Category Archives: Biology

Biology

Genomic attributes of airway commensal bacteria and mucosa | Communications Biology – Nature.com

Culture collection and isolate novelty

Lower airway bacteria were cultivated from bronchoscopic brushings from two asthmatics and three healthy individuals from the Celtic Fire Study (described below). We used a limited range of media with and without 0.5% mucin, followed by incubation in a standard atmosphere or an anaerobic workstation to capture 706 isolates. Those without overlapping 16S rRNA gene sequences were transferred to the Wellcome Sanger Institute and the whole-genome sequenced with assembly using Bactopia (v 1.4.11).

We cultured 651 isolates, 256 of which were successfully whole-genome sequenced. Of these, five sequences appeared mixed and were excluded. After removing duplicates on a 99.5% nucleotide identity threshold, 126 unique strains remained. The Bactopia quality report for the genome assemblies is reported in Supplementary Data1. Forty-four isolates were annotated to species level in accordance with MIGA24 (TypeMat and NCBIProk) and with GTDBtk. A further 30 species were identified by either MIGA (TypeMat and NCBIProk) or GTDBtk. The genome completeness and the contamination percentage were tested within the MIGA pipeline aligning 106 bacterial core genes25 (Supplementary Data2).

All isolates were assigned to genera in the TypeMat or NCBI prokaryotes database with P<0.05. Among these samples, we classified 49 Streptococcus, ten Veillonella, nine each of Gemella and Rothia, eight Prevotella, six each of Neisseria, Micrococcus and Pauljensenia, five each of Haemophilus and Staphylococcus, three Granulicatella, two each of Actinomyces, Cutibarterium and Fusobacterium and one Cuprividis, Leptotrichia, Microbacterium and Niallia, respectively (Fig.1a).

a Culture collection phylogeny based on average nucleotide identities between genomes with 1000bp fragment length. Putatively novel species are highlighted in red (indicating that it is not related to any species in the TypeMat DB or NCBI Prok DB (P<0.05) when assessed using MIGA and not assigned to a known species or incongruent species assignment using gtdbtk). Greyed-out isolates are not fully supported by MIGA and gtdbtk. Genome completeness and contamination are displayed as a bar chart. AMR finder was used to identify antimicrobial resistance genes at the protein level (red panel). Virulence factors were identified using the VFDB and Ariba databases and binned into 15 categories (heatmap). The asthma status of the host is indicated in the black asthma/control panel. Cultivation conditions are indicated in green circles for selected growth media, blue rectangles for aerobic, and white rectangles for anaerobic cultivation. Positive Gram staining for GNB, GNC, GPB, GPC, and other Gram staining is shown in black circles. The neuraminidase activity was tested if a blue star was present and was filled for the positive test and white for a negative test. b Taxonomic novelty as calculated by MIGA using TypeMat reference. The scatterplot shows support (P-value, vertical axis) for each taxon relative to complementary hypotheses that this taxon is a previously known one (red markers) or a novel one (cyan markers) at each taxonomic level (horizontal axis). Many of the isolate collections constitute novel species within known genera. c Composition of bacteria isolated and cultivated from five subjects. Counts are shown for all lineages from species level (outer circle) to phylum level (inner circle) in squared brackets. The ETE3 toolkit was used to fetch taxonomic lineages for all genera of cultured isolates101. The number of unique species was summed up and visualised along with their lineages using Krona tools102.

We defined a new species when isolates could not be assigned to known species in reference databases24. We classified isolates as putatively novel species when they exhibited no close relation to any species in the TypeMat or NCBI Prokaryotic Databases, determined by the MIGA tool with a P-value threshold of 0.05 and an incongruent species assignment indicated by gtdbtk.

Fifty-two isolates could not be assigned with P<0.05 to known species in the reference databases24 (Fig.1b). Twenty-eight of the putative novel species were contained within the Streptococcus genus, six within Pauljensenia (not previously recognised to be prevalent in the airways), and four each within Neisseria and Gemella (Fig.1c and Supplementary Data1).

Comparison of the entire sequences of our streptococcal isolates with 2477 public Streptococcus spp. sequences showed that the organisms were widely distributed amongst S. infantis, S. oralis, S. mitis, S. pseudopneumoniae, S. sanguinis, S. parasanguinis, and S. salivarius (Supplementary Fig.2).

We used the eggNOG (evolutionary genealogy of genes, Non-supervised Orthologous Groups) mapper tool (as previously for large-scale systematic genome annotations26) to assign by transfer 5,531 Kegg Ontology (KO) annotations for the 126 isolates. We encoded these in a binary matrix indicating presence or absence (Supplementary Data3) and constructed an isolate phylogeny after removing 254 zero-variance KOs (either present or absent in all isolates) and reducing identical KO presence/absence to single examples before hierarchical clustering with the Manhattan distance metric and complete linkage. The Dynamic Tree Cut algorithm27 identified 15 clusters of isolates that recovered known phylogenetic relationships (Fig.2a). Based on the observed 16S rRNA gene sequence similarity, we further divided one Streptococcus cluster into two (Strep I and Strep II, Fig.2a). Relative KO enrichment was estimated for each of the 16 clusters by contingency table analysis.

a Mapping of the 50 most abundant OTUs onto 126 novel airway isolates. Isolates are grouped into 16 clusters according to the distance and branching order of their inferred Kegg Ontology (KO) gene content. OTU/isolate nt identity is shown as 9597% (light blue), 9799% (medium blue), and 100% (dark blue). The complex relationship between OTUs and isolates reflects multiple copies of the 16S rRNA gene in different taxa, but in general, captures KO phylogenetic structures. b Comparison of abundance (left) and prevalence right) of bacterial OTUs in populations from northern (CELF) and southern (BUS) hemispheres. The species distribution is similar between the CELF and BUS studies. c Comparison of abundance (left) and prevalence right) of bacterial OTUs in the posterior oropharynx (ptOP) and the left lower lobe (LLL) in CELF subjects. The relative abundance of organisms in ptOP is very similar to those in the LLL, although absolute abundance is an order of magnitude lower in the LLL. Lower abundance OTUs in the CELF dataset are more prevalent in the upper than lower airways. d Spearman correlations between the abundance of organisms in the CELF ptOP samples, showing a high degree of positive and negative relationships between OTUs that is the basis of WGCNA network analysis. Common phyla are colour coded at the top of the matrix, and WGCNA modules (named for the most abundant membership) are at the bottom. Network module membership may be dominated by a single phylum (e.g., the Haemophilus or Streptococcus modules) or contain mixed phyla (e.g., the Veillonella module).

Annotation for the 5277 informative KOs (including duplicates removed during clustering) (Supplementary Data4) identified 247 uncharacterised proteins (Supplementary Data4). Features of particular interest among the known genes are summarised below.

Biofilm formation is a feature of respiratory pathogens, archetypically Pseudomonas spp. in patients with cystic fibrosis. Biofilm-associated genes were also common in the commensal collection (Supplementary File4b). Ninety genes were annotated with biofilm in their KO pathway descriptions, with cysE (serine O-acetyltransferase), vpsU (tyrosine-protein phosphatase), luxS (S-ribosylhomocysteine lyase), trpE (anthranilate synthase component I) and PYG (glycogen phosphorylase) present in >75% of isolates. Amongst the most abundant organisms, Haemophilus and Prevotella spp. had distinctive profiles of other biofilm pathway genes (Supplementary Data4).

Many of our isolates contained known genes for antimicrobial resistance (AMR) against tetracyclines and macrolides. Staphylococcus, Prevotella and Haemophilus spp. also possessed beta-lactam resistance (Fig.1a and Supplementary Data4). Virulence factors and toxins were concentrated in Streptococcus, Staphylococcus, Haemophilus, and Neisseria spp. (Fig.1a and Supplementary Data4). Although these annotations neither guarantee that the genes in question are expressed nor that they drive clinically relevant AMR or virulence, they do indicate such potential.

Competition between bacteria is fundamental to maintaining stable communities28. Genes with a KO pathway annotation for antibiotic synthesis (n=33) were present in many genera (Supplementary Data4). Arachin biosynthetic genes included acpP (acyl carrier protein) which was present in 120 isolates and auaG in seven (mostly Staphylococcus spp); rifB (rifamycin polyketide synthase) present in 20 (Veillonella and Staphylococcus spp.); BacF (bacilysin biosynthesis transaminase) present in 12 (Staphylococcus and Gemella spp.); and sgcE5 (enediyne biosynthesis protein E5) present in 12, mostly Haemophilus spp. Bacteriocin exporter genes blpB and blpA were present in 35 and 31 isolates respectively, predominately Streptococcus and Pauljensenia spp. (Supplementary Data4).

