Temporal sex specific brain gene expression pattern during early rat embryonic development.

Background: The classical concept of brain sex differentiation suggests that steroid hormones released from the gonads program male and female brains differently. However, several studies indicate that steroid hormones are not the only determinant of brain sex differentiation and that genetic differences could also be involved. Methods: In this study, we have performed RNA sequencing of rat brains at embryonic days 12 (E12), E13, and E14. The aim was to identify differentially expressed genes between male and female rat brains during early development. Results: Analysis of genes expressed with the highest sex differences showed that Xist was highly expressed in females having XX genotype with an increasing expression over time. Analysis of genes expressed with the highest male expression identified three early genes, Sry2, Eif2s3y, and Ddx3y. Discussion: The observed sex-specific expression of genes at early development confirms that the rat brain is sexually dimorphic prior to gonadal action on the brain and identifies Sry2 and Eif2s3y as early genes contributing to male brain development.

Zebrafish in understanding molecular pathophysiology, disease modeling, and developing effective treatments for Rett syndrome.

Rett syndrome (RTT) is a rare but dreadful X-linked genetic disease that mainly affects young girls. It is a neurological disease that affects nerve cell development and function, resulting in severe motor and intellectual disabilities. To date, no cure is available for treating this disease. In 90% of the cases, RTT is caused by a mutation in methyl-CpG-binding protein 2 (MECP2), a transcription factor involved in the repression and activation of transcription. MECP2 is known to regulate several target genes and is involved in different physiological functions. Mouse models exhibit a broad range of phenotypes in recapitulating human RTT symptoms; however, understanding the disease mechanisms remains incomplete, and many potential RTT treatments developed in mouse models have not shown translational effectiveness in human trials. Recent data hint that the zebrafish model emulates similar disrupted neurological functions following mutation of the mecp2 gene. This suggests that zebrafish can be used to understand the onset and progression of RTT pathophysiology and develop a possible cure. In this review, we elaborate on the molecular basis of RTT pathophysiology in humans and model organisms, including rodents and zebrafish, focusing on the zebrafish model to understand the molecular pathophysiology and the development of therapeutic strategies for RTT. Finally, we propose a rational treatment strategy, including antisense oligonucleotides, small interfering RNA technology and induced pluripotent stem cell-derived cell therapy.

Protein Kinase C (PKC)-mediated TGF-ß Regulation in Diabetic Neuropathy: Emphasis on Neuro-inflammation and Allodynia.

According to the World Health Organization (WHO), diabetes has been increasing steadily over the past few decades. In developing countries, it is the cause of increased morbidity and mortality. Diabetes and its complications are associated with education, occupation, and income across all levels of socioeconomic status. Factors, such as hyperglycemia, social ignorance, lack of proper health knowledge, and late access to medical care, can worsen diabetic complications. Amongst the complications, neuropathic pain and inflammation are considered the most common causes of morbidity for common populations. This review is focused on exploring protein kinase C (PKC)-mediated TGF-ß regulation in diabetic complications with particular emphasis on allodynia. The role of PKC-triggered TGF-ß in diabetic neuropathy is not well explored. This review will provide a better understanding of the PKC-mediated TGF-ß regulation in diabetic neuropathy with several schematic illustrations. Neuroinflammation and associated hyperalgesia and allodynia during microvascular complications in diabetes are scientifically illustrated in this review. It is hoped that this review will facilitate biomedical scientists to better understand the etiology and target drugs effectively to manage diabetes and diabetic neuropathy.

In silico analysis decodes transthyretin (TTR) binding and thyroid disrupting effects of per- and polyfluoroalkyl substances (PFAS).

