Inhibition of leukotriene-B4 signalling-mediated host response to tuberculosis is a potential mode of adjunctive host-directed therapy

Treatment of tuberculosis (TB) is faced with several challenges including the long treatment duration, drug toxicity and tissue pathology. Host-directed therapy provides promising avenues to find compounds for adjunctively assisting antimycobacterials in the TB treatment regimen, by promoting pathogen eradication or limiting tissue destruction. Eicosanoids are a class of lipid molecules that are potent mediators of inflammation and have been implicated in aspects of the host response against TB. Here, we have explored the blood transcriptome of pulmonary TB patients to understand the activity of leukotriene B4, a pro-inflammatory eicosanoid. Our study shows a significant upregulation in the leukotriene B4 signalling pathway in active TB patients, which is reversed with TB treatment. We have further utilized our in-house network analysis algorithm, ResponseNet, to identify potential downstream signal effectors of leukotriene B4 in TB patients including STAT1/2 and NADPH oxidase at a systemic as well as local level, followed by experimental validation of the same. Finally, we show the potential of inhibiting leukotriene B4 signalling as a mode of adjunctive host-directed therapy against TB. This study provides a new mode of TB treatment along with mechanistic insights which can be further explored in pre-clinical trials.

CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands

While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.

Prevalence and Clinical Significance of Electrocardiographic Complete Right Bundle Branch Block in Young Individuals

BACKGROUND AND AIMS

There is limited information on the clinical significance of complete right bundle branch block (CRBBB) in young individuals. The aim of this study was to determine the prevalence and significance of CRBBB in a large cohort of young individuals aged 14-35 years old.

METHODS

From 2008 to 2018, 104,369 consecutive individuals underwent a cardiovascular assessment with a health questionnaire, electrocardiogram, clinical consultation, and selective echocardiography. Follow-up was obtained via direct telephone consultations. Mean follow-up was 7.3 ± 2.7 years.

RESULTS

CRBBB was identified in 154 (0.1%) individuals and was more prevalent in males compared with females (0.20% vs. 0.06%; p<0.05) and in athletes compared with non-athletes (0.25% vs. 0.14%; p<0.05). CRBBB-related cardiac conditions were identified in 7 (5%) individuals (4 with atrial septal defect, 1 with Brugada syndrome, 1 with progressive cardiac conduction disease and 1 with atrial fibrillation). Pathology was more frequently identified in individuals with non-isolated CRBBB compared with individuals with isolated CRBBB (14% vs 1%; p < 0.05) and in individuals with a QRS duration of ≥130 milliseconds (ms) compared with individuals with a QRS of <130ms (10% vs 1%; p<0.05).

CONCLUSION

The prevalence of CRBBB in young individuals was 0.1% and was more prevalent in males and athletes. CRBBB-related conditions were identified in 5% of individuals and were more common in individuals with non-isolated CRBBB and more pronounced intraventricular conduction delay (QRS duration of ≥130ms). Secondary evaluation should be considered for young individuals with CRBBB with symptoms, concerning family history, additional electrocardiographic anomalies or significant QRS prolongation (≥130ms).

A novel stop-loss mutation in NKX2-2 gene as a cause of neonatal diabetes mellitus: molecular characterization and structural analysis

AIM

To identify the genetic etiology of neonatal diabetes in an infant and to elucidate the molecular mechanism of the identified mutation underlying the pathogenesis.

METHODS

Genetic analysis was carried out by sequencing of known etiological genes associated with NDM. Molecular characterization was performed by constructing a identified mutation in NKX2-2 gene and  functional aspects was tested using transactivation, protein expression, DNA binding, nuclear localization assays. Structural analysis was performed by modeling the NKX2-2 protein structure.

RESULTS

A novel homozygous frameshift mutation  c.772delC, p.Q258SFs*59 in the NKX2-2 gene was identified in a patient with neonatal diabetes. Functional studies revealed that this mutation resulted in an elongated protein sequence, affecting DNA binding activity and transcriptional function. Structural analysis suggested alterations in the protein's tertiary structure, likely contributing to its dysfunction.

CONCLUSION

This study presents the first report of a stop-loss mutation in the NKX2-2 gene associated with NDM. Our findings emphasize the importance of functional and structural characterization to understand the biological consequences of such mutations. This comprehensive analysis provides insights into the molecular mechanisms underlying NDM and its clinical phenotype, which may aid in better diagnosis and management of patients with similar variants in the future.

Cysteine desulfurase (IscS)-mediated fine-tuning of bioenergetics and SUF expression prevents Mycobacterium tuberculosis hypervirulence

Iron-sulfur (Fe-S) biogenesis requires multiprotein assembly systems, SUF and ISC, in most prokaryotes. M. tuberculosis (Mtb) encodes a complete SUF system, the depletion of which was bactericidal. The ISC operon is truncated to a single gene iscS (cysteine desulfurase), whose function remains uncertain. Here, we show that MtbΔiscS is bioenergetically deficient and hypersensitive to oxidative stress, antibiotics, and hypoxia. MtbΔiscS resisted killing by nitric oxide (NO). RNA sequencing indicates that IscS is important for expressing regulons of DosR and Fe-S-containing transcription factors, WhiB3 and SufR. Unlike wild-type Mtb, MtbΔiscS could not enter a stable persistent state, continued replicating in mice, and showed hypervirulence. The suf operon was overexpressed in MtbΔiscS during infection in a NO-dependent manner. Suppressing suf expression in MtbΔiscS either by CRISPR interference or upon infection in inducible NO-deficient mice arrests hypervirulence. Together, Mtb redesigned the ISC system to "fine-tune" the expression of SUF machinery for establishing persistence without causing detrimental disease in the host.

Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase

S-adenosylmethionine (SAM) is a ubiquitous co-factor that serves as a donor for methylation reactions and additionally serves as a donor of other functional groups such as amino and ribosyl moieties in a variety of other biochemical reactions. Such versatility in function is enabled by the ability of SAM to be recognized by a wide variety of protein molecules that vary in their sequences and structural folds. To understand what gives rise to specific SAM binding in diverse proteins, we set out to study if there are any structural patterns at their binding sites. A comprehensive analysis of structures of the binding sites of SAM by all-pair comparison and clustering, indicated the presence of 4 different site-types, only one among them being well studied. For each site-type we decipher the common minimum principle involved in SAM recognition by diverse proteins and derive structural motifs that are characteristic of SAM binding. The presence of the structural motifs with precise three-dimensional arrangement of amino acids in SAM sites that appear to have evolved independently, indicates that these are winning arrangements of residues to bring about SAM recognition. Further, we find high similarity between one of the SAM site types and a well known ATP binding site type. We demonstrate using in vitro experiments that a known SAM binding protein, HpyAII.M1, a type 2 methyltransferase can bind and hydrolyse ATP. We find common structural motifs that explain this, further supported through site-directed mutagenesis. Observation of similar motifs for binding two of the most ubiquitous ligands in multiple protein families with diverse sequences and structural folds presents compelling evidence at the molecular level in favour of convergent evolution.

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein-protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. We further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

Pressured cytotoxic T cell epitope strength among SARS-CoV-2 variants correlates with COVID-19 severity

Heterogeneity in susceptibility among individuals to COVID-19 has been evident through the pandemic worldwide. Cytotoxic T lymphocyte (CTL) responses generated against pathogens in certain individuals are known to impose selection pressure on the pathogen, thus driving emergence of new variants. In this study, we probe the role played by host genetic heterogeneity in terms of HLA-genotypes in determining differential COVID-19 severity in patients. We use bioinformatic tools for CTL epitope prediction to identify epitopes under immune pressure. Using HLA-genotype data of COVID-19 patients from a local cohort, we observe that the recognition of pressured epitopes from the parent strain Wuhan-Hu-1 correlates with COVID-19 severity. We also identify and rank list HLA-alleles and epitopes that offer protectivity against severe disease in infected individuals. Finally, we shortlist a set of 6 pressured and protective epitopes that represent regions in the viral proteome that are under high immune pressure across SARS-CoV-2 variants. Identification of such epitopes, defined by the distribution of HLA-genotypes among members of a population, could potentially aid in prediction of indigenous variants of SARS-CoV-2 and other pathogens.

Biosensor-integrated transposon mutagenesis reveals rv0158 as a coordinator of redox homeostasis in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is evolutionarily equipped to resist exogenous reactive oxygen species (ROS) but shows vulnerability to an increase in endogenous ROS (eROS). Since eROS is an unavoidable consequence of aerobic metabolism, understanding how Mtb manages eROS levels is essential yet needs to be characterized. By combining the Mrx1-roGFP2 redox biosensor with transposon mutagenesis, we identified 368 genes (redoxosome) responsible for maintaining homeostatic levels of eROS in Mtb. Integrating redoxosome with a global network of transcriptional regulators revealed a hypothetical protein (Rv0158) as a critical node managing eROS in Mtb. Disruption of rv0158 (rv0158 KO) impaired growth, redox balance, respiration, and metabolism of Mtb on glucose but not on fatty acids. Importantly, rv0158 KO exhibited enhanced growth on propionate, and the Rv0158 protein directly binds to methylmalonyl-CoA, a key intermediate in propionate catabolism. Metabolite profiling, ChIP-Seq, and gene-expression analyses indicate that Rv0158 manages metabolic neutralization of propionate toxicity by regulating the methylcitrate cycle. Disruption of rv0158 enhanced the sensitivity of Mtb to oxidative stress, nitric oxide, and anti-TB drugs. Lastly, rv0158 KO showed poor survival in macrophages and persistence defect in mice. Our results suggest that Rv0158 is a metabolic integrator for carbon metabolism and redox balance in Mtb.

Systems-level profiling of early peripheral host-response landscape variations across COVID-19 severity states in an Indian cohort

Host immune response to COVID-19 plays a significant role in regulating disease severity. Although big data analysis has provided significant insights into the host biology of COVID-19 across the world, very few such studies have been performed in the Indian population. This study utilizes a transcriptome-integrated network analysis approach to compare the immune responses between asymptomatic or mild and moderate-severe COVID-19 patients in an Indian cohort. An immune suppression phenotype is observed in the early stages of moderate-severe COVID-19 manifestation. A number of pathways are identified that play crucial roles in the host control of the disease such as the type I interferon response and classical complement pathway which show different activity levels across the severity spectrum. This study also identifies two transcription factors, IRF7 and ESR1, to be important in regulating the severity of COVID-19. Overall this study provides a deep understanding of the peripheral immune landscape in the COVID-19 severity spectrum in the Indian genetic background and opens up future research avenues to compare immune responses across global populations.