Toxins and antitoxin genes were common in the collection (Supplementary Data4), without distinctive enrichment in particular genera. They included homologues of antitoxin YefM (57 isolates); exfoliative toxin A/B eta, (57 isolates); toxin YoeB (51isolates); antitoxins HigA-1 (31) and HigA (30); antitoxin PezA (26); toxin RtxA (15); antitoxin HipB (14); toxin YxiD (13); antitoxin CptB (12); antitoxin Phd (11); and toxin FitB (10). These have not been previously recognised in commensal organisms and differ from the toxin spectrum of known airway pathogens29. They may have significant influences on the mucosa as well as other organisms.

Nitric oxide (NO) is a central host signalling molecule in the airways, where it mediates bronchodilation, vasodilation, and ciliary beating30. NO exhibits cytostatic or cytocidal activity against many pathogenic microorganisms31 and NO elevation in exhaled breath is used as a clinical marker for lower airway inflammation. Many isolate genes encoded NO reductases (Supplementary Data4), including norB (27 isolates); norV (11), norQ (5), norC (1) and norR (1). The hmp gene, encoding a NO dioxygenase, was present in 39 organisms. These enzymes may mitigate the antimicrobial activities of NO or affect host bronchodilation and mucus flow.

Iron is an essential nutrient for humans and many microbes and is a catalyst for respiration and DNA replication32. Host regulation of iron distribution through many mechanisms serves as an innate immune mechanism against invading pathogens (nutritional immunity)32.

We identified 47 genes with iron in their KO name (Supplementary Data2f). Those found in >75% of isolates were afuC (iron (III) transport system ATP-binding protein), ABC.FEV.P (iron complex transport system permease protein), ABC.FEV.S (substrate-binding protein), and ABC.FEV.A (ATP-binding protein). A further 19 genes were identified as members of haem pathways (Supplementary Data4).

Haemophilus spp. require haem for aerobic growth and possess multiple mechanisms to obtain this essential nutrient. These genes may play essential roles in Haemophilus influenzae virulence33. In our isolate collection sitC and sitD (manganese/iron transport system permease proteins) and fieF (a ferrous-iron efflux pump) were only found in Haemophilus spp., as were ccmA, ccmB, ccmC, ccmD (haem exporter proteins A, B, C and D) and hutZ (haem oxygenase). These are potential therapeutic targets.

The sphingolipids constitute an important class of bioactive lipids and include ceramide and sphingosine-1-phosphate (S1P). Ceramide is a hub in sphingolipid metabolism and mediates growth inhibition, apoptosis, differentiation, and senescence. S1P is a key regulator of cell motility and proliferation34.

Sphingolipids play significant roles in host antiviral responses35,36 and resistance to intracellular bacteria37. Their importance in humans is exemplified by a major childhood asthma susceptibility locus that upregulates ORMDL3 expression38. ORMDL3 protein acts as a rate-limiting step in sphingolipid synthesis39 and the ORMDL3 locus greatly increases the risk of HRV-induced acute asthma40.

De novo synthesis of sphingolipids is recognised in human bowel bacteria41 and maintains intestinal homoeostasis and microbial symbiosis42. In the skin, commensal S. epidermidis sphingomyelinase makes a crucial contribution to skin barrier homoeostasis43. Based on KO annotations, we did not find obvious SPT homologues in our isolates but identified 12 genes with putative roles in sphingolipid metabolism (Supplementary Data4). Of these, SPHK (sphingosine kinase, present in 12 isolates) which metabolises sphingosine to produce S1P; and ASAH2 (neutral ceramidase, present in seven isolates) have potential roles in modifying host inflammation and repair. These may interact with the ORMDL3 disease risk alleles described above.

Several genes present in the isolates may directly affect host immunity. These were enriched in Prevotella spp. (Supplementary Data4) and included immune inhibitor A (ina), a neutral metalloprotease secreted to degrade antibacterial proteins; Spa (immunoglobulin G-binding protein A), sbi (immunoglobulin G-binding protein Sbi); omp31 (outer membrane immunogenic protein); blpL (immunity protein cagA); and impA (immunomodulating metalloprotease).

A conserved commensal antigen, -hexosaminidase (HEXA_B), has a major role in induction of anti-inflammatory intestinal T lymphocytes44, and is present in 59 of our isolates with enrichment in Prevotella, Streptococcus and Pauljensenia spp.

Systemic lupus erythematosus (SLE) and Sjgren syndrome are chronic autoimmune inflammatory disorders with multiorgan effects. Lung involvement is common during the course of the disease45. Our Neisseria isolates contain a 60kDa SS-A/Ro ribonucleoprotein (Supplementary Data4) that is an ortholog to the human RO60 gene, a frequent target of the autoimmune response in patients with SLE and Sjgrens syndrome.

Other bacterial genomes contain potential Ro orthologs46, and a bacterial origin of SLE autoimmunity has been suggested47. Here, the abundance of Neisseria spp. in human airways and their close proximity to the mucosa are of interest, as is a recent report that the lung microbiome regulates brain autoimmunity48, and an earlier observation that T cells become licensed in the lung to enter the central nervous system49.

It is relevant that products of cognate microbial-immune interactions in the airways have direct access to the general arterial circulation through the left side of the heart, whereas molecules and cells carried in venous blood from the gut undergo extensive filtration and metabolism in the liver before accessing more distant sites.

Most respiratory viruses, including SARS2-Cov-19, have RNA genomes, and RNA-targeting CRISPR vectors have the potential to prevent or treat viral infections50. Type III RNA-targeting system elements (such as cas10, cas7, csm2 and csm5)51 are present in our isolates (particularly Fusobacteria and Prevotella spp.), as is the Type II system element cas9 (Supplementary Data4).

We sought context for our culture collection within the ecological variation of different geographic and anatomical locations. We studied airway microbial communities in 66 asthmatics and 44 normal subjects recruited from centres in Dublin (48 subjects), Swansea (46 subjects) and London (16 subjects) (collectively known as the Celtic Fire Study (CELF)). Swabs were taken from the posterior oropharynx (ptOPs) and bronchoscopic brushings from the left lower lobe (LLL) in all subjects. When tolerated, the left upper lobe (LUL) was also brushed in 52 subjects. We compared the European CELF microbial communities to 527 ptOP samples from an adult population sample in Busselton, West Australia (BUS)18. Operational Taxonomic Units (OTUs) were identified by sequencing the 16S rRNA gene amplicon and compared with the assembled genomes from our culture collection.

In the CELF ptOP samples, 17 operational taxonomic units (OTUs) covered >70% of the abundance and 41 OTUs covered >85% (Supplementary Data5). Coverage was less in LLL and LUL samples (respectively 64% and 50% at the 70% threshold), due to the expansion of H. influenzae (OTU Haemophilus_14694) and Tropheryma whipplei (OTU Glutamicibacter_5653) in the pulmonary samples, particularly those from asthmatics (Supplementary Data5).

Fifteen of the 17 most abundant OTUs were mapped to at least one isolate using a 99% nucleotide (nt) identity, and eleven of the next 24 OTUs were mapped to a cultured organism. Genera of moderate abundance (2.8%-0.4% of the total) yet to be cultivated include Fusobacterium, Selenomonas, Alloprevotella, Porphyromonas, Leptotrichiaceae, Megasphaera, Lachnospiraceae, Solobacterium, and Capnocytophaga.

OTUs corresponding to isolates for Staphylococcus, Micrococcus and Cupriavidus spp. had minimal representation in the community OTU analyses, although Staphylococcus aureus is a recognised lung pathogen. Their appearance in the isolates may represent oral or skin contamination or assertive growth in culture.

Mapping of the 50 most abundant OTU sequences onto the 126 isolates revealed complex relationships that reflect multiple copies of the 16S rRNA gene in different taxa52 (Fig.2a). In general, however, OTU assignment reflected the principal KO phylogenetic structures and referencing of OTU communities to our isolate genomes may still inform on community functional capabilities.

