stochastic cell fate decision process in the epithelial layer of multicellular organisms.

Cell fate determination is influenced by Notch heterogeneity.

Notch signaling (NS) determines the fate of adjacent cells during metazoans development. This intercellular signaling mechanism regulates diverse development processes like cell differentiation, proliferation, survival and is considered responsible for maintaining cellular homeostasis. In this study, we elucidate the role of Notch heterogeneity (NH) in cell fate determination. We studied the role of NH at intercellular, intracellular and the coexistence of Notch variation simultaneously at the intracellular and intercellular level in direct cell-cell signaling on an irregular cell mosaic. In addition, the effect of intracellular Notch receptor diffusion on an irregular cell lattice is also taken into account during Delta-Notch lateral inhibition (LI) process. Through mathematical and computational models, we discovered that the classical checkerboard pattern formation can be reproduced with an accuracy of 70-81% by accounting for NH in a realistic epithelial layer of multicellular organisms.

LncRNAs as Potential Therapeutic Targets in Thyroid Cancer.

Thyroid cancer (TC) is the most common cancer of endocrine system. TC can be subdivided into 4 different entities, papillary, follicular, medullary and anaplastic thyroid cancer. Among them, anaplastic thyroid cancer has the poorest prognosis. Exploring new therapeutic approach may entail favorable prediction as well as increasing overall survival rate of patients. Long non-coding RNAs (lncRNAs), have vast implications in different cancer types. Although they are not transcribed into proteins, they can act as a harness in regulating a plethora of biological functions. They have been implicated in a decisive role in gene expression via modulation of both coding and non-coding RNAs. This article discuss the multi-facet role of lncRNA in thyroid cancer biology.
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Wnt Signaling: A Potential Therapeutic Target in Head and Neck Squamous Cell Carcinoma

Cellular maintenance and development are two fundamental mechanisms regulated by the canonical Wnt signaling pathway. Wnt/beta-catenin signaling pathway controls a myriad of cellular processes that are essential for normal cell functioning. Cell cycle progression, differentiation, fate determination, and migration are generally orchestrated by canonical Wnt signaling. Altered Wnt/beta-catenin signaling has been considered a promoting event for different types of cancers and the oncogenic potential of Wnt signaling have been discussed in many cancer types, including breast, colon, pancreatic as well as head and neck. Furthermore, Wnt signaling is critical for the maintenance and stemness of both the normal as well as cancer stem cells. This review sheds new light on Wnt signaling and explains how it can regulate normal physiological processes and curtail the development of cancer. It depicts the vital functions of Wnt signaling in the stem cell growth and differentiation by focusing on current druggable targets that have been ascribed by recent studies. Thus, Wnt signaling pathway retains a tremendous potential in eradicating head and neck squamous cell carcinoma.

Thermal, Physico-Chemical, and Mechanical Behaviour of Mass Concrete with Hybrid Blends of Bentonite and Fly Ash.

Mass concrete has been commonly known for its thermal stresses which arise due to the entrapment of hydration temperature susceptible to thermal cracking. The utilization of mineral additives is a promising and widely adopted technique to mitigate such effects. This paper presents the thermal, physico-chemical, mechanical, and morphological behaviour of mass concrete with blends of bentonite (BT) and fly ash (FA). Apart from the rise in temperature due to hydration, the compressive strength, ultrasonic pulse velocity (UPV), differential thermal analysis (DTA), thermo-gravimetric analysis (TGA), X-ray diffraction (XRD) analysis, and microstructure were studied. The results of this study revealed that the substitution of BT and FA significantly improved the compressive strength and development rate of UPV in the mass concrete samples. The FA concrete (FC) specimen presented the lowest temperature during the peak hours compared to all other concrete mixes studied in this research. Bentonite concrete (BC) was also found to be more effective in controlling the escalation of temperature in mass concrete. Scan electron microscopy (SEM) micrographs presented partially reacted FA particles in a mix. XRD and DTA analysis indicated that the concentration of calcium hydroxide (CH) declined by substituting FA and BT, specifically in ternary blends, which was due to the dilution effect and consumption of CH through the pozzolanic reaction.

System-based strategies for p53 recovery.

