Validation and Estimation of Obesity-Induced Intervertebral Disc Degeneration through Subject-Specific Finite Element Modelling of Functional Spinal Units.

(1) Background: Intervertebral disc degeneration has been linked to obesity; its potential mechanical effects on the intervertebral disc remain unknown. This study aimed to develop and validate a patient-specific model of L3-L4 vertebrae and then use the model to estimate the impact of increasing body weight on disc degeneration. (2) Methods: A three-dimensional model of the functional spinal unit of L3-L4 vertebrae and its components were developed and validated. Validation was achieved by comparing the range of motions (RoM) and intradiscal pressures with the previous literature. Subsequently, the validated model was loaded according to the body mass index and estimated stress, deformation, and RoM to assess disc degeneration. (3) Results: During validation, L3-L4 RoM and intradiscal pressures: flexion 5.17° and 1.04 MPa, extension 1.54° and 0.22 MPa, lateral bending 3.36° and 0.54 MPa, axial rotation 1.14° and 0.52 MPa, respectively. When investigating the impact of weight on disc degeneration, escalating from normal weight to obesity reveals an increased RoM, by 3.44% during flexion, 22.7% during extension, 29.71% during lateral bending, and 33.2% during axial rotation, respectively. Also, stress and disc deformation elevated with increasing weight across all RoM. (4) Conclusions: The predicted mechanical responses of the developed model closely matched the validation dataset. The validated model predicts disc degeneration under increased weight and could lay the foundation for future recommendations aimed at identifying predictors of lower back pain due to disc degeneration.

The heparin-binding hemagglutinin protein of Mycobacterium tuberculosis is a nucleoid-associated protein.

Nucleoid-associated proteins (NAPs) regulate multiple cellular processes such as gene expression, virulence, and dormancy throughout bacterial species. NAPs help in the survival and adaptation of Mycobacterium tuberculosis (Mtb) within the host. Fourteen NAPs have been identified in Escherichia coli; however, only seven NAPs are documented in Mtb. Given its complex lifestyle, it is reasonable to assume that Mtb would encode for more NAPs. Using bioinformatics tools and biochemical experiments, we have identified the heparin-binding hemagglutinin (HbhA) protein of Mtb as a novel sequence-independent DNA-binding protein which has previously been characterized as an adhesion molecule required for extrapulmonary dissemination. Deleting the carboxy-terminal domain of HbhA resulted in a complete loss of its DNA-binding activity. Atomic force microscopy showed HbhA-mediated architectural modulations in the DNA, which may play a regulatory role in transcription and genome organization. Our results showed that HbhA colocalizes with the nucleoid region of Mtb. Transcriptomics analyses of a hbhA KO strain revealed that it regulates the expression of ∼36% of total and ∼29% of essential genes. Deletion of hbhA resulted in the upregulation of ∼73% of all differentially expressed genes, belonging to multiple pathways suggesting it to be a global repressor. The results show that HbhA is a nonessential NAP regulating gene expression globally and acting as a plausible transcriptional repressor.

The unique N-terminal region of Mycobacterium tuberculosis sigma factor A plays a dominant role in the essential function of this protein.

SigA (σA) is an essential protein and the primary sigma factor in Mycobacterium tuberculosis (Mtb). However, due to the absence of genetic tools, our understanding of the role and regulation of σA activity and its molecular attributes that help modulate Mtb survival is scant. Here, we generated a conditional gene replacement of σA in Mtb and showed that its depletion results in a severe survival defect in vitro, ex vivo, and in vivo in a murine infection model. Our RNA-seq analysis suggests that σA either directly or indirectly regulates ∼57% of the Mtb transcriptome, including ∼28% of essential genes. Surprisingly, we note that despite having ∼64% similarity with σA, overexpression of the primary-like σ factor SigB (σB) fails to compensate for the absence of σA, suggesting minimal functional redundancy. RNA-seq analysis of the Mtb σB deletion mutant revealed that 433 genes are regulated by σB, of which 283 overlap with the σA transcriptome. Additionally, surface plasmon resonance, in vitro transcription, and functional complementation experiments reveal that σA residues between 132-179 that are disordered and missing from all experimentally determined σA-RNAP structural models are imperative for σA function. Moreover, phosphorylation of σA in the intrinsically disordered N-terminal region plays a regulatory role in modulating its activity. Collectively, these observations and analysis provide a rationale for the centrality of σA for the survival and pathogenicity of this bacillus.

SARS-CoV-2 Variant Delta Potently Suppresses Innate Immune Response and Evades Interferon-Activated Antiviral Responses in Human Colon Epithelial Cells.