The 16S rRNA gene sequences poorly detected the extensive diversity of Streptococcus spp. in airways, as noted previously18. However, combinations of OTUs can be seen to form barcodes (Fig.2a) that may refine Streptococcus spp. identification into their three main KO phylogenetic groups.

The taxa defined by OTUs and their relative abundances were similar in CELF ptOP and CELF LLL samples and to the normal population in BUS ptOP (Fig.2b, c). Other than the most abundant organisms, the prevalence of most OTUs was lower in the LLL than in the ptOP (Fig.2c). The mean bacterial burden was much higher in ptOP samples from CELF than in the LLL (log10 mean 7.860.07 vs 5.060.05), consistent with previous studies8,16,17.

Strong correlations and anti-correlations were present between the abundances of OTUs in data from each site (exemplified for CELF ptOP samples in Fig.2d, and previously shown for the BUS ptOP results18). We used WGCNA analysis to find networks (named arbitrarily with colours) within these correlated taxa. Network structures were consistent in the CELF and BUS ptOP communities (Supplementary Figs.3 and 4), but less distinct in the lower airway samples where taxa were less diverse and of lower abundance (Supplementary Fig.5).

Networks often contained closely related species but also extended beyond phylogenetically related organisms (Fig.2d and Supplementary Fig.6). For example, in the CELF ptOP networks (Fig.2d and Supplementary Fig.6) there are phylogenetically homogeneous modules of Streptococci (blue, red and green-yellow), Gemella (magenta), Haemophilus (black and pink) and Granulicatella (purple).

Of interest is the brown module in the CELF ptOP samples, which contains multiple Prevotella and Veillonella spp. of high abundance. The presence of biofilm elements in Prevotella spp. described above supports a hypothesis that these organisms may adhere to form a basic commensal carpet of the airways18.

Both the CELF ptOP and BUS ptOP networks recovered the phylogenetic relationships found in the KO analysis amongst Streptococcus isolates. The three clusters of Streptococcus isolates (Strep. I-III) map to distinct sets of OTUs using sequence similarity (Fig.2a), and this similarity is also uncovered in the WGCNA network modules in both ptOP networks (Supplementary Fig.7).

Subtle alterations in bacterial community composition (dysbiosis53) are recognised in many diseases with microbial components. Community instability and inflammation in the presence of mild viral infections5 should be added to the recognised features of loss of diversity and pathobiont expansion in asthma and COPD. We, therefore, sought insights into airway dysbiosis in our subjects from genomic sequencing of the commensal organisms.

We explored underlying components of airway communities by using Dirichlet-Multinomial Mixtures (DMM)54 on all samples from the BUS and CELF subjects, finding that samples formed predominantly into two clusters (Airway Community Type 1 and 2: ACT1 and ACT2) (Fig.3a). The main drivers for the two pulmotype clusters were identified as Streptococcus, Veillonella, Prevotella and Haemophilus spp. in descending order of relative abundance across all samples. ACT1 was dominated by Streptococcus, Veillonella and Prevotella in 410 samples; whilst ACT2 was dominated by Streptococcus, Veillonella and Haemophilus in 478 samples (Fig.3a). Principal coordinates analysis based on Bray-Curtis-distance (-diversity) of the airway microbiota confirmed significant overall compositional differences between the two community type clusters (PERMANOVA P-value>0.001) (Fig.3b).

a Main drivers of Dirichlet-multinomial model-based airway communities. b Beta diversity based on Bray-Curtis dissimilarity principal coordinate analysis showing separation of the two communities. c Consistency of airway community assignment between samples of the same and different donors (left) and sampling sites (right). d Alpha diversity measures and correlations. e Univariate associations of CELF 16S samples binned on phylum level to metadata. f Proportion of community assignments between ptOP samples of different study origins, sampling sites and disease groups. g relative abundance of most abundant genera based on CELF samples 16S rRNA. h Univariate metabolite associations based on binning of CELF 16S rRNA sequences onto isolate annotation.

Congruence analysis of CELF samples (Fig.3c) confirmed consistency in assignment for samples coming from the same donor (<0.005) or the same sampling site (<0.005).

We performed univariate analysis to investigate the association between CELF subject metadata and potential indicators of dysbiosis, specifically, evenness and richness (Fig.3d), and bacterial abundance at the phylum level (Fig.3e). Features describing clinical phenotypes and sample origin were often strongly collinear. We, therefore, assessed found associations in turn for retained significance with each potential confounder, using a nested rank-transformed mixed model test55 and considering repeated sampling of patients as a random effect.

We saw pervasive effects both on alpha diversity and phylum level of the tested predictors (Fig.3d, e). Importantly, the Shannon index and richness were significantly decreased with asthma status and severity (MWU false-discovery rate (FDR)<0.1) (Fig.3d).

We found an increase (although not significant) of the Proteobacteria Phylum associated with asthma status (Fig.3e), in line with the taxonomic profile of patients with asthma vs. healthy controls (Fig.3g). This is consistent with many reports of Proteobacteria excess in asthmatic airways8,9,56. Type 2 communities were enriched in subjects with positive asthma status in all sample sites and in CELF subjects overall (Fig.3f).

We examined the impact of the study, asthma status, and sampling site on the distribution of community types in the CELF thoracic samples, using logistic regression models with sex and age as control variables. The results indicated significant differences in ACT proportions across different sampling sites: LUL vs. OTS: odds ratio 95% confidence interval 0.1350.444 (p-val: 3.1e-07); LLL vs. OTS: 0.0490.249 (P-val: 5.0e-10). Statistical significance was more marked for the left upper lobe (FDR q-value<0.001) than the left lower lobe (q<0.10).

We extrapolated metabolic activities from binning 16S rRNA gene abundance onto the isolate KOs using PICRUSt57, revealing metabolite profiles that distinguished measures of diversity and location within upper or lower airways (Fig.3h), as well as distinctive features of asthma and dysbiosis.

In order to relate our mapped microbiome to its ecosystem, we sought host components of the microbial-mucosal interface by serial measurements of global gene expression and supernatant metabolomics during full human airway epithelial cell (HAEC) differentiation in an air-liquid interface (ALI) model. We hypothesised that the transition from monolayer to ciliated epithelium over 28 days would be accompanied by the progressive expression of genes and secretion of metabolites for managing the microbiota.

HAEC from a single donor were grown in triplicate and harvested on days 0, 2, 3, 7, 14, 21 and 28. Trans-epithelial resistance (TEER) rose from 7.40.3 on day 0 to 1551113 on day 28, and MUC5AC mRNA production rose 30-fold over the same period (Supplementary Fig.8), indicating full epithelial development.

We found 2553 significantly changing transcripts organised into eight core temporal clusters of gene expression (Limma, 3.22.7) (Fig.4a and Supplementary Data6). Late peaks of expression were found in four clusters, three of which (CL2, CL4 and CL5) contained many genes likely to interact with the microbiome (Supplementary Data6). Transcripts in the other upgoing cluster (CL3) were elevated early and late in differentiation and were enriched for genes mediating cell mobility and localisation. Genes of particular interest in the other upgoing clusters are as follows.

a Global gene expression was measured 7 times over 28 days in an air-liquid model of epithelial differentiation (monolayer to ciliated epithelium). A total of 2,553 transcripts, summarised by 8 core temporal profiles, showed significant variation in abundance during mucociliary development. Hallmark functional roles are shown for each cluster. Clusters CL2, CL3, CL4 and CL5 show late peaks of expression and contain genes that can interact with the microbiome. Upregulated chemokines and immune-function genes are also noted within the clusters. b Metabolites (square) measured in the supernatant of the fully differentiated airway cells were linked to genes (circle) identified in bacterial isolates. Arrows indicate if the reactions were reversible or irreversible, with metabolites as substrates and products. These networks were built based on KEGG pathways. c Binary heatmap displaying the presence (1) or absence (0) of genes (columns) identified in the genomic sequences of bacterial isolates (rows). Bacterial isolates are organised into Kegg Ontology phylogeny clusters (see Fig.2). Gene annotations (top) indicate the frequency of the gene: frequent for genes in >75% of isolates, intermediate for genes in 2575% of isolates and rare for those in <25% of isolates.

Mucosal mucins are central to mucosal function and integrity, providing a source of nutrients and sites for tethering of commensals58, whilst restricting the density of organisms through upward flow by beating cilia59. Interactions of mucins with microbiota play an important role in normal function58, and direct cross-talk between microbes and mucin production is likely59.

