A supervised Bayesian factor model for the identification of multi-omics signatures.

MOTIVATION: Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. RESULTS: We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of coronavirus disease 2019 severity and breast cancer tumor subtypes. AVAILABILITY AND IMPLEMENTATION: SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

Hearing Loss Prevalence, Years Lived With Disability, and Hearing Aid Use in the United States From 1990 to 2019: Findings From the Global Burden of Disease Study.

OBJECTIVES: This article describes key data sources and methods used to estimate hearing loss in the United States, in the Global Burden of Disease study. Then, trends in hearing loss are described for 2019, including temporal trends from 1990 to 2019, changing prevalence over age, severity patterns, and utilization of hearing aids. DESIGN: We utilized population-representative surveys from the United States to estimate hearing loss prevalence for the Global Burden of Disease study. A key input data source in modeled estimates are the National Health and Nutrition Examination Surveys (NHANES), years 1988 to 2010. We ran hierarchical severity-specific models to estimate hearing loss prevalence. We then scaled severity-specific models to sum to total hearing impairment prevalence, adjusted estimates for hearing aid coverage, and split estimates by etiology and tinnitus status. We computed years lived with disability (YLDs), which quantifies the amount of health loss associated with a condition depending on severity and creates a common metric to compare the burden of disparate diseases. This was done by multiplying the prevalence of severity-specific hearing loss by corresponding disability weights, with additional weighting for tinnitus comorbidity. RESULTS: An estimated 72.88 million (95% uncertainty interval (UI) 68.53 to 77.30) people in the United States had hearing loss in 2019, accounting for 22.2% (20.9 to 23.6) of the total population. Hearing loss was responsible for 2.24 million (1.56 to 3.11) YLDs (3.6% (2.8 to 4.7) of total US YLDs). Age-standardized prevalence was higher in males (17.7% [16.7 to 18.8]) compared with females (11.9%, [11.2 to 12.5]). While most cases of hearing loss were mild (64.3%, 95% UI 61.0 to 67.6), disability was concentrated in cases that were moderate or more severe. The all-age prevalence of hearing loss in the United States was 28.1% (25.7 to 30.8) higher in 2019 than in 1990, despite stable age-standardized prevalence. An estimated 9.7% (8.6 to 11.0) of individuals with mild to profound hearing loss utilized a hearing aid, while 32.5% (31.9 to 33.2) of individuals with hearing loss experienced tinnitus. Occupational noise exposure was responsible for 11.2% (10.2 to 12.4) of hearing loss YLDs. CONCLUSIONS: Results indicate large burden of hearing loss in the United States, with an estimated 1 in 5 people experiencing this condition. While many cases of hearing loss in the United States were mild, growing prevalence, low usage of hearing aids, and aging populations indicate the rising impact of this condition in future years and the increasing importance of domestic access to hearing healthcare services. Large-scale audiometric surveys such as NHANES are needed to regularly assess hearing loss burden and access to healthcare, improving our understanding of who is impacted by hearing loss and what groups are most amenable to intervention.

Is self-testing the next paradigm for diagnostics?

The study estimates the usability and attitude assessment of users for India's first approved rapid antigen self-test kit; the CoviSelf™. India approved its first AI-powered self-test for Covid-19 in April 2021 a few weeks after the first approval in the US. We present here a study on usability and attitude assessment of users of India's first approved rapid antigen self-test kit; the CoviSelf™. The study evaluates participants' understanding of and performance of test procedure and interprets the results. Analysis revealed that more than 90% study participants followed steps correctly as illustrated in the user's manual. Age group and gender-based analysis showed comparable scores for usability of the test kit suggesting users of different age groups has same ease in using the test kit. What we learnt from this study could be start of self-test revolution, where rapid tests could expand the access of diagnostics for hundreds of diseases including HIV, HPV, and dengue to millions of people who could not get access to diagnostics because we lacked manpower or facility to conduct tests. Self-testing could break the barriers for diagnostics that Internet did for information.