Salmonella Typhimurium PgtE is an essential arsenal to defend against the host resident antimicrobial peptides

Salmonella enterica serovar Typhimurium is a common cause of gastroenteritis in humans and occasionally causes systemic infection. Salmonella's ability to survive and replicate within macrophages is an important characteristic during systemic infection. The outer membrane protease PgtE of S. enterica is a member of the Omptin family of outer membrane aspartate proteases which has well-characterized proteolytic activities in-vitro against a wide range of physiologically relevant substrates. However, no study has been done so far that draws a direct correlation between these in-vitro observations and the biology of the pathogen in-vivo. The main goals of this study were to characterize the pathogenesis-associated functions of pgtE and study its role in the intracellular survival and in-vivo virulence of Salmonella Typhimurium. Our study elucidated a possible role of Salmonella Typhimurium pgtE in combating host antimicrobial peptide- bactericidal/ permeability increasing protein (BPI) to survive in human macrophages. The pgtE-deficient strain of Salmonella showed attenuated proliferation and enhanced colocalization with BPI in U937 and Thp1 cells. In the presence of polymixin B, the attenuated in-vitro survival of STM ΔpgtE suggested a role of PgtE against the antimicrobial peptides. In addition, our study revealed that compared to the wild type Salmonella, the pgtE mutant is replication-deficient in C57BL/6 mice. Further, we showed that PgtE interacts directly with several antimicrobial peptides (AMPs) in the host gut. This gives the pathogen a survival advantage and helps to mount a successful infection in the host.

Identification of Probucol as a candidate for combination therapy with Metformin for Type 2 diabetes

Type 2 Diabetes (T2D) is often managed with metformin as the drug of choice. While it is effective overall, many patients progress to exhibit complications. Strategic drug combinations to tackle this problem would be useful. We constructed a genome-wide protein-protein interaction network capturing a global perspective of perturbations in diabetes by integrating T2D subjects' transcriptomic data. We computed a 'frequently perturbed subnetwork' in T2D that captures common perturbations across tissue types and mapped the possible effects of Metformin onto it. We then identified a set of remaining T2D perturbations and potential drug targets among them, related to oxidative stress and hypercholesterolemia. We then identified Probucol as the potential co-drug for adjunct therapy with Metformin and evaluated the efficacy of the combination in a rat model of diabetes. We find Metformin-Probucol at 5:0.5 mg/kg effective in restoring near-normal serum glucose, lipid, and cholesterol levels.

Profiling system-wide variations and similarities between rheumatic heart disease and acute rheumatic fever-A pilot analysis

Rheumatic heart disease (RHD) continues to affect developing countries with low income due to the lack of resources and effective diagnostic techniques. Understanding the genetic basis common to both the diseases and that of progression from its prequel disease state, acute rheumatic fever (ARF), would aid in developing predictive biomarkers and improving patient care. To gain system-wide molecular insights into possible causes for progression, in this pilot study, we collected blood transcriptomes from ARF (5) and RHD (5) patients. Using an integrated transcriptome and network analysis approach, we identified a subnetwork comprising the most significantly differentially expressed genes and most perturbed pathways in RHD compared to ARF. For example, the chemokine signaling pathway was seen to be upregulated, while tryptophan metabolism was found to be downregulated in RHD. The subnetworks of variation between the two conditions provide unbiased molecular-level insights into the host processes that may be linked with the progression of ARF to RHD, which has the potential to inform future diagnostics and therapeutic strategies. We also found a significantly raised neutrophil/lymphocyte ratio in both ARF and RHD cohorts. Activated neutrophils and inhibited Natural Killer cell gene signatures reflected the drivers of the inflammatory process typical to both disease conditions.

In-silico study of influence of HLA heterogeneity on CTL responses across ethnicities to SARS-CoV-2

Differences in outcome to COVID-19 infection in different individuals is largely attributed to genetic heterogeneity leading to differential immune responses across individuals and populations. HLA is one such genetic factor that varies across individuals leading to differences in how T-cell responses are triggered against SARS-CoV-2, directly influencing disease susceptibility. HLA alleles that influence COVID-19 outcome, by virtue of epitope binding and presentation, have been identified in cohorts worldwide. However, the heterogeneity in HLA distribution across ethnic groups limits the generality of such association. In this study, we address this limitation by comparing the recognition of CTL epitopes across HLA genotypes and ethnic groups. Using HLA allele frequency data for ethnic groups from Allele Frequency Net Database (AFND), we construct synthetic populations for each ethnic group and show that CTL epitope strength varies across HLA genotypes and populations. We also observe that HLA genotypes, in certain cases, can have high CTL epitope strengths in the absence of top-responsive HLA alleles. Finally, we show that the theoretical estimate of responsiveness and hence protection offered by a HLA allele is bound to vary across ethnic groups, due to the influence of other HLA alleles within the HLA genotype on CTL epitope recognition. This emphasizes the need for studying HLA-disease associations at the genotype level rather than at a single allele level.

Personalized quantitative models of NAD metabolism in hepatocellular carcinoma identify a subgroup with poor prognosis

Cancer cells are known to undergo metabolic adaptation to cater to their enhanced energy demand. Nicotinamide adenine dinucleotide (NAD) is an essential metabolite regulating many cellular processes within the cell. The enzymes required for NAD synthesis, starting from the base precursor - tryptophan, are expressed in the liver and the kidney, while all other tissues convert NAD from intermediate precursors. The liver, being an active metabolic organ, is a primary contributor to NAD biosynthesis. Inhibition of key enzymes in the NAD biosynthetic pathways is proposed as a strategy for designing anti-cancer drugs. On the other hand, NAD supplementation has also been reported to be beneficial in cancer in some cases. As metabolic adaptation that occurs in cancer cells can lead to perturbations to the pathways, it is important to understand the exact nature of the perturbation in each individual patient. To investigate this, we use a mathematical modelling approach integrated with transcriptomes of patient samples from the TCGA-LIHC cohort. Quantitative profiling of the NAD biosynthesis pathway helps us understand the NAD biosynthetic status and changes in the controlling steps of the pathway. Our results indicate that NAD biosynthesis is heterogeneous among liver cancer patients, and that Nicotinate phosphoribosyl transferase (NAPRT) levels are indicative of the NAD biosynthetic status. Further, we find that reduced NAPRT levels combined with reduced Nicotinamide phosphoribosyl transferase (NAMPT) levels contribute to poor prognosis. Identification of the precise subgroup who may benefit from NAD supplementation in subgroup with low levels of NAPRT and NAMPT could be explored to improve patient outcome.

Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment

Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.

Functional and Biochemical Characterization of the MazEF6 Toxin-Antitoxin System of Mycobacterium tuberculosis

The Mycobacterium tuberculosis genome harbors nine toxin-antitoxin (TA) systems that are members of the mazEF family, unlike other prokaryotes, which have only one or two. Although the overall tertiary folds of MazF toxins are predicted to be similar, it is unclear how they recognize structurally different RNAs and antitoxins with divergent sequence specificity. Here, we have expressed and purified the individual components and complex of the MazEF6 TA system from M. tuberculosis. Size exclusion chromatography-multiangle light scattering (SEC-MALS) was performed to determine the oligomerization status of the toxin, antitoxin, and the complex in different stoichiometric ratios. The relative stabilities of the proteins were determined by nano-differential scanning fluorimetry (nano-DSF). Microscale thermophoresis (MST) and yeast surface display (YSD) were performed to measure the relative affinities between the cognate toxin-antitoxin partners. The interaction between MazEF6 complexes and cognate promoter DNA was also studied using MST. Analysis of paired-end RNA sequencing data revealed that the overexpression of MazF6 resulted in differential expression of 323 transcripts in M. tuberculosis. Network analysis was performed to identify the nodes from the top-response network. The analysis of mRNA protection ratios resulted in identification of putative MazF6 cleavage site in its native host, M. tuberculosis. IMPORTANCE M. tuberculosis harbors a large number of type II toxin-antitoxin (TA) systems, the exact roles for most of which are unclear. Prior studies have reported that overexpression of several of these type II toxins inhibits bacterial growth and contributes to the formation of drug-tolerant populations in vitro. To obtain insights into M. tuberculosis MazEF6 type II TA system function, we determined stability, oligomeric states, and binding affinities of cognate partners with each other and with their promoter operator DNA. Using RNA-seq data obtained from M. tuberculosis overexpression strains, we have identified putative MazF6 cleavage sites and targets in its native, cellular context.

Mycobacterium tuberculosis requires SufT for Fe-S cluster maturation, metabolism, and survival in vivo

Iron-sulfur (Fe-S) cluster proteins carry out essential cellular functions in diverse organisms, including the human pathogen Mycobacterium tuberculosis (Mtb). The mechanisms underlying Fe-S cluster biogenesis are poorly defined in Mtb. Here, we show that Mtb SufT (Rv1466), a DUF59 domain-containing essential protein, is required for the Fe-S cluster maturation. Mtb SufT homodimerizes and interacts with Fe-S cluster biogenesis proteins; SufS and SufU. SufT also interacts with the 4Fe-4S cluster containing proteins; aconitase and SufR. Importantly, a hyperactive cysteine in the DUF59 domain mediates interaction of SufT with SufS, SufU, aconitase, and SufR. We efficiently repressed the expression of SufT to generate a SufT knock-down strain in Mtb (SufT-KD) using CRISPR interference. Depleting SufT reduces aconitase's enzymatic activity under standard growth conditions and in response to oxidative stress and iron limitation. The SufT-KD strain exhibited defective growth and an altered pool of tricarboxylic acid cycle intermediates, amino acids, and sulfur metabolites. Using Seahorse Extracellular Flux analyzer, we demonstrated that SufT depletion diminishes glycolytic rate and oxidative phosphorylation in Mtb. The SufT-KD strain showed defective survival upon exposure to oxidative stress and nitric oxide. Lastly, SufT depletion reduced the survival of Mtb in macrophages and attenuated the ability of Mtb to persist in mice. Altogether, SufT assists in Fe-S cluster maturation and couples this process to bioenergetics of Mtb for survival under low and high demand for Fe-S clusters.

A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R9-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R9-score can determine treatment effectiveness, making a case for designing a larger clinical study.

SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites

Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation.

A large number of biological functions are orchestrated by proteins. The function of proteins is governed by its structure and its interacting ligand. However, it is known that not all residues are involved in ligand recognition. More specifically, residues that are located within 4.5 Å of ligand atoms are considered to be ’binding sites’. Here, we have developed an algorithm called SiteMotif that efficiently aligns multiple binding sites into a common frame. This process enables us to derive conservation among the binding site residues in a sequence order independent manner. The algorithm was validated extensively across five different levels and measured binding site similarities in each of them. Previous research has found multiple instances where different proteins have comparable binding sites and hence perform the same function. We present the ability of our method to detect such scenarios. Finally, As a use case, we applied SiteMotif to a set of glutathione binding proteins and derived a site based sequence motif characteristic of all glutathione binding proteins.

A 9-gene biomarker panel identifies bacterial coinfections in culture-negative COVID-19 cases

Confirmatory diagnosis of bacterial coinfections with COVID-19 is challenging due to limited specificity of the gold-standard culture sensitivity test. This study presents a host gene signature to diagnose bacterial coinfection with high confidence.

Results of a nationally implemented cardiac screening programme in elite cricket players in England and Wales

OBJECTIVES

We assessed the diagnostic yield and costs of an electrocardiogram-based national screening programme in elite cricket players and the incremental value of transthoracic echocardiography and periodic evaluation.

DESIGN

Cross-sectional study.