In our ALI model, progressive up-regulation of the major secreted respiratory mucins MUC5AC and MUC5B in CL2 was accompanied by the membrane-associated MUC20 (Supplementary Data6). In contrast, CL5 contained three membrane-associated mucins (MUC13, MUC15, MUC16). These mucins do not form gels and are anchored to the apical cell surface, where they present a glycoarray for selective interactions with the microbial environment58.

Within CL5 we also found 17 gene families and 175 genes with putative roles in ciliary function, ciliogenesis, or spermatogenesis (Supplementary Data6). Mutations in many of these genes are known to cause primary ciliary dyskinesia (PCD)60, which results in recurrent pulmonary infections. Other genes in this list are candidates for mutation in cases of PCD without known cause.

The most significant effects (top hits) in CL2 included ENPP4 (which promotes haemostasis); ALOX15 (which generates bioactive lipid mediators including eicosanoids); GLIPR2 (which enhances type-I IFNs); MPPED2 (a metallophosphoesterase active in infection); INSR (insulin receptor); and MIR223 (an inhibitor of neutrophil extracellular trap (NET) formation in infection) (Supplementary Data6).

Immune-related genes significantly expressed in CL5 included complement factor 6 (C6) which forms part of the membrane attack complex. C6 deficiency is associated with Neisseria spp. infections. CD38 was also highly expressed, and its product is an activator of B-cells and T-cells.

Top hits in CL4 include ADH1C, an alcohol dehydrogenase; GSTA2 with a known role in the detoxification of electrophilic carcinogens, environmental toxins and products of oxidative stress by conjugation with glutathione; ACE2, the SARS2-Cov-19 binding site which cleaves angiotensins; and PIK3R3 which phosphorylates phosphatidylinositol to affect growth signalling pathways (Supplementary Data6).

CL4 contains five members of the cytochrome P450 families with potential roles in the detoxification of microbial products, including CYP2F1 (which modifies tryptophan toxins and xenobiotics); CYP4X1 (unknown substrates); CYP4Z1 (benzyl esters); CYP4F3 (Leukotriene B4); and CYP2C18 (sulfaphenazole). Also in CL4 were transporters SLC10A5 (substrate bile acids); SLC27A2 (fatty acids); SLC1A1 (glutamate); SLC4A11 (borate); SLC25A4 (ADP/ATP in mitochondria); SLC45A4 (sucrose); SLC25A28 (iron); and SLC39A11 (zinc).

Enrichment of genes for detoxification and transport was also present within CL2, which included CYP4B1 (substrate fatty acids and alcohols); CYP4V2 (fatty acids); CYP2A13 (nitrosamines); CYP2B6 (xenobiotics); CYP26A1 (retinoids); and CYP4F12 (arachidonic acids). Transporters included SLC40A1 (iron); SLC13A2 (citrate); SLC15A2 (small peptides); SLC12A7 (KCl co-transporter); and SLC35A5 (nucleoside sugars).

The bronchial mucosa is innervated with vagal sensory unmyelinated fibres that detect airway luminal substances and mediate smooth muscle tone, mucus secretion, and cough61. Airway sensory nerves are directly involved in immune or inflammatory responses, themselves releasing proinflammatory molecules (neurogenic inflammation)62,63. Neuroinflammation can change receptors, ion channels, neurochemistry, and fibre density64. It contributes to the disabling syndrome of cough hypersensitivity and chronic cough65.

A basis for innervation can be seen in top hits from CL2, which included ENPP5 and HECW2, which have putative roles in the development of airway sensory nerves (Supplementary Data6). Interestingly, CL2 and CL4 together contained ten members of the protocadherin beta gene family (PCDHB2, PCDHB3, PCDHB4, PCDHB5, PCDHB10, PCDHB12 and PCDHB18P in CL2; PCDHB13, PCDHB14, and PCDHB15 in CL4). Interactions between protocadherin beta extracellular domains specify self-avoidance in specific cell-to-cell neural connections66, and their abundant presence here may regulate singular neural-mucosal cell coherence.

Metabolites are central to biological signalling, and so we used the same time-series model of AEC differentiation to measure levels of metabolites released into the culture media of the cells (Supplementary Data7).

We then mapped the ALI culture metabolites to enzymes in matching bacterial pathways identified within the KO of isolate genomes (Fig.4b), based on direct reactions, as substrates or products. Notable interactions include amino acids, nucleotides and compounds involved in energy metabolism. The metabolite-related KOs exhibited distinctive patterns within the isolate phylogeny (Fig.4c).

Enrichment of these KOs onto global human and bacterial KO pathways with iPath67 is shown in Supplementary Figs.9 and 10. These suggest folate biosynthesis is ubiquitous amongst airway organisms, valine, leucine and isoleucine metabolism to be of intermediate importance and alanine, aspartate and glutamate metabolism to be rare functions amongst the isolates.

Original post:

Genomic attributes of airway commensal bacteria and mucosa | Communications Biology - Nature.com

Biology

Former Zookeeper Hopes to Share Passion for Biology as a Science Educator – Georgia State University News

story by Claire Miller

When she was growing up, Danielle Nawy would always ask her parents to take her to the zoo or the aquarium.

Her love for science and for animals in particular led her to earn a bachelors degree in zoology and begin a career as a zookeeper.

Nawy spent a few years working as a bird trainer and a grasslands zookeeper for the zebras and giraffes at Zoo Knoxville in Knoxville, Tenn. When she and her husband moved to Atlanta, she joined the staff at Zoo Atlanta, caring for the elephants, naked mole rats and meerkats.

I always get asked about my favorite animal to work with, but truthfully, I find that an incredibly difficult question to answer. There was something extraordinary about every animal I worked with, she said. But if I had to pick, it would be a tie between elephants and giraffes. Elephants are intelligent and they force you to be creative to keep them enriched. And training the giraffes and seeing them make strides towards certain behaviors was one of the highlights of my career.

Nawy also enjoyed talking with zoo guests about animals and conservation issues.

I fell in love with sharing my knowledge with others, she said. I would think about ways to expand my programming so that I could reach more guests and send them home with incredible messaging.

When she decided she wanted to pursue a different career, Nawy applied to Georgia State University to earn her Master of Arts in Teaching in Science Education.

She will graduate this spring from the College of Education & Human Developments Department of Middle and Secondary Education and hopes to find a job as a high school science teacher, where she can share her biology and zoology knowledge with her students.

Educators like Nawy can play a key role in encouraging girls to consider careers in science. This month, the United Nations will celebrate the International Day of Women and Girls in Science as a reminder that women and girls play a critical role in science and technology communities and that their participation should be strengthened.

It is important for women and girls to pursue science because there are not a lot of us. Most of the studies being conducted and decisions being made within the field are coming from a male perspective, Nawy said. Fifty percent of the world's population is women, so our perspectives should be seen within those studies and decisions, too.

Continue reading here:

Former Zookeeper Hopes to Share Passion for Biology as a Science Educator - Georgia State University News

Biology

Uses BgRT Radiation Therapy to Target Tumor – City of Hope

City of Hope continues to change the cancer treatment paradigm by leaning into available technology and adopting and applying it in innovative ways to care for its patients.

It was only the second institution in the world to begin using a novel radiation machine called RefleXion X1, which allows radiation oncologists to see more tumors throughout the body and specifically target them with radiation while minimizing exposure that can damage surrounding tissue.

More recently, City of Hope made history again by successfully treating its first patient using the RefleXion X1 machine and applying SCINTIX biology-guided radiation therapy, or BgRT. This is a novel and innovative form of radiation delivery that uses a signal generated by position emission tomography (PET) to guide external beam radiation therapy. It is a technology breakthrough that uses live, continuously updated data throughout the entire treatment session to determine exactly where to deliver radiotherapy to biologically active tumors.

What we want to do is shape the beam, so the tumor gets high dosage but the area around it does not, explained An Liu, Ph.D., clinical professor of radiation oncology. The more degrees of freedom we have, the better we can do that, and RefleXion and SCINTIX create that landscape and opportunity.

Using the PET signal may allow us to reduce the beam field size, allowing our radiation oncology team to reduce toxicity and avoid the need to manage motion, added Terence Williams, M.D., Ph.D., chair of radiation oncology.