Scoring System for the Use of Nebulizers in the Primary Care Settings: An Expert Consensus Statement.

BACKGROUND: The use of nebulizers is an important and useful method for delivering drugs to the lungs in patients with various airway and lung parenchymal disorders. They are primarily used in patients with acute symptoms and in a selected group of patients for maintenance treatment. Its use has increased, especially during the coronavirus disease 2019 (COVID-19) pandemic. To ensure the appropriate use of nebulizers by primary care physicians and to guide them, we aimed to develop a simple nebulizer use score. METHODS: An expert working group (EWG) of pulmonologists were formed who using a semi- Delphi method, developed a list of variables and a cut-off score to decide when to use nebulizers. We started with a total of 55 variables that were developed through an exhaustive review of the literature. These were further reduced to smaller numbers that had the maximum score as well as concordance with the EWG. The scores ranged from 1 to 10 (completely disagree to completely agree), and only those above 7.5 were selected. RESULTS: A total of 8 variables with the highest scores were selected (Table 1), which had a total maximum score of 40. A score of <15 was suggested to indicate no use of nebulizer and >20 to suggest definite use of nebulizer. A score between 15 and 20 was suggested for physician judgment. A separate table of 12 conditions was made where the use of nebulizers was mandatory. CONCLUSION: This first-of-its-kind nebulizer score can be used by primary care physicians to decide which patients should be put on nebulizer treatment.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients.

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Potential use of endemic human coronaviruses to stimulate immunity against pathogenic SARS-CoV-2 and its variants.

While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes significant morbidity and mortality in humans, there is a wide range of disease outcomes following virus exposures. Some individuals are asymptomatic while others develop complications within a few days after infection that can lead to fatalities in a smaller portion of the population. In the present study, we have analyzed the factors that may influence the outcome of post-SARS-CoV-2 infection. One factor that may influence virus control is pre-existing immunity conferred by an individual's past exposures to endemic coronaviruses (eCOVIDs) which cause the common cold in humans and generally, most children are exposed to one of the four eCOVIDs before 2 years of age. Here, we have carried out protein sequence analyses to show the amino acid homologies between the four eCOVIDs (i.e. OC43, HKU1, 229E, and NL63) as well as examining the cross-reactive immune responses between SARS-CoV-2 and eCOVIDs by epidemiologic analyses. Our results show that the nations where continuous exposures to eCOVIDs are very high due to religious and traditional causes showed significantly lower cases and low mortality rates per 100,000. We hypothesize that in the areas of the globe where Muslims are in majority and due to religious practices are regularly exposed to eCOVIDs they show a significantly lower infection, as well as mortality rate, and that is due to pre-existing cross-immunity against SARS-CoV-2. This is due to cross-reactive antibodies and T-cells that recognize SARS-CoV-2 antigens. We also have reviewed the current literature that has also proposed that human infections with eCOVIDs impart protection against disease caused by subsequent exposure to SARS-CoV-2. We propose that a nasal spray vaccine consisting of selected genes of eCOVIDs would be beneficial against SARS-CoV-2 and other pathogenic coronaviruses.

A supervised Bayesian factor model for the identification of multi-omics signatures.

MOTIVATION: Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are iden-tified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive model-ing. However, multi-omics integration and predictive modeling are generally performed independent-ly in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. RESULTS: We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the recon-struction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes. AVAILABILITY: SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

Probiotics: insights and new opportunities for Clostridioides difficile intervention.

