Results for “shotgun sequencing”

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗

NASA NTRSDate not supplied

Translational Line of Sight: a multi-omics longitudinal study of the murine retinal response to spaceflight hazard analogs

The space environment includes unique hazards like radiation and microgravity which adversely affect physiology and behavior of humans and rodent models. To better characterize the retinal response to spaceflight, we assessed a multi-omics NASA GeneLab dataset where 6-month-old female mice were gamma irradiated and/or hindlimb unloaded for 21 days followed by whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of retina samples collected at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles revealing a total of 4,178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation differences was seen across different conditions at the same time point (e.g., between radiation exposure and hindlimb unloaded at 7 days). Biological processes related to nucleotide metabolism were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. Interestingly, Notch3 and Lrg1 showed differential patterns in the NASA Twins Study in-flight samples and in response to stressors in the murine retina in the current study. A total of 23 genes were both differentially methylated and expressed, including genes involved in retinal disease or cataract development (Crybb3, Fgfr1, Pitpnm3, Sipa1l3, Sox9) and inflammatory response (B4galt6, Ppm1a, Sphk1). To our knowledge, the current multi-omics analysis is the first multi-omics study to interrogate the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination. The results provide an insight into the retinal response to individual spaceflight hazard analogs and their interplay at different post-exposure stages and contributes towards a mechanistic understanding of spaceflight-induced vision impairment using ground-based models.

Prachi Kothiyal↗

NASA NTRSDate not supplied

MULTI-OMICS ANALYSIS OF THE IMPACT OF CHRONIC LOW-DOSE RADIATION AND HINDLIMB SUSPENSION ON MURINE BRAIN AND RETINA

The space environment includes hazards like radiation and microgravity which can adversely affect biological systems. We assessed multi-omics multi-tissue NASA GeneLab datasets where 6-month-old female mice were gamma irradiated (IR) and/or hindlimb unloaded (HLU) for 21 days. Whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of brain and retina samples collected at 4 months post-exposure was performed to better characterize the retinal and neurological responses to spaceflight. We compared epigenomic and transcriptomic profiles within each exposure group for both tissue types to identify correlation (Pearson’s correlation test; p-value < 0.05) between gene expression and DNA methylation levels that may be related to transcriptional regulation. We then obtained genes with methylation-expression correlation that also showed differences in mean expression or dispersion between exposed and control groups (adjusted p-value < 0.25; relaxed to denote ‘hypothesis’) in the brain (37 genes in HLU, 4 in IR, and 156 in HLU+IR) or retina (92 genes in HLU, 1 in IR, and 55 in HLU+IR). Enriched Gene Ontology (GO) terms for these genes are listed in Table 1 for HLU and HLU+IR for both tissue types. No enriched terms and only a few genes were detected with IR-only exposure in the brain (Chmp1a, Limd1, Rab40b, Ubc) and retina (retinoblastoma binding protein Rbbp7). Cellular components related to synapse were enriched in both tissue types. Previous analysis of differentially expressed genes in the retina after 1 month of HLU+IR showed enrichment in the somatodendritic compartment of the neuron, which was also observed in the brain 4 months post-exposure. Interestingly, genes related to ubiquitination showed correlation between methylation and expression and were differentially expressed or dispersed in different exposure groups indicating that this pathway may play an important role in multi-stressor response (Figure 1). The current multi-omics and multi-tissue analysis interrogates the epigenomic and transcriptomic impacts of radiation and hindlimb unloading, in isolation and in combination, on the retina and the brain. The results provide insight into the adaptive response to individual spaceflight hazard analogs and their interplay, as well as hypotheses to be further tested for understanding spaceflight-induced neurological and vision effects.

Prachi Kothiyal↗

NASA NTRSDate not supplied

A Multi-omics Longitudinal Study of the Murine Retinal Response to Chronic Low-dose Irradiation and/or Simulated Microgravity

The space environment includes unique hazards like radiation and microgravity which adversely affect physiology and behavior of humans and rodent models. To better characterize the retinal response to spaceflight, we assessed a multi-omics NASA GeneLab dataset where 6-month-old female mice were gamma irradiated and/or hindlimb unloaded for 21 days followed by whole transcriptome shotgun sequencing (RNA-Seq) and reduced representation bisulfite sequencing (RRBS) of retina samples collected at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles revealing a total of 4,178 differentially methylated loci or regions, and 457 differentially expressed genes. Highest correlation in methylation differences was seen across different conditions at the same time point (e.g., between radiation exposure and hindlimb unloaded at 7 days). Biological processes related to nucleotide metabolism were enriched in all groups with activation at 1 month and suppression at 7 days and 4 months. Genes and processes related to Notch and Wnt signaling showed alterations 4 months post-exposure. Interestingly, Notch3 and Lrg1 showed differential patterns in the NASA Twins Study in-flight samples and in response to stressors in the murine retina in the current study. A total of 23 genes were both differentially methylated and expressed, including genes involved in retinal disease or cataract development (Crybb3, Fgfr1, Pitpnm3, Sipa1l3, Sox9) and inflammatory response (B4galt6, Ppm1a, Sphk1). To our knowledge, the current multi-omics analysis is the first multi-omics study to interrogate the epigenomic and transcriptomic impacts of radiation and hindlimb unloading on the retina in isolation and in combination. The results provide an insight into the retinal response to individual spaceflight hazard analogs and their interplay at different post-exposure stages and contributes towards a mechanistic understanding of spaceflight-induced vision impairment using ground-based models.