This technology is evolving, he said. Eventually, it may also allow us to more comprehensively treat patients with many metastases throughout the body for more complete metastatic ablation. Williams said with this first BgRT patient, City of Hope join the ranks of the National Cancer Institute-designated comprehensive cancer centers at Stanford University and the University of Texas-Southwestern in being the first institutions in the world to use RefleXion SCINTIX technology. City of Hope was the third institution to adopt this new technology and install the equipment, just behind Stanford and UT Southwestern, and second after Stanford to begin treating patients.

Radiation oncologist Sagus Sampath, M.D., medical director for Duarte Radiation Oncology, is the attending physician for the first BgRT patient, a 65-year-old lung cancer patient who came to City of Hope late last year.

The patient was newly diagnosed and came to us with his primary tumor in the left lung and a single metastasis in his left femur, Sampath said. We opted to treat the femur first and offered the patient a PET scan to see if he might be eligible for the SCINTIX technology.

The PET scan confirmed a higher PET signal, or bright spot, associated with the left femur metatasis, signaling the presence of cancer cells that could be treated with a potentially more precise, biologically targeted radiation approach.

It was that bright spot that caught my eye, Sampath said. The location of the metastasis within the femur made it feasible to offer a single fraction of treatment. This approach would also minimize any delays in starting my patients chemotherapy.

Sampath explained that RefleXion X1 could zero in on the bright spot in the leg through its SCINTIX technology by using the tumors own biological signature.

The RefleXion machine enabled us to use the patients own PET signal from the patients own tumor to define our approach to how we treat his cancer. Thats the novel piece of this. It was all about the patients biology from start to finish, Sampath said.

With the femur radiation complete, the patient started a course of chemotherapy. He will be returning soon to start a course of concurrent chemotherapy and radiation to treat his primary lung tumor. Sampath said the radiation oncology team is encouraged that this latest success establishes a precedent that can pave the way for more patients to benefit from the BgRT approach.

We have carried this one through and broken through the glass ceiling, Sampath said. We look forward to being able to offer this to more patients who can benefit from this leading-edge technology.

Williams agreed that treating this first patient is an important milestone for City of Hope.

Dr. An Liu and the rest of the physics team in particular Drs. Chunhui Han and Tyler Watkins have been working tirelessly to make this happen, he said. Im also thankful to our Duarte physicians who have helped us identify eligible patients for this treatment, and to Drs. Sagus Sampath and Stephanie Yoon for taking such good care of this first patient. Kudos also to our dosimetry and radiation therapy technology teams for getting us to this first-patient goal.

The patients successful treatment was very much a team effort, Sampath said, one that was years in the making.

The success of completing our first BGRT stems from months of accumulating experience across our entire department, including our therapist, dosimetry, physics and physician teams, Sampath said. I would like to personally thank all my colleagues, especially my fellow physicians, for their critical contributions in helping our department reach this important breakthrough and turning point.

Williams, Sampath and An all agreed that this first treatment marks the launch of a new era of radiation treatments and opens the door to a number of new clinical trials led by many physicians in the department, including Arya Amini, M.D., Jeffery Wong, M.D., Savita Dandapani, M.D., Ph.D., and many more.

Liu said achievements like this are all in service to City of Hopes mission.

The only way we can advance cancer cures is to be a pioneer in the field, he said. Thats who we are at City of Hope.

More here:

Uses BgRT Radiation Therapy to Target Tumor - City of Hope

Biology

Renowned evolutionary biologist to speak for SFA’s Darwin Day event | SFA – Stephen F. Austin State University

NACOGDOCHES, Texas In a celebration of scientific curiosity and the contributions of biologists, Stephen F. Austin State Universitys Department of Biology is set to celebrate Darwin Day Feb. 12 with special guest speaker Dr. David Hillis from The University of Texas at Austin.

Embracing the spirit of inquiry that defines Charles Darwin's groundbreaking contributions to evolutionary theory, Hillis will give his talk on Darwins Tree of Life hypothesis.

Hillis is the Alfred W. Roark Centennial Professor in Natural Sciences at UT Austin, where he studies molecular evolution and biodiversity in the Department of Integrative Biology. He is the director of UT Austins Biodiversity Center and also directs the Deans Scholars Program of the College of Natural Sciences.

"We are delighted to have Dr. Hillis on the SFA campus to speak at this special event as we gather together to celebrate, remember and reflect on not only the contributions of Darwin but also the contributions of many scientists in general," said Dr. Carmen Montaa, assistant professor of biology.

Hillis research is focused on the tree of life and how we can use it to understand processes of evolution. He is one of the foremost evolutionary biologists today investigating the evolutionary relationships among living organisms. His work has helped the study of the evolutionary development of a species or a group of organisms throughout most fields of molecular biology in recent years, from studies of the epidemiology of human immunodeficiency viruses to studies of the origin of life.

Hillis research appears in over 200 scientific publications, and he has authored numerous books, including his most recent: Armadillos to Ziziphus: A Naturalist in the Texas Hill Country." In recognition of his contributions to evolutionary biology, he has received many honors, including being elected to the American Academy of Arts and Sciences as well as the National Academy of Sciences. He has served as president of the Society for the Study of Evolution and the Society of Systematic Biologists.

Hillis will give his featured talk, Applications of the Great Tree of Life, from noon to 1 p.m. Feb. 12 in the Miller Science Building, Room 139, on the SFA campus.

ABOUT STEPHEN F. AUSTIN STATE UNIVERSITY Stephen F. Austin State University, the newest member of The University of Texas System, began a century ago as a teachers college in Texas oldest town, Nacogdoches. Today, it has grown into a regional institution comprising six colleges business, education, fine arts, forestry and agriculture, liberal and applied arts, and sciences and mathematics. Accredited by the Southern Association of Colleges and Schools, SFA enrolls approximately 11,000 students while providing the academic breadth of a state university with the personalized attention of a private school. The main campus encompasses 421 acres that include 37 academic facilities, nine residence halls, and 68 acres of recreational trails that wind through its six gardens. The university offers more than 80 bachelors degrees, more than 40 masters degrees and four doctoral degrees covering more than 120 areas of study. Learn more at sfasu.edu.

See more here:

Renowned evolutionary biologist to speak for SFA's Darwin Day event | SFA - Stephen F. Austin State University

Biology

State biologists want you to send them owl vomit – Bangor Daily News

Maine biologists are asking people to send them owl pellets as part of a national study.

Owl pellets can be equated to a cat hairball. When an owl eats its prey, the parts, such as hair and bones, that it cannot digest gather in its gizzard where they are compacted into a pellet. The owl regurgitates or vomits the indigestible pellet.

The owls diet includes small mammals, birds, amphibians and invertebrates, according to the Maine Department of Inland Fisheries and Wildlife.

Researchers hope to learn more about owl numbers, what they eat and the health of the birds and of their prey. The information Mainers gather will be added to a national study of owls.

The Maine Owl Project is a collaboration between the Maine Department of Inland Fisheries and Wildlife, University of New England and the U.S. Fish and Wildlife Service.

For this and several other research projects, state biologists rely heavily on community scientists, Maine residents who add their own observations based on forms and instructions the researchers provide. The forms stress that the well-being of the owls takes precedence over the research, and ask community scientists to try not to disturb the birds themselves.

Researchers hope that all of the information gathered will give them a clearer picture of owl biology, habits and habitat, plus raise public awareness about the birds.

More than 3,000 community scientists helped with a 40-year project to document numbersand locations of the states amphibians and reptiles. That information will be published next year.

Financing for the owl pellet studycomes from the Maine Outdoor Heritage Fund.

Owl pellets will be sent to UNE researcher Zach Olson.

Original post:

State biologists want you to send them owl vomit - Bangor Daily News

Biology

Dan Bush named a pioneer member for the American Society of Plant Biology – College of Natural Sciences – Colorado State University

Dan Bush, a renowned plant biologist and former chair of the Department of Biology and vice provost for faculty affairs at CSU, was recently named a pioneer member for theAmerican Society of Plant Biology (ASPB).

This prestigious recognition honors the work of researchers who have made significant contributions to the field of plant science and the scientific community, and who take seriously the mentorship of future researchers. The recognition includes fundraising of $5,000by the members former graduate students, postdocs, colleagues and friends that is used to support outreach and mentorship of young scientists.

Dan has been a tremendous mentor and friend to me. His impact on plant science is only overshadowed by his positive impacts on his mentees, said Cris Argueso, an associate professor of agricultural biology at CSU.

