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15.4: Materials and Procedures - Biology

15.4: Materials and Procedures - Biology


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The lab exercise on antimicrobial sensitivity is one of my favorites because it highlights antibiotics, one of the most important medical discoveries of the 20 th Century. Now that antibiotic resistant “superbugs” are becoming more prevalent, I feel it is valuable for you to observe first-hand, the proper use and testing protocols for antibiotics.
In this lab, students visualize the effectiveness of 5 different antibiotics against both Gram-positive and Gram-negative bacteria, and draw conclusions about how antibiotics are matched to certain bacterial infections. By replicating a protocol used in clinical labs, I hope you will also gain an appreciation for the strict quality control measures used in the assessment of antibiotics.

~Professor Kelly Cude

Materials

  • Per group-
    • Staphylococcus saprophyticus
    • Staphylococcus epidermidis
    • Escherichia coli (all from the Student Stocks)
  • 3-Mueller Hinton Agar plates
  • 3-TSB tubes
  • McFarland Standards and lined cards
  • Sterile swabs
  • Antibiotic disk dispenser with Chloramphenicol 30μg (C30), Penicillin 10μg (P10), Trimethoprim 5μg (TMP5), Ciprofloxacin 5μg (CIP5), Novobiocin 30μg (NB30)
  • Rulers (mm)

Procedures

  1. Make a suspension of each organism using a sterile swab and compare with the McFarland 0.5 Turbidity standard (use the striped card to help see the turbidity of the standard and the inoculated broth).
  2. Use another sterile swab for each inoculated TSB to streak a Mueller Hinton plate to create a “confluent lawn” of growth.
  3. Dispense antibiotic disks on top of the inoculated plates using the disk dispenser. Antibiotics to be tested- Chloramphenicol 30μg (C30), Penicillin 10μg (P10), Trimethoprim 5μg (TMP5), Ciprofloxacin 5μg (CIP5), Novobiocin 30μg (NB30).
  4. Let disks sit and adsorb onto the agar for a few minutes, then invert and incubate at 35C for 24h-48h (when comparing the Novobiocin results to the unknowns later in the course, make sure that you note the incubation time so that you have an accurate reference point).
  1. After incubation, measure the zones and compare to the reference chart.

Results

  1. Enter the size of the zones of inhibition in the chart below:

Bacterium/Gram reaction

C30 (mm)

P10 (mm)

TMP5 (mm)

CIP5 (mm)

NB30 (mm)

Staphylococcus saprophyticus

Staphylococcus epidermidis

Escherichia coli

  1. Based on the Zone of Inhibition Chart interpret your results and record below:

Bacterium/Gram reaction

C30 R, I, S

P10 R, I, S

TMP5 R, I, S

CIP5 R, I, S

NB30 R, I, S

Staphylococcus saprophyticus

Staphylococcus epidermidis

Escherichia coli


Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection

Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions.

Results

The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current in silico predictions and hitherto unavailable experimental data.

Conclusions

These data address a fundamental deficiency in our understanding of the model eukaryote Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.


Introduction

Sanger sequencing has dominated the genomic research for the past two decades and achieved a number of significant accomplishments including the completion of human genome sequence, which made the identification of single gene disorders and the detection of targeted somatic mutation for clinical molecular diagnostics possible [1, 2]. Despite Sanger sequencing's accomplishments, researchers are demanding for faster and more economical sequencing, which has led to the emergence of “next-generation” sequencing technologies (NGS). NGS’s ability to produce an enormous volume of data at a low price [3, 4] has allowed researchers to characterize the molecular landscape of diverse cancer types and has led to dramatic advances in cancer genomic studies.

The application of NGS, mainly through whole-genome (WGS) and whole-exome technologies (WES), has produced an explosion in the context and complexity of cancer genomic alterations, including point mutations, small insertions or deletions, copy number alternations and structural variations. By comparing these alterations to matched normal samples, researchers have been able to distinguish two categories of variants: somatic and germ line. The Whole transcriptome approach (RNA-Seq) can not only quantify gene expression profiles, but also detect alternative splicing, RNA editing and fusion transcripts. In addition, epigenetic alterations, DNA methylation change and histone modifications can be studied using other sequencing approaches including Bisulfite-Seq and ChIP-seq. The combination of these NGS technologies provides a high-resolution and global view of the cancer genome. Using powerful bioinformatics tools, researchers aim to decipher the huge amount of data to improve our understanding of cancer biology and to develop personalized treatment strategy. Figure 1 shows the workflow of integrating omics data in cancer research and clinical application.

The workflow of integrating omics data in cancer research and clinical application. NGS technologies detect the genomic, transcriptomic and epigenomic alternations including mutations, copy number variations, structural variants, differentially expressed genes, fusion transcripts, DNA methylation change, etc. Various kinds of bioinformatics tools are used to analyze, integrate, and interpret the data to improve our understanding of cancer biology and develop personalized treatment strategy.


STUDENT ENGAGEMENT

Another lens through which to consider educational video is student engagement. The idea is simple: if students do not watch videos, they cannot learn from them. Lessons on promoting student engagement derive from earlier research on multimedia instruction and more recent work on videos used within MOOCs (massive open online courses see Table 1 ).