The Delta variant of SARS-CoV-2 has caused more severe infections than its previous variants. We studied the host innate immune response to Delta, Alpha, and two earlier variants to map the evolution of the recent ones. Our biochemical and transcriptomic studies in human colon epithelial cell line Caco2 reveal that Alpha and Delta have progressively evolved over the ancestral variants by silencing the innate immune response, thereby limiting cytokine and chemokine production. Though Alpha silenced the retinoic acid-inducible gene (RIG)-I-like receptor (RLR) pathway just like Delta did, it failed to persistently silence the innate immune response, unlike Delta. Both Alpha and Delta have evolved to resist interferon (IFN) treatment, while they are still susceptible to RLR activation, further highlighting the importance of RLR-mediated, IFN-independent mechanisms in restricting SARS-CoV-2. Our studies reveal that SARS-CoV-2 Delta has integrated multiple mechanisms to silence the host innate immune response and evade the IFN response. We speculate that Delta's silent replication and sustained suppression of the host innate immune response, thereby resulting in delayed or reduced intervention by the adaptive immune response, could have potentially contributed to the severe symptoms and poor recovery index associated with it. It is likely that this altered association with the host would play an important role in the coevolution of SARS-CoV-2 with humans. IMPORTANCE Viruses generally learn to coexist with the host during the process of evolution. It is expected that SARS-CoV-2 would also evolve to coexist in humans by trading off its virulence for longer persistence, causing milder disease. Clinically, the fatality associated with COVID-19 has been declining due to vaccination and preinfections, but the Delta variant caused the most severe disease and fatality across several parts of the world. Our study identified an evolving trend of SARS-CoV-2 variants where the variants that emerged during early parts of the pandemic caused a more robust innate immune response, while the later emerging variant Delta showed features of suppression of the response. The features that Delta has acquired could have strongly influenced the distinct pathophysiology associated with its infection. How these changed associations with the host influence the long-term evolution of the virus and the disease outcome should be closely studied to understand the process of viral evolution.

Epidemiological and Clinical Characteristics of Adults with Coronavirus Disease 2019 Complicated with Pneumothorax.

Background: The major brunt of coronavirus disease-2019 (COVID-2019) is borne by the lungs. The major cause of morbidity and mortality in COVID-19 patients is a compromise of the respiratory system. Pneumothorax is noted as an insignificant proportion of patients suffering from COVID-19, but it jeopardizes the clinical recovery significantly. We, in the case series of 10 patients, will be summarizing the epidemiological, demographic, and clinical characteristics of COVID-19 patients who also developed pneumothorax. Patients and methods: All the confirmed cases of COVID-19 pneumonia diagnosed between May 1, 2020 and August 30, 2020, admitted at our center meeting the inclusion criteria and whose clinical course was complicated by pneumothorax were made part of our study. Their clinical records were studied, and epidemiological, demographic, and clinical data of these patients were collected and compiled in this case series. Results: All the patients in our study required ICU care, and 60% received non-invasive mechanical ventilation, while 40% of the patients progressed to intubation and invasive mechanical ventilation. A total of 70% of the patients in our study had a successful outcome, while 30% succumbed to the disease and expired. Conclusion: Epidemiological, demographic, and clinical characteristics of COVID-19 patients complicated with pneumothorax were evaluated. Our study showed that pneumothorax also developed in some patients who had not received mechanical ventilation, indicating that pneumothorax could be a secondary complication of SARS-CoV-2 infection. Our study also emphasizes the fact that even the majority of patients whose clinical course was complicated by pneumothorax had a successful outcome emphasizing the need for timely and adequate intervention in such cases. How to cite this article: Singh NK. Epidemiological and Clinical Characteristics of Adults with Coronavirus Disease 2019 Complicated with Pneumothorax. Indian J Crit Care Med 2022;26(7):833-835.

Analysis of the Differential Gene and Protein Expression Profiles of Corneal Epithelial Cells Stimulated with Alternating Current Electric Fields.