Bush said that the ASPB had a profound impact on his career and development as a plant biologist. He attended his first society meeting in 1983, coincidentally held at CSU.

I was awestruck by the diversity of plant science presented at the meeting, he said. The society played a central role in my career ASPB has had a profoundly positive impact on the plant biology discipline and I am proud of my service to the society.

Throughout Bushs career, he took on leadership roles within ASPB: organizing annual meetings, serving on the editorial board on the societys journal, chairing the Midwest section of the society, elected secretary and president of the society, and serving as chair of the board of trustees.

Dr. Dan Bush is a visionary scientist and a leader with a tireless commitment to advancing science, especially plant biology, said Anireddy S.N. Reddy, professor of biology at CSU. His decades of distinguished service and contributions to the plant science community at the national and international level in many leadership roles in different societies, including the ASPB, the American Association for the Advancement of Science and at Colorado State University, are impressive.

Beyond ASPB, Bushs career ethos was marked by a strong sense of scientific inquiry, collaboration and thoughtful mentorship.

He started as an art student at Humboldt State University in California (HSU), before finding inspiration from his first mentor, Dan Brant, a biology professor at HSU. Brant lived a life of inquiry, said Bush. He had an enormous curiosity about everything I spent a summer building a house with him and shortly thereafter became a biology major!

Bush later earned his Ph.D. at the University of California, Berkeley and did postdoctoral research at the University of Maryland.

He joined the Agricultural Research Service and the Plant Biology Department at the University of Illinois in 1984 where he made his first significant research achievements describing the transport properties of proton-coupled sucrose and amino acid transporters in purified membrane vesicles, and eventually cloning many of them by complementing yeast transport mutants with plant cDNA expression libraries. It was also at Illinois that he discovered sucrose is a signal molecule that controls carbon allocation from leaf tissue to the non-photosynthetic organs of the plant.

While I consider these and many other discoveries to be important contributions to plant science, I believe my most important contributions have been in the training of many Ph.D. and postdoctoral students, he wrote in his autobiography for ASPB. I am exceedingly proud of their successes and contributions to basic understanding of plant biology.

This philosophy of mentorship extended beyond lab work and into his classroom as well.

As an educator, I tried to assist students in engaging in active learning, as I helped them build a foundation of basic concepts and knowledge of biological systems.One of the challenges of any biology class is walking students through the depth of understanding we have of many biological processes while also exciting them about the plethora of unsolved biological questions, he said.

Bush brought this passion for plant biology education to CSU, where he served as chair of the Department of Biology from 2003 2012. In 2012 he became vice provost for faculty affairs, where he served CSU until his retirement in 2020.

As chair of biology, I am very proud of the many talented young faculty we hired and our conscious efforts to mentor them as they crafted their successful careers at CSU, he said. Many are now leaders in their fields and at CSU. As vice provost for faculty affairs, I am very proud of our work with departments making sure they set clear expectations for young faculty. It is exceeding important that young faculty understand the expectations for scholarly and teaching achievement, as well as their role as engaged citizens in academia.

Bush said he feels extremely lucky to have spent a career engaged in solving challenging scientific questions, working with likeminded colleagues and training the next generation of inquiry-driven plant scientists.

Read more about Bushs legacy in his biography and on SOURCE.

Read this article:

Dan Bush named a pioneer member for the American Society of Plant Biology - College of Natural Sciences - Colorado State University

Biology

Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit … – Nature.com

Figure1 illustrates the main idea of the proposed RI measuring technology. It leverages a suitably engineered ultra-dark hydrophilic surface of palladium (Pd). When a specimen carried inside a droplet of phosphate buffer solution (PBS) deposits on the Pd surface, it anchors itself to the surface at multiple points. The hydrophilic nature of the Pd surface causes the PBS to spread over the sample, resulting in the evaporation of the liquid within one minute of the deposition. The lack of liquid produces progressive dehydration of the specimen, causing it to flatten and stretch on the surface, forming a suspended, thin biological film. When a white light source illuminates this structure, the reflection spectrum shows complex frequency modulations based on interference-generated structural colors (Fig.1b). A conventional red, green, and blue (RGB) camera converts every pixels input spectral power distribution (SPD) into a triplet of RGB values.

a Deposition of a biological specimen using a PBS droplet onto a nanostructured Pd surface. b Stretched specimen acting as a thin film that exhibits interference-based colors when illuminated. Recording of spatially dependent colors by a digital camera. c Camera conversion of analyte SPD into RGB values. d Recovered thickness map for an HCT-116 colon cancer cell. e Micrographs of an HCT-116 cell. The color overlays indicate subcellular regions with similar refractive index.

The camera integrates its color-matching functions (CMFs) with the input SPD during the conversion. The CMFs (Fig.1c, b(), g(), and r() curves) represent the devices sensitivity to the three primary color bands. The output RGB value encodes unique information on the biological properties of the analyte, such as its thickness and refractive index. After imaging, machine learning software performs a pixel-by-pixel segmentation by recovering the thickness and refractive indices from the RGB features encoded by the camera. Figure1d shows an example three-dimensional reconstruction of the thickness map of an HCT-116 colorectal cancer cell. The layered colors on the panels of Fig.1e highlight distinct sub-cellular clustered structures with similar refractive indexes. This approach does not rely on cell preparation and is free from chemical alterations. At the same time, it enables automated measurement of the thickness and refractive index information in a single parallel acquisition with diffraction-limited spatial resolution. This technique requires only a conventional camera and a reflection microscope, opening up the possibility of in-situ integrated setups compatible with equipment for cell culture growth and development studies.

Figure2a shows an example of the experimentally fabricated Pd surface used for the analysis. Surface manufacturing uses electrodeposition of Pd on a gold-coated glass piece (more details in Methods). We optimize the deposition potential and time to create large and prominent tree-like features (Fig.2a, black area) and achieve broadband light absorption. The combination of the Pd surface texture and its low reflectivity produce the cell stretching to thin film effect while simultaneously allowing the thin film interference colors to be detectable. Figure2b, d shows scanning electron microscope (SEM) images obtained from the top and cross sections of the sample. The deposited Pd grows on a layer approximately 30 m in height and comprises irregularly shaped pillars, producing a pattern reminiscent of a rainforest canopy. The insets in panels b and d show that each pillar is further textured at the nanometer scale, contributing to their hydrophilic nature. Figure2c shows a photograph of the Pd surface at 100 magnification under a brightfield reflection microscope. The image highlights the highly absorbing nature of the sample, with only minor light reflection at the tips of the Pd pillars under direct Khler illumination. The inset in Fig.2c reports the regions reflectivity across visible wavelengths measured with an integrating sphere, showing that the nanostructured Pd reflects less than 2% of visible light relative to a silver mirror. Most of the light scattering from the pillars occurs at high angles, enabling the detection of thin film interference components that scatter within the numerical aperture of the microscope objective.

a Photograph of nanostructured Pd sample. The color squares correspond to the regions imaged in (bd). b Overhead SEM micrograph of nanostructured Pd. c Optical micrograph of nanostructured Pd. The inset shows the reflection average reflection spectra of the area. d Cross-sectional SEM micrograph of nanostructured Pd.

The RGB color features of a stretched biological specimen depend on its local thickness and refractive index uniquely. Figure3a illustrates this point quantitatively. The figure presents examples of standard RGB (sRGB) colors generated via thin-film interference at four representative film thicknesses (see Methods for more detail). For each thickness, the refractive index varies in the biological range from 1.33 to 1.55. Figure3a shows that sRGB features encode unique combinations of thickness and refractive index that do not intersect, thus permitting the retrieval of these quantities with no ambiguity. This feature allows for overcoming the limitation of QPM methods, which require pre-existing knowledge of the sample thickness.

a Biological thin film colors in the sRGB colorspace for four different thicknesses as the refractive index varies from 1.33 to 1.55. b, c Sensitivity limits for refractive index and thickness values recovery as a function of the channel bit depth of the camera used and the stability of the image values. The plot is composed of discrete points with the dashed lines intended to help visualizing the trends.