METHODS

Between 2008 and 2019, 1208 cricketers underwent screening with a health questionnaire, 12-lead electrocardiogram and cardiology consultation. Athletes with concerning findings underwent on-site transthoracic echocardiography and further investigations as necessary. In addition, despite a normal health questionnaire and electrocardiogram, 342 (28.3%) athletes had a transthoracic echocardiogram and 493 (40.8%) underwent repeat evaluations.

RESULTS

After initial evaluation, 47 (3.9%) athletes underwent on-site transthoracic echocardiography of whom 35 (2.8%) were referred for further evaluation. Four athletes (0.3%) were diagnosed with major cardiac conditions; hypertrophic cardiomyopathy (n = 1), arrhythmogenic cardiomyopathy (n = 1) and Wolff-Parkinson-White pattern (n = 2). Two athletes were identified with minor valvular abnormalities. Repeat evaluation of 493 athletes identified hypertrophic cardiomyopathy in a 22-year-old athlete, two years after his initial normal screening. During a follow-up of 5.8 ± 2.9 years no additional diagnoses or adverse cardiac events were reported. The cost of the electrocardiogram-based programme was £127,844, translating to £106 per athlete and £25,569 per major cardiac condition identified.Routine transthoracic echocardiography in 342 athletes identified two athletes with major cardiac conditions (bicuspid aortic valve with severe aortopathy and aortic regurgitation and an atrial septal defect associated with right ventricular volume overload) and 10 athletes with minor abnormalities.

CONCLUSIONS

An electrocardiogram-based national screening programme identified a major cardiac condition in 0.3% of athletes. Routine transthoracic echocardiography and periodic evaluation increased the diagnostic yield to 0.6%, at an incremental cost.

Competing risks survival data under middle censoring-An application to COVID-19 pandemic

Survival data is being analysed here under the middle censoring scheme, using specifically quantile function modelling under competing risks. The use of middle censoring scheme has been shown to be very appropriate under the COVID-19 pandemic scenario. Cause-specific quantile inference under middle censoring is employed. Such quantile inferences are obtained through cumulative incidence function based on cause-specific proportional hazards model. The baseline lifetime is assumed to follow a very general parametric model namely the Weibull distribution, and is independent of the censoring mechanism. We obtain estimates of the unknown parameters and cause specific quantile functions under classical as well as a Bayesian set-up. A Monte Carlo simulation study assesses the relative performance of the different estimators. Finally, a real life data analysis is given for illustration of the proposed methods.

Cyclic di-GMP sensing histidine kinase PdtaS controls mycobacterial adaptation to carbon sources

Cell signaling relies on second messengers to transduce signals from the sensory apparatus to downstream signaling pathway components. In bacteria, one of the most important and ubiquitous second messenger is the small molecule cyclic diguanosine monophosphate (c-di-GMP). While the biosynthesis, degradation, and regulatory pathways controlled by c-di-GMP are well characterized, the mechanisms through which c-di-GMP controls these processes are not entirely understood. Herein we present the report of a c-di-GMP sensing sensor histidine kinase PdtaS (Rv3220c), which binds to c-di-GMP at submicromolar concentrations, subsequently perturbing signaling of the PdtaS-PdtaR (Rv1626) two-component system. Aided by biochemical analysis, genetics, molecular docking, FRET microscopy, and structural modelling, we have characterized the binding of c-di-GMP in the GAF domain of PdtaS. We show that a pdtaS knockout in Mycobacterium smegmatis is severely compromised in growth on amino acid deficient media and exhibits global transcriptional dysregulation. The perturbation of the c-di-GMP-PdtaS-PdtaR axis results in a cascade of cellular changes recorded by a multiparametric systems' approach of transcriptomics, unbiased metabolomics, and lipid analyses.

PathExt: a general framework for path-based mining of omics-integrated biological networks

MOTIVATION

Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs.

RESULTS

We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls.

AVAILABILITYAND IMPLEMENTATION

The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

BACKGROUND

Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.

METHODS

We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.

FINDING

We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.

INTERPRETATION

Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

FUNDING

Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Immune Subtyping in Latent Tuberculosis

Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

VapC21 Toxin Contributes to Drug-Tolerance and Interacts With Non-cognate VapB32 Antitoxin in Mycobacterium tuberculosis

The prokaryotic ubiquitous Toxin-antitoxin (TA) modules encodes for a stable toxin and an unstable antitoxin. VapBC subfamily is the most abundant Type II TA system in M. tuberculosis genome. However, the exact physiological role for most of these Type II TA systems are still unknown. Here, we have comprehensively characterized the VapBC21 TA locus from M. tuberculosis. The overexpression of VapC21 inhibited mycobacterial growth in a bacteriostatic manner and as expected, growth inhibition was abrogated upon co-expression of the cognate antitoxin, VapB21. We observed that the deletion of vapC21 had no noticeable influence on the in vitro and in vivo growth of M. tuberculosis. Using co-expression and biophysical studies, we observed that in addition to VapB21, VapC21 is also able to interact with non-cognate antitoxin, VapB32. The strength of interaction varied between the cognate and non-cognate TA pairs. The overexpression of VapC21 resulted in differential expression of approximately 435 transcripts in M. tuberculosis. The transcriptional profiles obtained upon ectopic expression of VapC21 was similar to those reported in M. tuberculosis upon exposure to stress conditions such as nutrient starvation and enduring hypoxic response. Further, VapC21 overexpression also led to increased expression of WhiB7 regulon and bacterial tolerance to aminoglycosides and ethambutol. Taken together, these results indicate that a complex network of interactions exists between non-cognate TA pairs and VapC21 contributes to drug tolerance in vitro.