Transthyretin (TTR) is a homo-tetramer protein involved in the transport of thyroid hormone (thyroxine; T4) in the plasma and cerebrospinal fluid. Many pollutants have been shown to bind to TTR, which could be alarming as disruption in the thyroid hormone system can lead to several physiological problems. It is also indicated that the monomerization of tetramer and destabilization of monomer can lead to amyloidogenesis. Many compounds are identified that can bind to tetramer and stabilize the tetramer leading to the inhibition of amyloid fibril formation. Other compounds are known to bind tetramer and induce amyloid fibril formation. Among the pollutants, per- and polyfluoroalkyl substances (PFAS) are known to disrupt the thyroid hormone system. The molecular mechanisms of thyroid hormone disruption could be diverse, as some are known to bind with thyroid hormone receptors, and others can bind to membrane transporters. Binding to TTR could also be one of the important pathways to alter thyroid signaling. However, the molecular interactions that drive thyroid-disrupting effects of long-chain and short-chain PFASs are not comprehensively understood at the molecular level. In this study, using a computational approach, we show that carbon chain length and functional group in PFASs are structural determinants, in which longer carbon chains of PFASs and sulfur-containing PFASs favor stronger interactions with TTR than their shorter-chained counterparts. Interestingly, short-chain PFAS also showed strong binding capacity, and the interaction energy for some was as close to the longer-chain PFAS. This suggests that short-chain PFASs are not completely safe, and their use and build-up in the environment should be carefully regulated. Of note, TTR homologs analysis suggests that thyroid-disrupting effects of PFASs could be most likely translated to TTR-like proteins and other species.

Identification of a Chemotherapeutic Lead Molecule for the Potential Disruption of the FAM72A-UNG2 Interaction to Interfere with Genome Stability, Centromere Formation, and Genome Editing.

Family with sequence similarity 72 A (FAM72A) is a pivotal mitosis-promoting factor that is highly expressed in various types of cancer. FAM72A interacts with the uracil-DNA glycosylase UNG2 to prevent mutagenesis by eliminating uracil from DNA molecules through cleaving the N-glycosylic bond and initiating the base excision repair pathway, thus maintaining genome integrity. In the present study, we determined a specific FAM72A-UNG2 heterodimer protein interaction using molecular docking and dynamics. In addition, through in silico screening, we identified withaferin B as a molecule that can specifically prevent the FAM72A-UNG2 interaction by blocking its cell signaling pathways. Our results provide an excellent basis for possible therapeutic approaches in the clinical treatment of cancer.

An engineered cellobiohydrolase I for sustainable degradation of lignocellulosic biomass.

This study provides computational-assisted engineering of the cellobiohydrolase I (CBH-I) from Penicillium verruculosum with simultaneous enhanced thermostability and tolerance in ionic liquids, deep eutectic solvent, and concentrated seawater without affecting its wild-type activity. Engineered triple variant CBH-I R1 (A65R-G415R-S181F) showed 2.48-fold higher thermostability in terms of relative activity at 65°C after 1 h of incubation when compared with CBH-I wild type. CBH-I R1 exhibited 1.87-fold, 1.36-fold, and 1.57-fold higher specific activities compared with CBH-I wild type in [Bmim]Cl (50 g/L), [Ch]Cl (50 g/L), and two-fold concentrated seawater, respectively. In the multicellulases mixture, CBH-I R1 showed higher hydrolytic efficiency to hydrolyze aspen wood compared with CBH-I wild type in the buffer, [Bmim]Cl (50 g/L), and two-fold concentrated seawater, respectively. Structural analysis revealed a molecular basis for the higher stability of the CBH-I structure in which A65R and G415R substitutions form salt bridges (D64 … R65, E411 … R415) and S181F forms π-π interaction (Y155 … F181), leading to stabilize surface-exposed flexible α-helixes and loop in the multidomain ß-jelly roll fold structure, respectively. In conclusion, the variant CBH-I R1 could enable efficient lignocellulosic biomass degradation as a cost-effective alternative for the sustainable production of biofuels and value-added chemicals.

FAM72, Glioblastoma Multiforme (GBM) and Beyond.

Neural stem cells (NSCs) offer great potential for regenerative medicine due to their excellent ability to differentiate into various specialized cell types of the brain. In the central nervous system (CNS), NSC renewal and differentiation are under strict control by the regulation of the pivotal SLIT-ROBO Rho GTPase activating protein 2 (SRGAP2)-Family with sequence similarity 72 (FAM72) master gene (i.e., |-SRGAP2-FAM72-|) via a divergent gene transcription activation mechanism. If the gene transcription control unit (i.e., the intergenic region of the two sub-gene units, SRGAP2 and FAM72) gets out of control, NSCs may transform into cancer stem cells and generate brain tumor cells responsible for brain cancer such as glioblastoma multiforme (GBM). Here, we discuss the surveillance of this |-SRGAP2-FAM72-| master gene and its role in GBM, and also in light of FAM72 for diagnosing various types of cancers outside of the CNS.

Engineering Robust Cellulases for Tailored Lignocellulosic Degradation Cocktails.