Figure3b, c present a theoretical analysis of the resolution limits of this method. The y-axis of the plots represents the level of variation, in units of bits, that the image file may suffer from due to thermal, electrical, or illumination fluctuations in the experimental setup. This value can be estimated by examining the variation in pixel values between images of the same object taken at different times. For a given bit variation, each circle marks the thickness or refractive index resolution below which two distinct biological structures yield the same RGB triplet. The dotted lines of the image help visualizing the resolution dependence on bit depth, but the plots are not continuous as a discrete variation of camera bit depth yields a discrete variation in the sensitivity of the technique. Figure3b, c shows that this technique achieves state-of-the-art refractive index resolutions (104) for a 16 bit per color channel camera. Likewise, this method reaches nanometer thickness resolution when employing cameras of 14 bits per channel or higher.

While the mapping between a spectrum and an RGB triplet is unique within the expected biological thickness and RI ranges, in a limited number of cases, the conversion of an SPD to the bit-limited RGB space of the camera yields very close RGB values, a phenomenon known as metamerism. Figure4a shows an example of this by plotting the theoretical reflection spectra of two metameric films, S1 and S2. The two spectral curves represent the response of thin films deposited over a silicon substrate with RI values of 1.41, 1.49 and thickness values of 588 nm and 356 nm, respectively. These thicknesses and RI values lie within the expected range of biological specimens27. While the two films have different properties, when integrated through an 8-bit cameras CMFs they map to RGB colors that are almost indistinguishable to the human eye: RGB = [149,251,122] (S1) and RGB = [141,251,134] (S2). We designed and implemented a machine learning recovery procedure that retrieves thickness and RI without human bias or intervention for these challenging metameric scenarios.

a Reflection spectra and RGB color of metameric thin films S1 and S2. b Clustering of thin film sample into two pixel groups. c Cost maps for four pixels of cluster 1. d Expanded view of the cost map of pixel ii, the pink and blue areas indicate the probability of the thickness and RI values respectively. e Pooled cost function for the pixels of cluster 1.

The process starts by accurately characterizing the cameras CMFs through supervised learning. In this step, we used a training and validation experimental dataset of 65 thin films of known thickness and RI. We manufactured these thin films via the spin coating of PMMA photoresist on silicon wafer pieces at different speeds and measured their thickness and RI through spectroscopic ellipsometry (see Supplementary Figs.1 and 2ad). We then acquired reflection spectra and photograph pairs for each film sample. Using these samples, we trained a regression model using non-linear basis functions (see Supplementary Note1 for implementation details). This approach yields the CMFs up to the desired resolution in frequency, controlled by the size of the regression model. This training process allows the measurement of any biological thin film imaged by the camera, as the ML algorithm is agnostic to the type of cell or imaged material, learning only the relation between the spectral power distribution of the specimen and the color outputted by the camera.

After estimating the CMFs, the ML recovery algorithm can extract the thickness and RI values for each pixel of a samples image. However, due to metamerism, working with each pixel as an isolated element can result in incorrect recoveries. The ML algorithm addresses this by pooling information from pixels with close RGB values, generating groups of adjacent pixels possessing similar RGB colors in the image. This process uses an unsupervised k-means clustering algorithm that labels pixels of similar RGB colors as belonging to the same cluster. The ML recovery procedure automatically sets the number of clusters to yield an average variation of less than 2% between the RGB values of the pixels in each cluster and the cluster centroid RGB value. We set this value as a threshold found through successive iterations of the algorithm, with the condition that a lower value would result in the differences in recovered RI and thickness values for the pixels in a cluster being below the sensitivity of our setup. Slight RGB differences between adjacent pixels correspond to nanometer scale fluctuations in the materials thickness, which the camera perceives even at the single nanometer. (see Fig.3c).

Figure4b illustrates clustering for an experimental thin film sample manufactured with the parameters of S2. Running the clustering process results in two clusters for the image, one corresponding to the green area of the thin film and another for the black edge of the field stop of the microscope used to take the image. The average difference between the RGB triplets in the green cluster and the centroid RGB value is 0.86%.

In each cluster, ML recovery employs a pooling strategy similar to using pooling layers in convolutional neural networks28. For a subset of 1000 randomly sampled pixels within the cluster, we compute a mean square error (MSE) cost map:

$${{{{{{{rm{MSE}}}}}}}}=frac{1}{3}{leftvert {{{{{{{bf{X}}}}}}}}-hat{{{{{{{{bf{X}}}}}}}}}rightvert }^{2}=frac{1}{3}mathop{sum}limits_{i}{left({X}_{i}-{hat{X}}_{i}right)}^{2},$$

(1)

where X=[X1,X2,X3]=[R,G,B] is the measured RGB triplet of the pixel, and (hat{{{{{{{{bf{X}}}}}}}}}=[{hat{X}}_{1},{hat{X}}_{2},{hat{X}}_{3}]=[hat{R},hat{G},hat{B}]) a numerically computed thin film RGB value from a table of RGB values corresponding to thin films of known RI and thickness values (see Supplementary Fig.2e, f). We calculate the RGB table only once, and the cost map executes in parallel for each cluster. Figure4c illustrates the cost maps associated with four random pixels in the cluster, and Fig.4d presents an expanded view of the map of pixel ii. Because of metamerism, the MSE cost map shows two local minima (yellow areas), one corresponding to the thickness and RI values of S1 and the other to the values of S2. The ML recovery procedure computes the probability of each of these RI and the correct thickness values by slicing the MSE map along each axis and comparing the minimum values (Fig.4d pink and light blue probability areas). This step results in a 0.62 probability that the acquired RGB value belongs to the RI and thickness of S1 for pixel ii.

The algorithm then pools together the cost maps of each pixel within the same cluster to improve the low-confidence probabilities and correctly identify the thickness and RI values of the film. This procedure averages out outliers and yields the MSE map depicted in Fig.4e. This map presents a single minimum, which correctly corresponds to the samples thickness and RI values with unitary confidence and no ambiguity.

Figure5 summarizes validation results for the ML RI and thickness recovery on synthetic cell-like objects with engineered thickness and refractive index. These synthetic cells are 30 m wide squares of cured SU-8 photoresist (see Methods for fabrication details). We measured the cells thickness t using optical and contact profilometry (see Supplementary Fig.1), obtaining t=(5676)nm, and obtained the ground truth RI from the resist manufacturer datasheet. Figure5a shows a photograph of a synthetic cell through a reflection microscope at 100 magnification. The blurring on the right side of Fig.5a does not originate from a thickness variation but is the result of a slight tilt of the cell, which places this area outside the depth of field of the 100, 0.9 NA, objective we use to acquire the image. The cell is of a near uniform green color except for two dark spots within its area, which correspond to supporting Pd pillars seen through the cell. Figure5b presents a three-dimensional image of the cell positioned on the Pd substrate, illustrating how the cell is supported at a slight angle by these two pillars. Figure5c, d shows the ML calculated thickness and RI maps of the artificial cell structure. As the cell is uniform in both thickness and refractive index, the plots present constant values for both quantities over the cells surface, save for the areas where the Pd pillars are detected. Our algorithm treats the Pd pillars background as a black thin film during the calculations, and will not further processes these areas for RI and thickness recovery. Figure5e, f presents the absolute uncertainty against the ground truth values. We calculated the uncertainty as the difference between the values recovered by our algorithm and ground truth measurements of the refractive index and thickness. The procedure yields results with an average discrepancy of 0.6 nm in the thickness recovery compared to the average cell thickness obtained with the profilometer measurements and of 3103RIU compared to the datasheet RI over the synthetic cell area.

a Photograph of a synthetic cell as seen under 100 magnification on top of the Pd substrate. The two dark spots correspond to Pd pillars visible through the cell. b 3D model showing the relative positioning of the synthetic cell on the Pd pillars. c, d 3D reconstruction of the thickness and refractive index maps obtained for the synthetic cell. e, f Uncertainty maps for the thickness and RI of the synthetic cell.