Clostridioides difficile infection (CDI) is a life-threatening disease caused by the Gram-positive, opportunistic intestinal pathogen C. difficile. Despite the availability of antimicrobial drugs to treat CDI, such as vancomycin, metronidazole, and fidaxomicin, recurrence of infection remains a significant clinical challenge. The use of live commensal microorganisms, or probiotics, is one of the most investigated non-antibiotic therapeutic options to balance gastrointestinal (GI) microbiota and subsequently tackle dysbiosis. In this review, we will discuss major commensal probiotic strains that have the potential to prevent and/or treat CDI and its recurrence, reassess the efficacy of probiotics supplementation as a CDI intervention, delve into lessons learned from probiotic modulation of the immune system, explore avenues like genome-scale metabolic network reconstructions, genome sequencing, and multi-omics to identify novel strains and understand their functionality, and discuss the current regulatory framework, challenges, and future directions.

Evaluating the effect and user satisfaction of an adapted and translated mobile health application ASTHMAXcel© among adults with asthma in Pune, India.

OBJECTIVE: ASTHMAXcel© is a mobile application previously shown to improve asthma knowledge, control, and quality of life. In this study, we translated the application to Marathi for pilot testing in Pune, India in order to evaluate its impact on user satisfaction and asthma knowledge among adult asthma patients. METHODS: ASTHMAXcel© was adapted to Marathi with the help of asthma patients and clinicians from Bharati Hospital. 57 different asthma patients were then recruited and received the Asthma Knowledge Questionnaire (AKQ), Asthma Control Questionnaire (ACQ), and Mini Asthma Quality of Life Questionnaire (Mini-AQLQ) to complete at baseline. Study participants then completed the adapted ASTHMAXcel© application. Post-intervention, participants filled out a post-AKQ and Questionnaire for User Interface Satisfaction (QUIS). A subset of participants was also interviewed for qualitative feedback. Paired t-tests and Pearson's correlation were used for statistical analysis. RESULTS: Mean AKQ improved from 5.0+/-2.4 to 12.4+/-1.6 (p = 0.0001). QUIS results revealed that participants were highly satisfied with the application, scoring an average of 50 out of 54 maximum points. Better baseline asthma control was correlated with greater overall experience with the application (-0.110, p = 0.0417). Finally, the qualitative feedback revealed four themes for future refinement. CONCLUSION: The adapted version of ASTHMAXcel© was linked to significant improvement in patient asthma knowledge and a high level of user satisfaction. These results support the potential utility of mHealth applications in promoting guideline-based asthma care in India. However, further studies are needed to establish a causal relationship between ASTHMAXcel© and improved clinical outcomes.

Hypoxia Drives Centrosome Amplification in Cancer Cells via HIF1α-dependent Induction of Polo-Like Kinase 4.

Centrosome amplification (CA) has been implicated in the progression of various cancer types. Although studies have shown that overexpression of PLK4 promotes CA, the effect of tumor microenvironment on polo-like kinase 4 (PLK4) regulation is understudied. The aim of this study was to examine the role of hypoxia in promoting CA via PLK4. We found that hypoxia induced CA via hypoxia-inducible factor-1α (HIF1α). We quantified the prevalence of CA in tumor cell lines and tissue sections from breast cancer, pancreatic ductal adenocarcinoma (PDAC), colorectal cancer, and prostate cancer and found that CA was prevalent in cells with increased HIF1α levels under normoxic conditions. HIF1α levels were correlated with the extent of CA and PLK4 expression in clinical samples. We analyzed the correlation between PLK4 and HIF1A mRNA levels in The Cancer Genome Atlas (TCGA) datasets to evaluate the role of PLK4 and HIF1α in breast cancer and PDAC prognosis. High HIF1A and PLK4 levels in patients with breast cancer and PDAC were associated with poor overall survival. We confirmed PLK4 as a transcriptional target of HIF1α and demonstrated that in PLK4 knockdown cells, hypoxia-mimicking agents did not affect CA and expression of CA-associated proteins, underscoring the necessity of PLK4 in HIF1α-related CA. To further dissect the HIF1α-PLK4 interplay, we used HIF1α-deficient cells overexpressing PLK4 and showed a significant increase in CA compared with HIF1α-deficient cells harboring wild-type PLK4. These findings suggest that HIF1α induces CA by directly upregulating PLK4 and could help us risk-stratify patients and design new therapies for CA-rich cancers. IMPLICATIONS: Hypoxia drives CA in cancer cells by regulating expression of PLK4, uncovering a novel HIF1α/PLK4 axis.