In cells, intrinsic endogenous direct current (DC) electric fields (EFs) serve as morphogenetic cues and are necessary for several important cellular responses including activation of multiple signaling pathways, cell migration, tissue regeneration and wound healing. Endogenous DC EFs, generated spontaneously following injury in physiological conditions, directly correlate with wound healing rate, and different cell types respond to these EFs via directional orientation and migration. Application of external DC EFs results in electrode polarity and is known to activate intracellular signaling events in specific direction. In contrast, alternating current (AC) EFs are known to induce continuous bidirectional flow of charged particles without electrode polarity and also minimize electrode corrosion. In this context, the present study is designed to study effects of AC EFs on corneal epithelial cell gene and protein expression profiles in vitro. We performed gene and antibody arrays, analyzed the data to study specific influence of AC EFs, and report that AC EFs has no deleterious effect on epithelial cell function. Gene Ontology results, following gene and protein array data analysis, showed that AC EFs influence similar biological processes that are predominantly responsive to organic substance, chemical, or external stimuli. Both arrays activate cytokine-cytokine receptor interaction, MAPK and IL-17 signaling pathways. Further, in comparison to the gene array data, the protein array data show enrichment of diverse activated signaling pathways through several interconnecting networks.

Downregulation of the FTO m6A RNA demethylase promotes EMT-mediated progression of epithelial tumors and sensitivity to Wnt inhibitors.

Post-transcriptional modifications of RNA constitute an emerging regulatory layer of gene expression. The demethylase fat mass- and obesity-associated protein (FTO), an eraser of N6-methyladenosine (m6A), has been shown to play a role in cancer, but its contribution to tumor progression and the underlying mechanisms remain unclear. Here, we report widespread FTO downregulation in epithelial cancers associated with increased invasion, metastasis and worse clinical outcome. Both in vitro and in vivo, FTO silencing promotes cancer growth, cell motility and invasion. In human-derived tumor xenografts (PDXs), FTO pharmacological inhibition favors tumorigenesis. Mechanistically, we demonstrate that FTO depletion elicits an epithelial-to-mesenchymal transition (EMT) program through increased m6A and altered 3'-end processing of key mRNAs along the Wnt signaling cascade. Accordingly, FTO knockdown acts via EMT to sensitize mouse xenografts to Wnt inhibition. We thus identify FTO as a key regulator, across epithelial cancers, of Wnt-triggered EMT and tumor progression and reveal a therapeutically exploitable vulnerability of FTO-low tumors.

The Fungal Histone Acetyl Transferase Gcn5 Controls Virulence of the Human Pathogen Candida albicans through Multiple Pathways.

Fungal virulence is regulated by a tight interplay of transcriptional control and chromatin remodelling. Despite compelling evidence that lysine acetylation modulates virulence of pathogenic fungi such as Candida albicans, the underlying mechanisms have remained largely unexplored. We report here that Gcn5, a paradigm lysyl-acetyl transferase (KAT) modifying both histone and non-histone targets, controls fungal morphogenesis - a key virulence factor of C. albicans. Our data show that genetic removal of GCN5 abrogates fungal virulence in mice, suggesting strongly diminished fungal fitness in vivo. This may at least in part arise from increased susceptibility to killing by macrophages, as well as by other phagocytes such as neutrophils or monocytes. Loss of GCN5 also causes hypersensitivity to the fungicidal drug caspofungin. Caspofungin hypersusceptibility requires the master regulator Efg1, working in concert with Gcn5. Moreover, Gcn5 regulates multiple independent pathways, including adhesion, cell wall-mediated MAP kinase signaling, hypersensitivity to host-derived oxidative stress, and regulation of the Fks1 glucan synthase, all of which play critical roles in virulence and antifungal susceptibility. Hence, Gcn5 regulates fungal virulence through multiple mechanisms, suggesting that specific inhibition of Gcn5 could offer new therapeutic strategies to combat invasive fungal infections.

TET2-Dependent Hydroxymethylome Plasticity Reduces Melanoma Initiation and Progression.

Although numerous epigenetic aberrancies accumulate in melanoma, their contribution to initiation and progression remain unclear. The epigenetic mark 5-hydroxymethylcytosine (5hmC), generated through TET-mediated DNA modification, is now referred to as the sixth base of DNA and has recently been reported as a potential biomarker for multiple types of cancer. Loss of 5hmC is an epigenetic hallmark of melanoma, but whether a decrease in 5hmc levels contributes directly to pathogenesis or whether it merely results from disease progression-associated epigenetic remodeling remains to be established. Here, we show that NRAS-driven melanomagenesis in mice is accompanied by an overall decrease in 5hmC and specific 5hmC gains in selected gene bodies. Strikingly, genetic ablation of Tet2 in mice cooperated with oncogenic NRASQ61K to promote melanoma initiation while suppressing specific gains in 5hmC. We conclude that TET2 acts as a barrier to melanoma initiation and progression, partly by promoting 5hmC gains in specific gene bodies. SIGNIFICANCE: This work emphasizes the importance of epigenome plasticity in cancer development and highlights the involvement of druggable epigenetic factors in cancer.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Identifying genes relevant to specific biological conditions in time course microarray experiments.

Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call "relative Signal-to-Noise ratio" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

Derivation of an interaction/regulation network describing pluripotency in human.

Identification of the key genes/proteins of pluripotency and their interrelationships is an important step in understanding the induction and maintenance of pluripotency. Experimental approaches have accumulated large amounts of interaction/regulation data in mouse. We investigate how far such information can be transferred to human, the species of maximum interest, for which experimental data are much more limited. To address this issue, we mapped an existing mouse pluripotency network (the PluriNetWork) to human. We transferred interaction and regulation links between genes/proteins from mouse to human on the basis of orthologous relationship of the genes/proteins (called interolog mapping). To reduce the number of false positives, we used four different methods: phylogenetic profiling, Gene Ontology semantic similarity, gene co-expression, and RNA interference (RNAi) data. The methods and the resulting networks were evaluated by a novel approach using the information about the genes known to be involved in pluripotency from the literature. The RNAi method proved best for filtering out unlikely interactions, so it was used to construct the final human pluripotency network. The RNAi data are based on human embryonic stem cells (hESCs) that are generally considered to be in a (primed) epiblast stem cell state. Therefore, we assume that the final human network may reflect the (primed) epiblast stem cell state more closely, while the mouse network reflects the (unprimed/naïve) embryonic stem cell state more closely.

TMBHMM: a frequency profile based HMM for predicting the topology of transmembrane beta barrel proteins and the exposure status of transmembrane residues.

Transmembrane beta barrel (TMB) proteins are found in the outer membranes of bacteria, mitochondria and chloroplasts. TMBs are involved in a variety of functions such as mediating flux of metabolites and active transport of siderophores, enzymes and structural proteins, and in the translocation across or insertion into membranes. We present here TMBHMM, a computational method based on a hidden Markov model for predicting the structural topology of putative TMBs from sequence. In addition to predicting transmembrane strands, TMBHMM also predicts the exposure status (i.e., exposed to the membrane or hidden in the protein structure) of the residues in the transmembrane region, which is a novel feature of the TMBHMM method. Furthermore, TMBHMM can also predict the membrane residues that are not part of beta barrel forming strands. The training of the TMBHMM was performed on a non-redundant data set of 19 TMBs. The self-consistency test yielded Q(2) accuracy of 0.87, Q(3) accuracy of 0.83, Matthews correlation coefficient of 0.74 and SOV for beta strand of 0.95. In this self-consistency test the method predicted 83% of transmembrane residues with correct exposure status. On an unseen, non-redundant test data set of 10 proteins, the 2-state and 3-state TMBHMM prediction accuracies are around 73% and 72%, respectively, and are comparable to other methods from the literature. The TMBHMM web server takes an amino acid sequence or a multiple sequence alignment as an input and predicts the exposure status and the structural topology as output. The TMBHMM web server is available under the tmbhmm tab at: http://service.bioinformatik.uni-saarland.de/tmx-site/.

T-iDT : tool for identification of drug target in bacteria and validation by Mycobacterium tuberculosis.

With the completion of the Human Genome Project in 2003, many new projects to sequence bacterial genomes were started and soon many complete bacterial genome sequences were available. The sequenced genomes of pathogenic bacteria provide useful information for understanding host-pathogen interactions. These data prove to be a new weapon in fighting against pathogenic bacteria by providing information about potential drug targets. But the limitation of computational tools for finding potential drug targets has hindered the process and further experimental analysis. There are many in silico approaches proposed for finding drug targets but only few have been automated. One such approach finds essential genes in bacterial genomes with no human homologue and predicts these as potential drug targets. The same approach is used in our tool. T-iDT, a tool for the identification of drug targets, finds essential genes by comparing a bacterial gene set against DEG (Database of Essential Genes) and excludes homologue genes by comparing against a human protein database. The tool predicts both the set of essential genes as well as potential target genes for the given genome. The tool was tested with Mycobacterium tuberculosis and results were validated. With default parameters, the tool predicted 236 essential genes and 52 genes to encode potential drug targets. A pathway-based approach was used to validate these potential drug target genes. The pathway in which the products of these genes are involved was determined. Our analysis shows that almost all these pathways are very essential for the bacterial survival and hence these genes encode possible drug targets. Our tool provides a fast method for finding possible drug targets in bacterial genomes with varying stringency level. The tool will be helpful in finding possible drug targets in various pathogenic organisms and can be used for further analysis in novel therapeutic drug development. The tool can be downloaded from http://www.milser.co.in/research.htm and http://www.srmbioinformatics.edu.in/ forum.htm.