Figure6 presents the results of the recovery process applied to a natural cell. Figure6a shows a photograph of an HCT-116 colon cancer cell after deposition and stretching on the Pd surface. Spatially varying thin film interference colors are visible across the specimen. The dark spots in the central part of the cell correspond to debris from a Pd pillar that moved over the cell during the deposition process. The blurriness on edge results from the short depth of field of the 100, 0.9 NA objective used to capture the image. We set the microscope to focus on the largest possible cell area as the sample must be in focus to prevent overlap between neighboring pixels RGB values and allow the technique to obtain sharp RI and thickness maps. Figure6b, c shows the ML computed RI and thickness maps of the specimen using 50 color clusters. This number results in a maximum variation considering all clusters of 1.98% between the RGB values of the pixels and their cluster centroid RGB triplet. Consistently with previously reported RI maps for HCT-116 cells, no sharp nucleous-cytoplasm boundary is apparent, however, the RI values shown in Fig.6b are larger than those reported in the literature for living HCT-116 cells by approximately 0.1 RIU29,30. This RI increase is a consequence of cell dehydration, and is consistent with the previously reported RI increase of up to 0.15 RIU across the visible wavelength range for dehydrated tissues and isolated cells undergoing dehydration31,32. The ML algorithm correctly isolates the Pd background in both results, grouping all pixels with low RGB values into the background cluster. This clustering step produces a sharp boundary separating the cell from the Pd according to whether the RGB values of the pixels are above the threshold the algorithm defines as the background. The algorithm likewise identifies and groups the Pd debris on the cell with the background pixels. Figure6d illustrates the ten most significant clusters, excluding the background, that the algorithm finds for the photographed cells. The cells dark gray interior represents the remaining smaller clusters. Each cluster corresponds to groups of pixels the algorithm identifies as having equal RI and thickness values. Figure6e is an SEM close-up of the specimen. The panel shows the thin film nature of the cell and the raised height of the specimen edges relative to the rest of the body that cause the edge blurriness of Fig.6a. We ensured the SEM imaged cell was the same as the cell shown in Fig.6a by scratching markings in the Pd surrounding the cell. We estimated the cell thickness from the SEM image by measuring the number of pixels in the image corresponding to the raised border of the cell, and then multiplying this value by the size in nanometers of one pixel. The estimated cells thickness from the SEM image lies between 250 nm and 800 nm, in good agreement with reconstructed values in Fig.6c. Figure6f presents a complete 3D reconstruction of the cell thickness profile with a color overlay that varies according to the point-to-point RI value.

a Photograph of an HCT-116 cell stretched on the Pd substrate showing thin film interference based spatially dependent colors. b, c. ML recovery results for the thickness and RI of the specimen in (a). d Ten largest clusters found for the cell depicted in (a), the remaining clusters are grouped as the dark gray interior of the cell. e SEM micrograph of the cell on the Pd substrate. f 3D reconstruction of the thickness map of the cell with overlayed RI information.

See the original post:

Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit ... - Nature.com

Biology

Clownfish: Studying their Complex Lives and Anemone Homes | The Brink – Boston University

The social dynamics of clownfish are not as simple as the adoring father-son relationship of Marlin and Nemo in Disneys iconic films Finding Nemo and Finding Dory. The reality for these brightly hued orange-and-white fish is far more complexand one that has long stumped evolutionary biologists. Peter Buston, a Boston University College of Arts & Sciences associate professor of biology, has been studying clownfish for over two decades, and has housed hundreds of these fish in his Marine Evolutionary Ecology lab.

One major difference between Nemo and real-life clownfish is that they dont always live with their biological relatives. Instead, groups of up to six cohabiting fish are led by a femalethe queen bee of the clownfishwhile living in friendly competition with one another based on their size and color. Only the largest of the group mates with the reigning queen.

Fascinatingly, all clownfish are born male, with the capacity to change gender later in life. Once the female of a group dies, the next largest in the group changes gender from male to female, and becomes the new leader. The smaller fish all move up one spot in the social ladder, waiting their turn until theyre next in line to mate.

The idea that the smaller, duller-colored clownfish put up with this arrangement fascinates Buston. Through his research, he has tried to figure out why this social hierarchy doesnt lead to the smaller fish leaving their home anemonewhich live attached to the seafloor or coral reefs and have long tentaclesto breed elsewhere. In a 2020 paper, Buston found that a combination of ecological and social constraints seem to be the reason for them staying. Clownfish didnt even leave when presented with a nearby alternative, because of the risks of entering a new home, and most of them returned to their original anemone after being moved to a different one.

Their behaviors can be quite complex, says Buston, who has studied clownfish behavior both in the lab and in the wild. And clownfish and anemones have a quintessential symbiotic relationship. In the ocean, sea anemones trap food with stinging cells on their tentacles that paralyze their prey. Clownfish, though, secrete a mucus that shields them from the stings. The bright-colored clownfish attract predator fish to the anemone, which then stings and eats the fish. And in return, the anemone provides a safe, protected environment for the clownfish.

To make matters more complicated, Buston and his team have found that clownfish can control their growth depending on the specific social contextso two rival males put together will race to get bigger and become dominant. The team is currently investigating the genetic mechanisms that allow the fish to do this. Theyve also learned how to introduce baby clownfish to new social groups in different-size anemones and created more than 10 social groups in the labwith aims to create more soon.

Watch the video above to see the clownfish in action.

See the original post here:

Clownfish: Studying their Complex Lives and Anemone Homes | The Brink - Boston University

Biology

Rucaparib and its major metabolite exhibit differential biological activity and synergy – News-Medical.Net

Once they enter the body, drugs, apart from carrying out their therapeutic function, are biochemically transformed by the action of the metabolic machinery, a process that facilitates their expulsion. This biotransformation results in a gradual disappearance of the drug, which is converted into its metabolites. These, in turn, can reach high concentrations in the body and also show a biological activity that may be different from that of the original drug. That is, the metabolites and the drug coexist in the body, and can cause effects different from those obtained with the individual molecules. This is the case of Rucaparib, a drug used in chemotherapy for ovarian cancer, breast cancer and, more recently, prostate cancer, and its metabolite, the M324 molecule. Rucaparib is part of a group of drugs designed to treat several types of cancers that show alterations in DNA repair. Specifically, they are inhibitors of the PARP1 enzyme, involved precisely in the process of repairing mutations in the genetic material.

A study led by researchers Albert A. Antolin, from the Oncobell program of the Bellvitge Biomedical Research Institute (IDIBELL) and ProCure of the Catalan Institute of Oncology (ICO), and Amadeu Llebaria, from the Institute of Advanced Chemistry of Catalonia (IQAC-CSIC ), has shown that Rucaparib and its main metabolite M324 exhibit differential activities. Published in the journal Cell Chemical Biology, the paper has analyzed Rucaparib and M324, making a computational prediction of the metabolite's activity. The article describes the synthesis of M324 and its biological assay, demonstrating that the drug and its metabolite have differentiated activities and act synergistically in some prostate cancer cell lines. And that, surprisingly, M324 reduces the accumulation of the protein -synuclein (an important component of Lewy bodies) in neurons derived from patients with Parkinson's, a neurodegenerative disease characterized by a movement disorder, and in which neurons do not produce sufficient amounts of the neurotransmitter dopamine.

Specifically, the synergy demonstrated between Rucaparib and M324 in prostate cancer cell lines could have an impact on clinical trials for advanced stages of this type of cancer. On the other hand, the fact that M324 is capable of reducing the abnormal accumulation of -synuclein in neurons derived from stem cells of a Parkinson's patient, highlights the therapeutic potential of this metabolite and its possible pharmacological application for the treatment of this neurodegenerative disease. These results have been obtained thanks to the collaboration of the IDIBELL and ICO groups led by Miquel ngel Pujana and lvaro Ayts, and the group of Antonella Consiglio, from IDIBELL and the UB.

Researchers have used computational and experimental methods to comprehensively characterize, and for the first time, the pharmacology of the M324 molecule. The first author of the work, Huabin Hu, has made an exhaustive prediction of the differential activity of the original drug and its product, which translates into different spectra of the phosphorylation pattern of cellular proteins. Carme Serra, from the MCS group at IQAC-CSIC, has synthesized the metabolite M324, which has allowed experimental verification of the computational prediction in biological and cellular assays. The results obtained could have implications for clinical treatment with Rucaparib and, in turn, open new opportunities for drug discovery.

In summary, the study points towards a new conceptual perspective in pharmacology: one that considers drug metabolism not as an undesirable process that degrades and eliminates the therapeutic molecule from the body, but rather as one that can have potential advantages from a therapeutic point of view. Indeed, the work highlights the importance of characterizing the activity of drug metabolites to comprehensively understand their clinical response and apply it in precision medicine.

Source:

Journal reference:

Hu, H., et al. (2024). Identification of differential biological activity and synergy between the PARP inhibitor rucaparib and its major metabolite.Cell Chemical Biology. doi.org/10.1016/j.chembiol.2024.01.007.

Here is the original post:

Rucaparib and its major metabolite exhibit differential biological activity and synergy - News-Medical.Net