Lignocellulosic biomass is a most promising feedstock in the production of second-generation biofuels. Efficient degradation of lignocellulosic biomass requires a synergistic action of several cellulases and hemicellulases. Cellulases depolymerize cellulose, the main polymer of the lignocellulosic biomass, to its building blocks. The production of cellulase cocktails has been widely explored, however, there are still some main challenges that enzymes need to overcome in order to develop a sustainable production of bioethanol. The main challenges include low activity, product inhibition, and the need to perform fine-tuning of a cellulase cocktail for each type of biomass. Protein engineering and directed evolution are powerful technologies to improve enzyme properties such as increased activity, decreased product inhibition, increased thermal stability, improved performance in non-conventional media, and pH stability, which will lead to a production of more efficient cocktails. In this review, we focus on recent advances in cellulase cocktail production, its current challenges, protein engineering as an efficient strategy to engineer cellulases, and our view on future prospects in the generation of tailored cellulases for biofuel production.

Proteomic Atomics Reveals a Distinctive Uracil-5-Methyltransferase.

Carbon (C), hydrogen (H), nitrogen (N), oxygen (O), and sulfur (S) atoms intrigue as they are the foundation for amino acid (AA) composition and the folding and functions of proteins and thus define and control the survival of a cell, the smallest unit of life. Here, we calculated the proteomic atom distribution in >1500 randomly selected species across the entire current phylogenetic tree and identified uracil-5-methyltransferase (U5MTase) of the protozoan parasite Plasmodium falciparum (Pf, strain Pf3D7), with a distinct atom and AA distribution pattern. We determined its apicoplast location and in silico 3D protein structure to refocus attention exclusively on U5MTase with tremendous potential for therapeutic intervention in malaria. Around 300 million clinical cases of malaria occur each year in tropical and subtropical regions of the world, resulting in over one million deaths annually, placing malaria among the most serious infectious diseases. Genomic and proteomic research of the clades of parasites containing Pf is progressing slowly and the functions of most of the ∼5300 genes are still unknown. We applied a 'bottom-up' comparative proteomic atomics analysis across the phylogenetic tree to visualize a protein molecule on its actual basis - i. e., its atomic level. We identified a protruding Pf3D7-specific U5MTase, determined its 3D protein structure, and identified potential inhibitory drug molecules through in silico drug screening that might serve as possible remedies for the treatment of malaria. Besides, this atomic-based proteome map provides a unique approach for the identification of parasite-specific proteins that could be considered as novel therapeutic targets.

Disulfide Bond Engineering of an Endoglucanase from Penicillium verruculosum to Improve Its Thermostability.

Endoglucanases (EGLs) are important components of multienzyme cocktails used in the production of a wide variety of fine and bulk chemicals from lignocellulosic feedstocks. However, a low thermostability and the loss of catalytic performance of EGLs at industrially required temperatures limit their commercial applications. A structure-based disulfide bond (DSB) engineering was carried out in order to improve the thermostability of EGLII from Penicillium verruculosum. Based on in silico prediction, two improved enzyme variants, S127C-A165C (DSB2) and Y171C-L201C (DSB3), were obtained. Both engineered enzymes displayed a 15⁻21% increase in specific activity against carboxymethylcellulose and ß-glucan compared to the wild-type EGLII (EGLII-wt). After incubation at 70 °C for 2 h, they retained 52⁻58% of their activity, while EGLII-wt retained only 38% of its activity. At 80 °C, the enzyme-engineered forms retained 15⁻22% of their activity after 2 h, whereas EGLII-wt was completely inactivated after the same incubation time. Molecular dynamics simulations revealed that the introduced DSB rigidified a global structure of DSB2 and DSB3 variants, thus enhancing their thermostability. In conclusion, this work provides an insight into DSB protein engineering as a potential rational design strategy that might be applicable for improving the stability of other enzymes for industrial applications.

Livebearing or egg-laying mammals: 27 decisive nucleotides of FAM168.