Modeling Stripe Formation on Growing Zebrafish Tailfins

As zebrafish develop, black and gold stripes form across their skin due to the interactions of brightly colored pigment cells. These characteristic patterns emerge on the growing fish body, as well as on the anal and caudal fins. While wild-type stripes form parallel to a horizontal marker on the body, patterns on the tailfin gradually extend distally outward. Interestingly, several mutations lead to altered body patterns without affecting fin stripes. Through an exploratory modeling approach, our goal is to help better understand these differences between body and fin patterns. By adapting a prior agent-based model of cell interactions on the fish body, we present an in silico study of stripe development on tailfins. Our main result is a demonstration that two cell types can produce stripes on the caudal fin. We highlight several ways that bone rays, growth, and the body-fin interface may be involved in patterning, and we raise questions for future work related to pattern robustness.

A Strategic Target Rescues Trimethoprim Sensitivity in Escherichia coli

Trimethoprim, a preferred treatment for urinary tract infections, is becoming obsolete owing to the rapid dissemination of resistant E. coli. Although direct resistance mechanisms such as overexpression of a mutant FolA and dfr enzymes are well characterized, associated alterations that drive or sustain resistance are unknown. We identify the repertoire of resistance-associated perturbations by constructing and interrogating a transcriptome-integrated functional interactome. From the cross talk between perturbations in stress-response and metabolic pathways, we identify the critical dependence on serine hydroxymethyltransferase (GlyA) as an emergent vulnerability. Through its deletion, we demonstrate that GlyA is necessary to sustain high levels of resistance in both laboratory-evolved resistant E. coli and a multidrug-resistant clinical isolate. Through comparative evolution, we show that the absence of GlyA activity decelerates the acquisition of resistance in E. coli. Put together, our results identify GlyA as a promising target, providing a basis for the rational design of drug combinations.

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TMP-resistant E. coli show cross talk between stress response and metabolic pathways

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Dependence on glyA is an emergent vulnerability associated with TMP resistance

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Knockout of glyA partially rescues sensitivity to TMP in E. coli

P615Role of three-dimensional (3D) speckle-tracking echocardiography (STE) system, using comparison with cardiac magnetic resonance imaging (CMR) and to assess the contribution of regional RV function to

Background

Chronic obstructive pulmonary disease may alter right and left ventricular function by changing intrathoracic pressure. Pulmonary hyperinflation may increase right atrial pressure, leading to reduced venous return and subsequent reductions in RV pre-load. In COPD patients, hyperinflation has been directly correlated with reduced atrial chamber size, global RV dysfunction, and reduced LV filling. Accurate assessment of global and regional right ventricular (RV) systolic function is challenging.

Purpose

The aims of this study were to confirm the reliability and feasibility of a three-dimensional (3D) speckle-tracking echocardiography (STE) system, using comparison with cardiac magnetic resonance imaging (CMR), and to assess the contribution of regional RV function to global function.

Methods

In a retrospective, cross-sectional study setting, RV volumetric data were studied in 302 patients who were referred for both CMR and 3D echocardiography within 1 month. Three-dimensional STE-derived area strain, longitudinal strain, and circumferential strain were assessed as global, inlet, outflow, apical, and septal segments.

Results

208 patients (69%) had adequate 3D echocardiographic data. RV measurements derived from 3D STE and CMR were closely related (RV end-diastolic volume, R2=0.88; RV end-systolic volume, R2=0.81; RV ejection fraction [RVEF], R2=0.69; P<0.004 for all). RVEF and RV end-diastolic volume from 3D STE were slightly but significantly smaller than CMR values (mean differences, −2.8% and −7.8 mL for RVEF and RV end-diastolic volume, respectively). Among conventional echocardiographic parameters for RV function (tricuspid annular plane systolic excursion, fractional area change, S' of the tricuspid annulus, RV free wall two-dimensional longitudinal strain), only fractional area change was significantly related to RVEF (r=0.29, P=0.003). Among segmental 3D strain variables, inlet area strain (r=−0.48, P<0.002) and outflow circumferential strain (r=−0.37, P<0.003) were independent factors associated with CMR-derived RVEF.

Conclusions

Regional RV wall motion showed that heterogeneous segmental deformations affect global RV function differently; specifically, inlet area strain and outflow circumferential strain.RV volume and RVEF determined by STE were comparable with CMR measurements. Severity of COPD influences RV systolic dysfunction, which is reflected in speckle tracking 3D echocardiographic parameters.

Development and Characterization of a Potent Tumor Necrosis Factor-Alpha-Blocking Agent

Tumor necrosis factor-α (TNFα), one of the major proinflammatory cytokines, plays a key role in an effective immune response. However, the chronic presence of TNFα can lead to several inflammatory disorders, such as rheumatoid arthritis, psoriasis, Crohn's disease, etc. Inhibition of TNFα by pharmacological inhibitors or antibodies has proven to be effective in palliative treatment to some extent. The aim of this study was to develop an anti-TNFα antibody, which may be used as a therapeutic option to inhibit TNFα-mediated cytotoxicity. We characterized several hybridoma clones secreting monoclonal antibodies (mAbs) to human-TNFα. Four mAbs rescued L929 fibroblast cells from TNFα-triggered cell death and one of these, namely C8, was found to have the highest affinity. To gain insights into the mechanism by which mAb C8 inhibits human TNFα-mediated toxicity, the epitope corresponding to the mAb was delineated. The antigenic determinant was found to comprise of the stretch of amino acids 99-120, of which, 102-104 (glutamine, arginine, glutamic acid) form the core epitope. The observation was supported by bioinformatics analyses of an antigen/antibody complex model. In addition, the binding affinity of mAb C8 to TNFα was found to be comparable with that of infliximab, which is a commercially available anti-TNFα mAb.