Prachi Kothiyal↗

NASA NTRSDate not supplied

ISS External Microorganisms: Collecting Planetary Protection Samples During Extravehicular Activity

Introduction: We have designed1, built, and tested a sampling kit (Fig. 1) to aseptically collect microbiological samples from exterior surfaces on the ISS (International Space Station). The kit was flown to ISS as part of the NG-19 commercial cargo mission in August of 2023. Astronauts will use the kit to collect samples from six exterior surfaces on ISS. These samples will be frozen at -80°C after collection and returned to the ground for next generation DNA sequencing. The results of this experiment will help inform planetary protection requirements for crewed missions. We hypothesize that there are detectable microbial communities outside ISS and that these communities originate from inside ISS. Current life support systems do not include any components for reducing microbial leakage. Gases are vented from inside ISS without filtration and there are currently no protocols in place to minimize bioburden on space suit exteriors prior to use. It is important to quantify the bioburden on current vehicles so that achievable limits can be set for crewed missions to astrobiologically relevant locations like Mars. Figure 1: The sampling kit contains eight swab canisters in total (six on the top and two on the bottom). A resuable end effector (1) is used to remove and reinstall the swabs. The kit also contains a handrail (2), tether loop (3), and, bayonet probes(4) to allow easy manipulation during EVA. Methods: We have designed a kit to carry 8 commercially available foam swabs into and out of vacuum without compromising the swabs’ sterility. The specially designed swab canisters contain a 0.2 µm Teflon filter that allows the canister to accommodate pressure changes without introducing unwanted contaminants. The kit meets existing EVA (Extravehicular Activity) safety requirements and has been used by human test subjects in the neutral buoyance lab and at vacuum in test chambers at the Johnson Space Center. In the early part of 2024, astronauts will use this sampling kit to swab six surfaces on the exterior of ISS. We will collect samples from: the airlock vestibule, the interior surface of the airlock thermal cover, an exterior handrail, a vent connected to the CO2 removal system, and a vent connected to the payload vacuum system inside ISS. Approximately 300 cm2 will be swabbed at each location. A seventh swab will be exposed to the vacuum of space without touching any surfaces as a blank. The eighth swab will remain sealed as a process control. After the EVA the swab kit will be frozen at -80°C and returned to earth at the earliest possible opportunity. The samples will remain frozen until they are thawed for next generation DNA sequencing on earth. We will use amplicon sequencing to identify any bacteria, archaea or fungi present in the samples. If there is enough DNA present, we will use shotgun metagenomic sequencing to further characterize the microbial ecology outside ISS. Preliminary Results: Results from ground-based testing demonstrate that the swab kit is capable of cycling in and out of vacuum without contaminating the swabs. Test subjects, wearing flight-like EVA gloves could remove swabs from the canister and sample discrete locations without inadvertently touching any other surfaces. Several types of bacteria and fungi were collected during these ground tests and survived up to 6 hours at vacuum. This includes non-spore forming bacterial like Staphyloccus capitis that are not traditionally considered extremophiles. Shotgun metagenomic sequencing of the ground-test samples revealed bacteria associated with human skin and airways on the exterior of space suits used during these tests 2 In addition to these results we will present preliminary results from our space-flight samples. We will also present lessons learned from attempting to collect microbiological samples during an EVA and describe how our results will affect planetary protection requirements for future crewed missions.

Aaron B. Regberg↗

NASA NTRSDate not supplied

ISS External Microorganisms: Collecting Planetary Protection Samples During Extravehicular Activity