Extracellular calcium alters calcium-sensing receptor network integrating intracellular calcium-signaling and related key pathway.

G-protein-coupled receptors (GPCRs) are a target for over 34% of current drugs. The calcium-sensing receptor (CaSR), a family C GPCR, regulates systemic calcium (Ca2+) homeostasis that is critical for many physiological, calciotropical, and noncalciotropical outcomes in multiple organs. However, the mechanisms by which extracellular Ca2+ (Ca2+ex) and the CaSR mediate networks of intracellular Ca2+-signaling and players involved throughout the life cycle of CaSR are largely unknown. Here we report the first CaSR protein-protein interactome with 94 novel putative and 8 previously published interactors using proteomics. Ca2+ex promotes enrichment of 66% of the identified CaSR interactors, pertaining to Ca2+ dynamics, endocytosis, degradation, trafficking, and primarily to protein processing in the endoplasmic reticulum (ER). These enhanced ER-related processes are governed by Ca2+ex-activated CaSR which directly modulates ER-Ca2+ (Ca2+ER), as monitored by a novel ER targeted Ca2+-sensor. Moreover, we validated the Ca2+ex dependent colocalizations and interactions of CaSR with ER-protein processing chaperone, 78-kDa glucose regulated protein (GRP78), and with trafficking-related protein. Live cell imaging results indicated that CaSR and vesicle-associated membrane protein-associated A (VAPA) are inter-dependent during Ca2+ex induced enhancement of near-cell membrane expression. This study significantly extends the repertoire of the CaSR interactome and reveals likely novel players and pathways of CaSR participating in Ca2+ER dynamics, agonist mediated ER-protein processing and surface expression.

LinkedImm: a linked data graph database for integrating immunological data.

BACKGROUND: Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. RESULTS: We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. CONCLUSION: We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

RNA-seq and miRNA-seq data from pharmacological inhibition of the G9a/GLP histone methyltransferase complex with UNC0642 in SAMP8 mice.

Growing evidence demonstrates the epigenetic modulation as a key event in Alzheimer's disease (AD) pathology. Furthermore, recent data suggests that the epigenetic regulation by the methyltransferase G9a is a crucial mechanism involved in learning and memory formation. Taking this into account, we hereby provide genomics data from pharmacological intervention with UNC0642, a potent and selective G9a/GLP in SAMP8 mice, a model of Alzheimer's disease (AD). We have generated novel RNA-seq and miRNA-seq data for three groups, healthy SAMR1, SAMP8 control and SAMP8 treated with UNC0642 (5 mg/Kg). Thus, the new data can be used to find miRNA regulation, and the mRNA's modified in AD under G9a/GLP inhibition.

G protein-coupled receptors as candidates for modulation and activation of the chemical senses in decapod crustaceans.

Many studies have characterized class A GPCRs in crustaceans; however, their expression in crustacean chemosensory organs has yet to be detailed. Class A GPCRs comprise several subclasses mediating diverse functions. In this study, using sequence homology, we classified all putative class A GPCRs in two chemosensory organs (antennular lateral flagellum [LF] and walking leg dactyls) and brain of four species of decapod crustaceans (Caribbean spiny lobster Panulirus argus, American lobster Homarus americanus, red-swamp crayfish Procambarus clarkii, and blue crab Callinectes sapidus). We identified 333 putative class A GPCRs- 83 from P. argus, 81 from H. americanus, 102 from P. clarkii, and 67 from C. sapidus-which belong to five distinct subclasses. The numbers of sequences for each subclass in the four decapod species are (in parentheses): opsins (19), small-molecule receptors including biogenic amine receptors (83), neuropeptide receptors (90), leucine-rich repeat-containing GPCRs (LGRs) (24), orphan receptors (117). Most class A GPCRs are predominately expressed in the brain; however, we identified multiple transcripts enriched in the LF and several in the dactyl. In total, we found 55 sequences with higher expression in the chemosensory organs relative to the brain across three decapod species. We also identified novel transcripts enriched in the LF including a metabotropic histamine receptor and numerous orphan receptors. Our work establishes expression patterns for class A GPCRs in the chemosensory organs of crustaceans, providing insight into molecular mechanisms mediating neurotransmission, neuromodulation, and possibly chemoreception.