Ω76: A designed antimicrobial peptide to combat carbapenem- and tigecycline-resistant Acinetobacter baumannii

Drug resistance is a public health concern that threatens to undermine decades of medical progress. ESKAPE pathogens cause most nosocomial infections, and are frequently resistant to carbapenem antibiotics, usually leaving tigecycline and colistin as the last treatment options. However, increasing tigecycline resistance and colistin's nephrotoxicity severely restrict use of these antibiotics. We have designed antimicrobial peptides using a maximum common subgraph approach. Our best peptide (Ω76) displayed high efficacy against carbapenem and tigecycline-resistant Acinetobacter baumannii in mice. Mice treated with repeated sublethal doses of Ω76 displayed no signs of chronic toxicity. Sublethal Ω76 doses co-administered alongside sublethal colistin doses displayed no additive toxicity. These results indicate that Ω76 can potentially supplement or replace colistin, especially where nephrotoxicity is a concern. To our knowledge, no other existing antibiotics occupy this clinical niche. Mechanistically, Ω76 adopts an α-helical structure in membranes, causing rapid membrane disruption, leakage, and bacterial death.

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

Protein Tagging, Destruction and Infection

Cells possess protein quality control mechanisms to maintain proper cellular homeostasis. In eukaryotes, the roles of the ubiquitination and proteasome-mediated degradation of cellular proteins is well established. Recent studies have elucidated protein tagging mechanisms in prokaryotes, involving transfer messenger RNA (tmRNA) and pupylation. In this review, newer insights and bioinformatics analysis of two distinct bacterial protein tagging machineries are discussed. The machinery for tmRNAmediated tagging is present in several eubacterial representatives, e.g. Escherichia coli, Mycobacterium tuberculosis, Bacillus subtilis etc., but not in two archaeal representatives, such as Thermoplasma acidophilum and Sulfolobus solfataricus. On the other hand, the machinery involving tagging with the prokaryotic ubiquitin-like protein (Pup) is absent in most bacteria but is encoded in some eubacterial representatives, e.g. Mycobacterium tuberculosis and Mycobacterium leprae. Furthermore, molecular details on the relationship between protein tagging and enzymes involved in protein degradation in bacteria during infection are emerging. Several pathogenic bacteria that do not express the major ATP-dependent proteases, Lon and Caseinolytic protease (ClpP), are avirulent. Also, some ATP-independent peptidases, such as PepA and PepN, modulate the infection process. The roles of bacterial proteins involved in tagging and degradation during infection are discussed. These aspects add a new dimension to better understanding of the peculiarities of host-pathogen interactions.

Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach

Sorafenib (SFB), a multi-kinase inhibitor, is the only approved drug for treating hepatocellular carcinoma (HCC). However, SFB shows low efficacy in many cases. HCC related mortality therefore remains to be high worldwide. SFB, a multi-kinase inhibitor is also known to modulate the redox homeostasis in cancer cells. To understand the effect of SFB on the redox status, a quantitative understanding of the system is necessary. Kinetic modeling of the relevant pathways is a useful approach for obtaining a quantitative understanding of the pathway dynamics and to rank the individual factors based on the extent of influence they wield on the pathway. Here, we report a comprehensive model of the glutathione reaction network (GSH_net ), consisting of four modules and includes SFB-induced redox stress. We compared GSH_net simulations for HCC of six different etiologies with healthy liver, and correctly identified the expected variations in cancer. Next, we studied alterations induced in the system upon SFB treatment and observed differential H₂ O₂ dynamics in all the conditions. Using metabolic control analysis, we identified glutathione S-transferase (GST) as the enzyme with the highest selective control coefficient, making it an attractive co-target for potentiating the action of SFB across all six etiologies. As a proof-of-concept, we selected ethacrynic acid (EA), a known inhibitor of GST, and verified ex vivo that EA synergistically potentiates the cytotoxic effect of SFB. Being an FDA approved drug, EA is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.

Assessment of the Effectiveness of Combat Eyewear Protection Against Blast Overpressure

It is unclear whether combat eyewear used by U. S. Service members is protective against blast overpressures (BOPs) caused by explosive devices. Here, we investigated the mechanisms by which BOP bypasses eyewear and increases eye surface pressure. We performed experiments and developed three-dimensional (3D) finite element (FE) models of a head form (HF) equipped with an advanced combat helmet (ACH) and with no eyewear, spectacles, or goggles in a shock tube at three BOPs and five head orientations relative to the blast wave. Overall, we observed good agreement between experimental and computational results, with average discrepancies in impulse and peak-pressure values of less than 15% over 90 comparisons. In the absence of eyewear and depending on the head orientation, we identified three mechanisms that contributed to pressure loading on the eyes. Eyewear was most effective at 0 deg orientation, with pressure attenuation ranging from 50 (spectacles) to 80% (goggles) of the peak pressures observed in the no-eyewear configuration. Spectacles and goggles were considerably less effective when we rotated the HF in the counter-clockwise direction around the superior-inferior axis of the head. Surprisingly, at certain orientations, spectacles yielded higher maximum pressures (80%) and goggles yielded larger impulses (150%) than those observed without eyewear. The findings from this study will aid in the design of eyewear that provides better protection against BOP.

Co-relation Between Total Cholesterol, High Density Lipoprotein, Low Density Lipoprotein and Glycosylated Haemoglobin (HbA1c) in Diabetic Patients with Acute Coronary Syndrome (ACS)

BACKGROUND

Patients with diabetes & dyslipidaemia are at increased risk of developing coronary artery disease that many time manifests as life threatening ACS.

AIM

To study co-relation between HbA1c &total cholesterol, low density lipoprotein (LDL), high density lipoprotein (HDL) in diabetic patients with acute coronary syndrome and also their co-relationship with severity of ACS independently.