Introduction: We have designed1, built, and tested a sampling kit (Fig. 1) to aseptically collect microbiological samples from exterior surfaces on the ISS (International Space Station). The kit was flown to ISS as part of the NG-19 commercial cargo mission in August of 2023. Astronauts will use the kit to collect samples from six exterior surfaces on ISS. These samples will be frozen at -80°C after collection and returned to the ground for next generation DNA sequencing. The results of this experiment will help inform planetary protection requirements for crewed missions. We hypothesize that there are detectable microbial communities outside ISS and that these communities originate from inside ISS. Current life support systems do not include any components for reducing microbial leakage. Gases are vented from inside ISS without filtration and there are currently no protocols in place to minimize bioburden on space suit exteriors prior to use. It is important to quantify the bioburden on current vehicles so that achievable limits can be set for crewed missions to astrobiologically relevant locations like Mars. Figure 1: The sampling kit contains eight swab canisters in total (six on the top and two on the bottom). A resuable end effector (1) is used to remove and reinstall the swabs. The kit also contains a handrail (2), tether loop (3), and, bayonet probes(4) to allow easy manipulation during EVA. Methods: We have designed a kit to carry 8 commercially available foam swabs into and out of vacuum without compromising the swabs’ sterility. The specially designed swab canisters contain a 0.2 µm Teflon filter that allows the canister to accommodate pressure changes without introducing unwanted contaminants. The kit meets existing EVA (Extravehicular Activity) safety requirements and has been used by human test subjects in the neutral buoyance lab and at vacuum in test chambers at the Johnson Space Center. In the early part of 2024, astronauts will use this sampling kit to swab six surfaces on the exterior of ISS. We will collect samples from: the airlock vestibule, the interior surface of the airlock thermal cover, an exterior handrail, a vent connected to the CO2 removal system, and a vent connected to the payload vacuum system inside ISS. Approximately 300 cm2 will be swabbed at each location. A seventh swab will be exposed to the vacuum of space without touching any surfaces as a blank. The eighth swab will remain sealed as a process control. After the EVA the swab kit will be frozen at -80°C and returned to earth at the earliest possible opportunity. The samples will remain frozen until they are thawed for next generation DNA sequencing on earth. We will use amplicon sequencing to identify any bacteria, archaea or fungi present in the samples. If there is enough DNA present, we will use shotgun metagenomic sequencing to further characterize the microbial ecology outside ISS. Preliminary Results: Results from ground-based testing demonstrate that the swab kit is capable of cycling in and out of vacuum without contaminating the swabs. Test subjects, wearing flight-like EVA gloves could remove swabs from the canister and sample discrete locations without inadvertently touching any other surfaces. Several types of bacteria and fungi were collected during these ground tests and survived up to 6 hours at vacuum. This includes non-spore forming bacterial like Staphyloccus capitis that are not traditionally considered extremophiles. Shotgun metagenomic sequencing of the ground-test samples revealed bacteria associated with human skin and airways on the exterior of space suits used during these tests 2 In addition to these results we will present preliminary results from our space-flight samples. We will also present lessons learned from attempting to collect microbiological samples during an EVA and describe how our results will affect planetary protection requirements for future crewed missions.

Aaron B. Regberg↗

NASA NTRS2021

Characterization of Spacesuit Associated Microbial Communities and their Implications for NASA Missions

Crewed National Aeronautics and Space Administration (NASA) missions to other solar system bodies are currently being planned. One high-profile scientific focus during such expeditions would be life detection, specifically the discovery of past or present microbial life, if they exist. However, both humans and associated objects typically carry a high microbial burden. Thus, it is essential to distinguish between microbes brought with the expedition and those present on the exploring planets. Modern spacesuits are unique, customized spacecraft which provide protection, mobility and life support to crew during spacewalks, yet they vent continuously, and the mobility of microbes through spacesuits has not been studied. To evaluate the microbial colonization of spacesuits, NASA used an Extravehicular Activity swab kit to examine viable microbial populations of 48 samples from spacesuits using both traditional microbiological methods and molecular sequencing methods. The cultivable microbial population ranged from below the detection limit to 9 x 102 colony forming units per 25 cm2 of sample and also significantly varied by the location. The cultivable microbial diversity was dominated by members of Bacillus, Arthrobacter, and Ascomycota. However, 16S rRNA-based viable bacterial burden ranged from 105 to 106 copies per 25 cm2 of sample. Shotgun metagenome sequencing revealed the presence of a diverse microbial population on the spacesuit surfaces, including Curtobacterium and Methylobacterium from across all sets of spacesuits in high abundance. Among bacterial species identified, higher abundance of Cutibacterium acnes, Methylobacterium oryzae, and M. phyllosphaerae reads were documented. The results of this study provide evidence that identical microbial strains may live on the wrist joint, inner gauntlet, and outer gauntlet of spacesuits. This raises the possibility, but does not confirm, that viable microbes are able to migrate between the interior and exterior of spacesuits. Overall, these data provide the first estimate of microbial dispersion from internal to external spacesuit surfaces, which will help future mission planners develop effective planetary protection strategies.

spacesuit↗

NASA NTRS1989

Phylogenetic perspective and the search for life on earth and elsewhere

Any search for microbial life on Mars cannot rely upon cultivation of indigenous organisms. Only a minority of even terrestrial organisms that are observed in mixed, naturally-occurring microbial populations can be cultivated in the laboratory. Consequently, methods are being developed for analyzing the phylogenetic affiliations of the constituents of natural microbial populations without the need for their cultivation. This is more than an exercise in taxonomy, for the extent of phylogenetic relatedness between unknown and known organisms is some measure of the extent of their biochemical commonalities. In one approach, total DNA is isolated from natural microbial populations and 16S rRNA genes are shotgun cloned for rapid sequence determinations and phylogenetic analyses. A second approach employs oligodeoxynucleotide hybridization probes that bind to phylogenetic group-specific sequences in 16S rRNA. Since each actively growing cell contains about 104 ribosomes, the binding of the diagnostic probes to single cells can be visualized by radioactivity or fluorescence. The application of these methods and the use of in situ cultivation techniques is illustrated using submarine hydrothermal vent communities. Recommendations are made regarding planning toward future Mars missions.

Pace, Norman R.↗