The authors have proposed a systems theory-based novel drug design approach for the p53 pathway. The pathway is taken as a dynamic system represented by ordinary differential equations-based mathematical model. Using control engineering practices, the system analysis and subsequent controller design is performed for the re-activation of wild-type p53. p53 revival is discussed for both modes of operation, i.e. the sustained and oscillatory. To define the problem in control system paradigm, modification in the existing mathematical model is performed to incorporate the effect of Nutlin. Attractor point analysis is carried out to select the suitable domain of attraction. A two-loop negative feedback control strategy is devised to drag the system trajectories to the attractor point and to regulate cellular concentration of Nutlin, respectively. An integrated framework is constituted to incorporate the pharmacokinetic effects of Nutlin in the cancerous cells. Bifurcation analysis is also performed on the p53 model to see the conditions for p53 oscillation.

Mathematical Modeling of E6-p53 interactions in Cervical Cancer

Background: Cervical cancer is the third most common cancer in women throughout the world. The human papillomavirus (HPV) E6 viral protein plays an essential role in proteasomal degradation of the cancer suppressant protein p53. As a result, p53 negative regulation and apoptosis relevant activities are abrogated, facilitating development of cervical cancer. Methods: A mathematical model of E6-p53 interactions was developed using mathematical laws. In-silico simulations were carried out on CellDesigner and as a test case the small molecule drug RITA was considered for its ability to rescue the functions of tumor suppressor p53 by inhibiting E6 mediated proteasomal degradation. Results: Using a computational model we scrutinized how p53 responds to RITA, and chemical reactions of this small molecule drug were incorporated to perceive the full effects. The evolved strategy allowed the p53 response and rescue of its tumor suppressor function to be delineated, RITA being found to block p53 interactions with E6 associated proteins. Conclusion: We could develop a model of E6-p53 interactions with incorporation of actions of the small molecule drug RITA. Suppression of E6 associated proteins by RITA induces accumulation of tumor suppressant p53. Using CellDesigner to encode the model ensured that it can be easily modified and extended as more data become available. This strategy should play an effective role in the development of therapies against cancer.

On p53 revival using system oriented drug dosage design.

We propose a new paradigm in the drug design for the revival of the p53 pathway in cancer cells. It is shown that the current strategy of using small molecule based Mdm2 inhibitors is not enough to adequately revive p53 in cancerous cells, especially when it comes to the extracting pulsating behavior of p53. This fact has come to notice when a novel method for the drug dosage design is introduced using system oriented concepts. As a test case, small molecule drug Mdm2 repressor Nutlin 3a is considered. The proposed method determines the dose of Nutlin to revive p53 pathway functionality. For this purpose, PBK dynamics of Nutlin have also been integrated with p53 pathway model. The p53 pathway is the focus of researchers for the last thirty years for its pivotal role as a frontline cancer suppressant protein due to its effect on cell cycle checkpoints and cell apoptosis in response to a DNA strand break. That is the reason for finding p53 being absent in more than 50% of tumor cancers. Various drugs have been proposed to revive p53 in cancer cells. Small molecule based drugs are at the foremost and are the subject of advanced clinical trials. The dosage design of these drugs is an important issue. We use control systems concepts to develop the drug dosage so that the cancer cells can be treated in appropriate time. We investigate by using a computational model how p53 protein responds to drug Nutlin 3a, an agent that interferes with the MDM2-mediated p53 regulation. The proposed integrated model describes in some detail the regulation network of p53 including the negative feedback loop mediated by MDM2 and the positive feedback loop mediated by Mdm2 mRNA as well as the reversible represses of MDM2 caused by Nutlin. The reported PBK dynamics of Nutlin 3a are also incorporated to see the full effect. It has been reported that p53 response to stresses in two ways. Either it has a sustained (constant) p53 response, or there are oscillations in p53 concentration. The claimed dosage strategy achieves the p53 response in the first case. However, for the induction of oscillations, it is shown through bifurcation analysis that to achieve oscillating behavior of p53 inhibition of Mdm2 is not enough, rather antirepression of the p53-Mdm2 complex is also needed which leads to the need of a new drug design paradigm.

Accounting for randomness in measurement and sampling in studying cancer cell population dynamics.

Knowing the expected temporal evolution of the proportion of different cell types in sample tissues gives an indication about the progression of the disease and its possible response to drugs. Such systems have been modelled using Markov processes. We here consider an experimentally realistic scenario in which transition probabilities are estimated from noisy cell population size measurements. Using aggregated data of FACS measurements, we develop MMSE and ML estimators and formulate two problems to find the minimum number of required samples and measurements to guarantee the accuracy of predicted population sizes. Our numerical results show that the convergence mechanism of transition probabilities and steady states differ widely from the real values if one uses the standard deterministic approach for noisy measurements. This provides support for our argument that for the analysis of FACS data one should consider the observed state as a random variable. The second problem we address is about the consequences of estimating the probability of a cell being in a particular state from measurements of small population of cells. We show how the uncertainty arising from small sample sizes can be captured by a distribution for the state probability.