MATERIALS AND METHOD

Blood samples of 51 known diabetic patients presented to emergency with ACS were sent for HbA1c & lipid profile estimation. All patients underwent coronary angiography. Obtained results were statistically analysed & co-related.

RESULTS

Patients were divided as having: 1. HDL <40, >40; 2. LDL <100, >100; 3. Total cholesterol <200, >200; 4. HbA1c 6.5-8.4, >8.4; 5. Single vessel disease (SVD) / multi vessel disease (MVD). Statistically significant direct co-relationship was found between HbA1c, LDL, Total cholesterol, ACS severity (SVD/MVD) & inverse co-relationship with HDL.

CONCLUSION

Severity & incidence of ACS in diabetic patients can be minimised by maintaining adequate glycaemic control & also by keeping circulating lipids under control.

Design of a heme-binding peptide motif adopting a β-hairpin conformation

Heme-binding proteins constitute a large family of catalytic and transport proteins. Their widespread presence as globins and as essential oxygen and electron transporters, along with their diverse enzymatic functions, have made them targets for protein design. Most previously reported designs involved the use of α-helical scaffolds, and natural peptides also exhibit a strong preference for these scaffolds. However, the reason for this preference is not well-understood, in part because alternative protein designs, such as those with β-sheets or hairpins, are challenging to perform. Here, we report the computational design and experimental validation of a water-soluble heme-binding peptide, Pincer-1, composed of predominantly β-scaffold secondary structures. Such heme-binding proteins are rarely observed in nature, and by designing such a scaffold, we simultaneously increase the known fold space of heme-binding proteins and expand the limits of computational design methods. For a β-scaffold, two tryptophan zipper β-hairpins sandwiching a heme molecule were linked through an N-terminal cysteine disulfide bond. β-Hairpin orientations and residue selection were performed computationally. Heme binding was confirmed through absorbance experiments and surface plasmon resonance experiments (K_D = 730 ± 160 nm). CD and NMR experiments validated the β-hairpin topology of the designed peptide. Our results indicate that a helical scaffold is not essential for heme binding and reveal the first designed water-soluble, heme-binding β-hairpin peptide. This peptide could help expand the search for and design space to cytoplasmic heme-binding proteins.

Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza

Genetic differences contribute to variations in the immune response mounted by different individuals to a pathogen. Such differential response can influence the spread of infectious disease, indicating why such diseases impact some populations more than others. Here, we study the impact of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. For a population with known HLA class-I allele frequency and for a given H1N1 viral strain, we classify individuals into sub-populations according to their level of susceptibility to infection. Our core hypothesis is that the susceptibility of a given individual to a disease such as H1N1 influenza is inversely proportional to the number of high affinity viral epitopes the individual can present. This number can be extracted from the HLA genetic profile of the individual. We use ethnicity-specific HLA class-I allele frequency data, together with genome sequences of various H1N1 viral strains, to obtain susceptibility sub-populations for 61 ethnicities and 81 viral strains isolated in 2009, as well as 85 strains isolated in other years. We incorporate these data into a multi-compartment SIR model to analyse the epidemic dynamics for these (ethnicity, viral strain) epidemic pairs. Our results show that HLA allele profiles which lead to a large spread in individual susceptibility values can act as a protective barrier against the spread of influenza. We predict that populations skewed such that a small number of highly susceptible individuals coexist with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but distributed more uniformly across individuals. Our model tracks some well-known qualitative trends of influenza spread worldwide, suggesting that HLA genetic diversity plays a crucial role in determining the spreading potential of different influenza viral strains across populations.

Levels of immunity to strains of H1N1 influenza can vary, depending on the individual. This strongly influences how the disease spreads in a population. Accounting for such variations is a major challenge for the epidemiology of infectious diseases. We study the effect of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. We model the immune response of specific ethnicities to a number of H1N1 viral strains, using this information to study disease spread for these (ethnicity, viral strain) epidemic pairs. Our results show that larger genetic diversity at the level of immune response, leading to the presence of susceptibility sub-populations with a broad distribution of susceptibilities, protects against the spread of influenza in a population. We also show that populations with a small number of highly susceptible individuals, but with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but where it is more uniformly distributed. Our work captures some qualitative trends of influenza spread worldwide, providing a first attempt at understanding how susceptibility heterogeneities arising from variations in immune response determine disease spread in populations.

Biophysical and biochemical characterization of Rv3405c, a tetracycline repressor protein from Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of tuberculosis disease, is one among the deadliest pathogens in the world. Due to long treatment regimen, HIV co-infection, persistence of bacilli in latent form and development of XDR and TDR strains of Mtb, tuberculosis has posed serious concerns for managing the disease, and calls for discovery of new drugs and drug targets. Using a computational pipeline involving analysis of the structural models of the Mtb proteome and an analysis of the ATPome, followed by a series of filters to identify druggable proteins, solubility and length of the protein, several candidate proteins were shortlisted. From this, Rv3405c, a tetR family of DNA binding protein involved in antibiotic resistance, was identified as one of the good drug targets. Rv3405c binds to the upstream non-coding region of Rv3406 and causes repression of Rv3406 activity there by affecting the downstream processes involved in antibiotic resistance was further characterized. The Rv3405c gene was cloned; the gene product was over-expressed in E. coli and purified by Ni NTA chromatography. DNA binding studies by EMSA showed that the recombinant Rv3405c protein binds to the DNA sequence corresponding to the promoter region of Rv3406 and upon addition of tetracycline, the DNA binding activity was lost. β-galactosidase reporter assay in E. coli using both wild type and a DNA binding defective mutant protein indeed proved that Rv3405c acts as a repressor.

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria

There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum β-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.