Current clinical trials and patent update on lung cancer: a retrospective review.

Several clinical trials using different interventions are currently being sponsored to combat lung cancer at its different stages. The purpose of this study was to provide a portfolio of those trials. All active, open and recruiting clinical trials registered at ClinicalTrials.gov up to March 2018 were included. Information related to 6092 registered lung cancer trials was downloaded. Phase II trials were in the majority, comprising nearly 48.7% of total clinical trials with industry the major sponsor (41.3%) followed by NIH (12.3%). Multicenter studies were the norm accounting for 47.9% and the main study location was the USA (50.9%). Common interventions were radiation (26%), surgery (22%) and EGFR inhibitors (17%). Patent information includes major patent filing office and sponsors. The data analysis provides a comprehensive description of lung cancer trials.

Integrated Genomic and Functional Characterization of the Anti-diabetic Potential of Arthrobacter sp. SW1.

For decades, bacterial natural products have served as valuable resources for developing novel drugs to treat several human diseases. Recent advancements in the integrative approach of using genomic and functional tools have proved beneficial in obtaining a comprehensive understanding of these biomolecules. This study presents an in-depth characterization of the anti-diabetic activity exhibited by a bacterial isolate SW1, isolated from an effluent treatment plant. As a primary screening, we assessed the isolate for its potential to inhibit alpha-amylase and alpha-glucosidase enzymes. Upon confirmation, we further utilized LC-MS, ESI-MS/MS, and NMR spectroscopy to identify and characterize the biomolecule. These efforts were coupled with the genomic assessment of the biosynthetic gene cluster involved in the anti-diabetic compound production. Our investigation discovered that the isolate SW1 inhibited both α-amylase and α-glucosidase activity. The chemical analysis suggested the production of acarbose, an anti-diabetic biomolecule, which was further confirmed by the presence of biosynthetic gene cluster "acb" in the genome. Our in-depth chemical characterization and genome mining approach revealed the potential of bacteria from an unconventional niche, an effluent treatment plant. To the best of our knowledge, it is one of the first few reports of acarbose production from the genus Arthrobacter.

Description and genomic insights into a multidrug resistant novel bacterium Savagea serpentis sp. nov., isolated from the scats of a vine snake (Ahaetulla nasuta).

Two Gram-stain positive, endospore forming, non-motile, rod shaped bacterial strains SN6T and SN6b were isolated from scats of a mildly venomous vine snake (Ahaetulla nasuta). Strains were phenotypically resistant to multiple antibiotics of four different classes i.e. aminoglycosides, ß-lactams, fluoroquinolones and sulphonamides. Cells of both the strains were catalase positive and oxidase negative. Phylogenetic analysis based on 16S rRNA gene sequence analysis of these two strains showed closest similarity (99.2% and 99.3%) with Savagea faecisuis Con12T, the only species of the genus Savagea and ≤ 94.9% with the species of other closest genera of the family Planococcaceae. The 16S rRNA gene sequence similarity (99%), DNA-DNA relatedness (95%) and similar phenotypic characteristics between the strains SN6T and SN6b revealed their phylogenetic affiliation to the same species. Hence, strain SN6b is an additional strain of the type strain SN6T. DNA-DNA relatedness of strain SN6T with S. faecisuis Con12T was 32.8%. Predominant fatty acids were iso-C15:0 (32.0%), iso-C16:1 ω11c (19.2%) and iso-C17:1 ω10c (12.1%). MK-6 (100%) was the only respiratory quinone of strain SN6T. Diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine were the major polar lipids. Cell wall peptidoglycan was A4α; L-Lys-Gly-D-Glu type. The DNA G + C content (mol%) of SN6T was 40.8. Whole genome sequence of SN6T consisted of 26,37,389 base pairs in length with 2667 annotated genes, out of which 1021 corresponds to hypothetical proteins and 1646 with functional assignments including antibiotic resistance, multidrug resistance efflux pumps, invasion and virulence factors. Comparative polyphasic study of the strains SN6T, SN6b and S. faecisuis Con12T elucidated the differentiating characteristics which led to describing strain SN6T and SN6b as a novel species of the genus Savagea for which the name Savagea serpentis sp. nov is proposed. The type strain of Savagea serpentis is SN6T (= KCTC 33546T = CCUG 6786T).