Stochastic approaches in systems biology.

The discrete and random occurrence of chemical reactions far from thermodynamic equilibrium, and low copy numbers of chemical species, in systems biology necessitate stochastic approaches. This review is an effort to give the reader a flavor of the most important stochastic approaches relevant to systems biology. Notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of a stochastic framework for modeling subcellular biochemical systems. In particular, we make an effort to show how the notion of propensity, the chemical master equation (CME), and the stochastic simulation algorithm arise as consequences of the Markov property. Most stochastic modeling reviews focus on stochastic simulation approaches--the exact stochastic simulation algorithm and its various improvements and approximations. We complement this with an outline of an analytical approximation. The most common formulation of stochastic models for biochemical networks is the CME. Although stochastic simulations are a practical way to realize the CME, analytical approximations offer more insight into the influence of randomness on system's behavior. Toward that end, we cover the chemical Langevin equation and the related Fokker-Planck equation and the two-moment approximation (2MA). Throughout the text, two pedagogical examples are used to key illustrate ideas. With extensive references to the literature, our goal is to clarify key concepts and thereby prepare the reader for more advanced texts.

Investigating the two-moment characterisation of subcellular biochemical networks.

While ordinary differential equations (ODEs) form the conceptual framework for modelling many cellular processes, specific situations demand stochastic models to capture the influence of noise. The most common formulation of stochastic models for biochemical networks is the chemical master equation (CME). While stochastic simulations are a practical way to realise the CME, analytical approximations offer more insight into the influence of noise. Towards that end, the two-moment approximation (2MA) is a promising addition to the established analytical approaches including the chemical Langevin equation (CLE) and the related linear noise approximation (LNA). The 2MA approach directly tracks the mean and (co)variance which are coupled in general. This coupling is not obvious in CME and CLE and ignored by LNA and conventional ODE models. We extend previous derivations of 2MA by allowing (a) non-elementary reactions and (b) relative concentrations. Often, several elementary reactions are approximated by a single step. Furthermore, practical situations often require the use of relative concentrations. We investigate the applicability of the 2MA approach to the well-established fission yeast cell cycle model. Our analytical model reproduces the clustering of cycle times observed in experiments. This is explained through multiple resettings of M-phase promoting factor (MPF), caused by the coupling between mean and (co)variance, near the G2/M transition.

The dynamic systems approach to control and regulation of intracellular networks.

Systems theory and cell biology have enjoyed a long relationship that has received renewed interest in recent years in the context of systems biology. The term 'systems' in systems biology comes from systems theory or dynamic systems theory: systems biology is defined through the application of systems- and signal-oriented approaches for an understanding of inter- and intra-cellular dynamic processes. The aim of the present text is to review the systems and control perspective of dynamic systems. The biologist's conceptual framework for representing the variables of a biochemical reaction network, and for describing their relationships, are pathway maps. A principal goal of systems biology is to turn these static maps into dynamic models, which can provide insight into the temporal evolution of biochemical reaction networks. Towards this end, we review the case for differential equation models as a 'natural' representation of causal entailment in pathways. Block-diagrams, commonly used in the engineering sciences, are introduced and compared to pathway maps. The stimulus-response representation of a molecular system is a necessary condition for an understanding of dynamic interactions among the components that make up a pathway. Using simple examples, we show how biochemical reactions are modelled in the dynamic systems framework and visualized using block-diagrams.

Modeling and simulation of intracellular dynamics: choosing an appropriate framework.

Systems biology is a reemerging paradigm which, among other things, focuses on mathematical modeling and simulation of biochemical reaction networks in intracellular processes. For most simulation tools and publications, they are usually characterized by either preferring stochastic simulation or rate equation models. The use of stochastic simulation is occasionally accompanied with arguments against rate equations. Motivated by these arguments, we discuss in this paper the relationship between these two forms of representation. Toward this end, we provide a novel compact derivation for the stochastic rate constant that forms the basis of the popular Gillespie algorithm. Comparing the mathematical basis of the two popular conceptual frameworks of generalized mass action models and the chemical master equation, we argue that some of the arguments that have been put forward are ignoring subtle differences and similarities that are important for answering the question in which conceptual framework one should investigate intracellular dynamics.