In the present study, we determine comprehensive molecular phylogenetic relationships of the novel myelin-associated neurite-outgrowth inhibitor (MANI) gene across the entire eukaryotic lineage. Combined computational genomic and proteomic sequence analyses revealed MANI as one of the two members of the novel family with sequence similarity 168 member (FAM168) genes, consisting of FAM168A and FAM168B, having distinct genetic differences that illustrate diversification in its biological function and genetic taxonomy across the phylogenetic tree. Phylogenetic analyses based on coding sequences of these FAM168 genes revealed that they are paralogs and that the earliest emergence of these genes occurred in jawed vertebrates such as Callorhinchus milii. Surprisingly, these two genes are absent in other chordates that have a notochord at some stage in their lives, such as branchiostoma and tunicates. In the context of phylogenetic relationships among eukaryotic species, our results demonstrate the presence of FAM168 orthologs in vertebrates ranging from Callorhinchus milii to Homo sapiens, displaying distinct taxonomic clusters, comprised of fish, amphibians, reptiles, birds, and mammals. Analyses of individual FAM168 exons in our sample provide new insights into the molecular relationships between FAM168A and FAM168B (MANI) on the one hand and livebearing and egg-laying mammals on the other hand, demonstrating that a distinctive intermediate exon 4, comprised of 27 nucleotides, appears suddenly only in FAM168A and there in the livebearing mammals only but is absent from all other species including the egg-laying mammals.

Neurotrophin Signaling and Stem Cells-Implications for Neurodegenerative Diseases and Stem Cell Therapy.

Neurotrophins (NTs) are members of a neuronal growth factor protein family whose action is mediated by the tropomyosin receptor kinase (TRK) receptor family receptors and the p75 NT receptor (p75NTR), a member of the tumor necrosis factor (TNF) receptor family. Although NTs were first discovered in neurons, recent studies have suggested that NTs and their receptors are expressed in various types of stem cells mediating pivotal signaling events in stem cell biology. The concept of stem cell therapy has already attracted much attention as a potential strategy for the treatment of neurodegenerative diseases (NDs). Strikingly, NTs, proNTs, and their receptors are gaining interest as key regulators of stem cells differentiation, survival, self-renewal, plasticity, and migration. In this review, we elaborate the recent progress in understanding of NTs and their action on various stem cells. First, we provide current knowledge of NTs, proNTs, and their receptor isoforms and signaling pathways. Subsequently, we describe recent advances in the understanding of NT activities in various stem cells and their role in NDs, particularly Alzheimer's disease (AD) and Parkinson's disease (PD). Finally, we compile the implications of NTs and stem cells from a clinical perspective and discuss the challenges with regard to transplantation therapy for treatment of AD and PD.

3D Structure, Dimerization Modeling, and Lead Discovery by Ligand-protein Interaction Analysis of p60 Transcription Regulator Protein (p60TRP).

The p60 transcription regulator protein (p60TRP) is a basic helix-loop-helix (bHLH) domain-containing neuroprotective protein and a member of the G-protein-coupled receptor (GPCR)-associated sorting protein (GPRASP) family. In the present study, multiple theoretical physico-chemical methods (e.g. Modeller v.9.13, I-TASSER, PROCHECK and ClusPro v2.0 with PIPER) were applied to unveil the three-dimensional (3D) protein structure of the p60TRP homo-dimer protein and explore potential ligand-protein interactions. Our results suggest a Mg(2+) -containing 3D p60TRP dimer protein that potentially interacts with 5-(1-aziridinyl)-2,4-dinitrobenzamide (CB1954) and [2-(3-dodecylimidazolidin-1-yl)-1-phosphonoethyl] phosphonic acid (B73). The discovery of CB1954 and B73 may serve as a potential lead for further drug screening tests to normalize the p60TRP signaling pathway in neurodegenerative diseases. Interference with p60TRP signaling via CB1954/B73-related molecules might be a novel option for modifying neurodegenerative signaling pathways (e.g. RIN1, PP2A, RanBP5, CREB and SYNJ1) to treat various brain diseases.

All-or-(N)One - an epistemological characterization of the human tumorigenic neuronal paralogous FAM72 gene loci.

FAM72 is a novel neuronal progenitor cell (NPC) self-renewal supporting protein expressed under physiological conditions at low levels in other tissues. Accumulating data indicate the potential pivotal tumourigenic effects of FAM72. Our in silico human genome-wide analysis (GWA) revealed that the FAM72 gene family consists of four human-specific paralogous members, all of which are located on chromosome (chr) 1. Unique asymmetric FAM72 segmental gene duplications are most likely to have occurred in conjunction with the paired genomic neighbour SRGAP2 (SLIT-ROBO Rho GTPase activating protein), as both genes have four paralogues in humans but only one vertebra-emerging orthologue in all other species. No species with two or three FAM72/SRGAP2 gene pairs could be identified, and the four exclusively human-defining ohnologues, with different mutation patterns in Homo neanderthalensis and Denisova hominin, may remain under epigenetic control through long non-coding (lnc) RNAs.

Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17).

FAM72A (p17) is a novel neuronal protein that has been linked to tumorigenic effects in non-neuronal tissue. Using state of the art in silico physicochemical analyses (e.g., I-TASSER, RaptorX, and Modeller), we determined the three-dimensional (3D) protein structure of FAM72A and further identified potential ligand-protein interactions. Our data indicate a Zn(2+)/Fe(3+)-containing 3D protein structure, based on a 3GA3_A model template, which potentially interacts with the organic molecule RSM ((2s)-2-(acetylamino)-N-methyl-4-[(R)-methylsulfinyl] butanamide). The discovery of RSM may serve as potential lead for further anti-FAM72A drug screening tests in the pharmaceutical industry because interference with FAM72A's activities via RSM-related molecules might be a novel option to influence the tumor suppressor protein p53 signaling pathways for the treatment of various types of cancers.

Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches.

Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.

Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli.

Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool "PaDEL-Descriptor".

Predictive regression-based models for bioconcentration factor (BCF) have been developed using mechanistically interpretable descriptors computed from open source tool PaDEL-Descriptor ( http://padel.nus.edu.sg/software/padeldescriptor/ ). A data set of 522 diverse chemicals has been used for this modeling study, and extended topochemical atom (ETA) indices developed by the present authors' group were chosen as the descriptors. Due to the importance of lipohilicity in modeling BCF, XLogP (computed partition coefficient) was also tried as an additional descriptor. Genetic function approximation followed by multiple linear regression algorithm was applied to select descriptors, and subsequent partial least squares analyses were performed to establish mathematical equations for BCF prediction. The model generated from only ETA indices shows importance of seven descriptors in model development, while the model generated from ETA descriptors along with XlogP shows importance of four descriptors in model development. In general, BCF depends on lipophilicity, presence of heteroatoms, presence of halogens, fused ring system, hydrogen bonding groups, etc. The developed models show excellent statistical qualities and predictive ability. The developed models were used also for prediction of an external data set available from the literature, and good quality of predictions (R (2) pred = 0.812 and 0.826) was demonstrated. Thus, BCF can be predicted using ETA and XlogP descriptors calculated from open source PaDEL-Descriptor software in the context of aquatic chemical toxicity management.

Environmental toxicological fate prediction of diverse organic chemicals based on steady-state compartmental chemical mass ratio using quantitative structure-fate relationship (QSFR) models.

Four quantitative prediction models for steady-state compartmental chemical mass concentrations (Wn,g) were obtained from structural information, physiochemical properties, degradation rate and transport coefficients of 455 diverse organic chemicals using chemometric tools in a quantitative structure-fate relationship (QSFR) study. The mass ratio assessment of environmentally prevalent organic chemicals may be helpful to predict their toxicological fate in the ecosystems. Four sets of mass ratios [(1) log(Wair) from water emissions (water to air compartment), (2) log(Wair) from air emissions (within different zones of the air compartment), (3) log(Wwater) from water emissions (within different zones of the water compartment) and (4) log(Wwater) from air emissions (air to water compartment)] have been used. The developed models using genetic function approximation followed by multiple linear regression (GFA-MLR) and subsequent partial least squares (PLS) treatment identify only four descriptors for log(Wair) from water emission, six descriptors for log(Wair) from air emission, five descriptors for log(Wwater) from water emission and seven descriptors for log(Wwater) from air emission for predicting efficiently a large number of test set chemicals (ntest=182). The conclusive models suggest that descriptors such as partition coefficients (Kaw, Kow and Ksw), degradation parameters (Ksoil,Kwater and Kair), vapor pressure (Pv), diffusivity (Dwater), spatial descriptors (Jurs-WNSA-1, Jurs-WNSA-2, Jurs-WPSA-3, Jurs-FNSA-3 and Density), thermodynamic descriptors (MolRef and AlogP98), electrotopological state indices (S_dsN, S_ssNH and S_dsCH) are important for predicting the chemical mass ratios. The developed models may be applicable in toxicological fate prediction of diverse chemicals in the ecosystems.