Hemoglobin Induces Early and Robust Biofilm Development in Streptococcus pneumoniae by a Pathway That Involves comC but Not the Cognate comDE Two-Component System.

Streptococcus pneumoniae grows in biofilms during both asymptomatic colonization and infection. Pneumococcal biofilms on abiotic surfaces exhibit delayed growth and lower biomass and lack the structures seen on epithelial cells or during nasopharyngeal carriage. We show here that adding hemoglobin to the medium activated unusually early and vigorous biofilm growth in multiple S. pneumoniae serotypes grown in batch cultures on abiotic surfaces. Human blood (but not serum, heme, or iron) also stimulated biofilms, and the pore-forming pneumolysin, ply, was required for this induction. S. pneumoniae transitioning from planktonic into sessile growth in the presence of hemoglobin displayed an extensive transcriptome remodeling within 1 and 2 h. Differentially expressed genes included those involved in the metabolism of carbohydrates, nucleotides, amino acid, and lipids. The switch into adherent states also influenced the expression of several regulatory systems, including the comCDE genes. Inactivation of comC resulted in 67% reduction in biofilm formation, while the deletion of comD or comE had limited or no effect, respectively. These observations suggest a novel route for CSP-1 signaling independent of the cognate ComDE two-component system. Biofilm induction and the associated transcriptome remodeling suggest hemoglobin serves as a signal for host colonization in pneumococcus.

Hemoglobin stimulates vigorous growth of Streptococcus pneumoniae and shapes the pathogen's global transcriptome.

Streptococcus pneumoniae (Spn) must acquire iron from the host to establish infection. We examined the impact of hemoglobin, the largest iron reservoir in the body, on pneumococcal physiology. Supplementation with hemoglobin allowed Spn to resume growth in an iron-deplete medium. Pneumococcal growth with hemoglobin was unusually robust, exhibiting a prolonged logarithmic growth, higher biomass, and extended viability in both iron-deplete and standard medium. We observed the hemoglobin-dependent response in multiple serotypes, but not with other host proteins, free iron, or heme. Remarkably, hemoglobin induced a sizable transcriptome remodeling, effecting virulence and metabolism in particular genes facilitating host glycoconjugates use. Accordingly, Spn was more adapted to grow on the human α - 1 acid glycoprotein as a sugar source with hemoglobin. A mutant in the hemoglobin/heme-binding protein Spbhp-37 was impaired for growth on heme and hemoglobin iron. The mutant exhibited reduced growth and iron content when grown in THYB and hemoglobin. In summary, the data show that hemoglobin is highly beneficial for Spn cultivation in vitro and suggest that hemoglobin might drive the pathogen adaptation in vivo. The hemoglobin receptor, Spbhp-37, plays a role in mediating the positive influence of hemoglobin. These novel findings provide intriguing insights into pneumococcal interactions with its obligate human host.

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence.

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open-source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k-means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1.