The first and most important guideline for maximizing student attention to educational video is to keep it short. Guo and colleagues examined the length of time students watched streaming videos within four edX MOOCs, analyzing results from 6.9 million video-watching sessions ( Guo et al., 2014 ). They observed that the median engagement time for videos less than 6 minutes long was close to 100%–that is, students tended to watch the whole video (although there are significant outliers see the paper for more complete information). As videos lengthened, however, student engagement dropped, such that the median engagement time with 9- to 12-minute videos was �%, and the median engagement time with 12- to 40-minute videos was �%. In fact, the maximum median engagement time for a video of any length was 6 minutes. Making videos longer than 6𠄹 minutes is therefore likely to be wasted effort. In complementary work, Risko et al. (2012) showed 1-hour videos to students in a lab setting, probing student self-reports of mind wandering four times in each lecture and testing student retention of lecture material after the lecture. They found that student report of mind wandering increased and retention of material decreased across the video lecture ( Risko et al., 2012) .

Another method to keep students engaged is to use a conversational style. Called the personalization principle by Mayer, the use of conversational rather than formal language during multimedia instruction has been shown to have a large effect on students’ learning, perhaps because a conversational style encourages students to develop a sense of social partnership with the narrator that leads to greater engagement and effort ( Mayer, 2008 ). In addition, some research suggests that it can be important for video narrators to speak relatively quickly and with enthusiasm. In their study examining student engagement with MOOC videos, Guo and colleagues observed that student engagement was dependent on the narrator’s speaking rate, with student engagement increasing as speaking rate increased ( Guo et al., 2014 ). It can be tempting for video narrators to speak slowly to help ensure that students grasp important ideas, but including in-video questions, 𠇌hapters,” and speed control can give students control over this feature𠅊nd increasing narrator speed appears to promote student interest.

Instructors can also promote student engagement with educational videos by creating or packaging them in a way that conveys that the material is for these students in this class. One of the benefits for instructors in using educational videos can be the ability to reuse them for other classes and other semesters. When creating or choosing videos, however, it is important to consider whether they were created for the type of environment in which they will be used. For example, a face-to-face classroom session that is videotaped and presented within an online class may feel less engaging than a video that is created with an online environment as the initial target ( Guo et al., 2014 ). A video’s adaptability can be enhanced, however: when reusing videos, instructors can package them for a particular class using text outside the video to contextualize the relevance for that particular class and lesson.


Results

Mutations not observed in cancer patients have low mutability

We analyzed all theoretically possible codon substitutions that could have occurred by single point mutations in 520 cancer census genes and calculated their mutability values based on their genomic context. We found that only about one percent of all theoretically possible codon substitutions were observed in the surveyed 12,013 tumor samples derived from the COSMIC v85 cohort (S1 Table). Using the pan-cancer model, across all analyzed possible codon substitutions produced by single point mutation, mutability ranged from 1.61 x 10 −7 to 1.80 x 10 −5 (mean = 1.34 x 10 −6 ). Lower and upper boundaries for mutability are dependent on the cancer model selection, and cancer models with higher mutational burdens like melanomas (1.92 x 10 −7 to 1.35 x 10 −4 , mean = 7.00 x 10 −6 ) have higher mutability values compared to cancers such as prostate adenocarcinoma (5.12 x 10 −8 to 7.31 x 10 −6 , mean = 3.95 x 10 −7 ).

We found that across codon substitutions which were not observed in the COSMIC v85 cohort, the mean mutability (1.29 x 10 −6 ) was found to be three-fold lower compared to the mutability of observed codon substitutions (3.88 x 10 −6 ) using pan-cancer background model, Mann-Whitney-Wilcoxon test p < 0.01 (Fig 1A). This finding also holds true for different cancer-specific models (the list of cancer-specific mutational profiles can be found in https://www.ncbi.nlm.nih.gov/research/mutagene/signatures#mutational_profiles). The same result is confirmed for per-nucleotide mutability (1.04 x 10 −6 versus 3.36 x 10 −6 , Mann-Whitney-Wilcoxon test p < 0.01). In addition, we validated our result on a set of observed mutations from 9,228 patients who had undergone prospective sequencing of MSK-IMPACT gene panel. Looking at mutations in the genes which were sequenced in all patients in the MSK-IMPACT cohort, the same pattern remains that observed codon substitutions had a higher mutability (3.41 x 10 −6 ), compared to those which were theoretically possible, but did not occur in cancer patients (1.30 x 10 −6 ,Mann-Whitney-Wilcoxon test, p < 0.01) (Fig 1B).

Mutability of all theoretically possible codon substitutions (“not observed”) and all substitutions that were observed in: (A) COSMIC v85 pan-cancer cohort (B) MSK-IMPACT cohort. Asterisks show the differences on Mann-Whitney-Wilcoxon test significant at p < 0.01. Mutability values have been converted to negative log10 scale as pan-cancer codon mutability ranges several orders of magnitude.

S1 Fig shows cumulative and probability density distributions of nucleotide mutability values for all observed mutations in patients, for theoretically possible mutations in all cancer census genes and for two genes in particular, CASP8 and TP53. While there are many theoretically possible mutations with low mutability values, the observed cancer spectrum is dominated by mutations with high mutability. A similar pattern is seen for cancer-specific cases (Fig 2).

Counts are binned and refer to how many times a particular mutation was observed in the given cancer type. ‘0’, ‘1’, ‘2’ and ‘3+’ refer to mutations that were not observed (including all possible point mutations), observed once, twice, or in three or more cancer samples. Blue boxes show mutations with the observed frequency calculated in the COSMIC v85 cohort and green boxes refer to MSK-IMPACT cohort. (A) breast cancer (nCOSMIC = 1,667, nMSK = 783 samples), (B) Lung adenocarcinoma (nCOSMIC = 301, nMSK = 1,203), (C) Colon adenocarcinoma (nCOSMIC = 369, nMSK = 688) and (D) Skin malignant melanoma (nCOSMIC = 376, nMSK = 182).

Silent mutations have the highest mutabilities

Fig 3A and 3B show the distributions of codon mutability values for all possible missense, nonsense, and silent mutations accessible by single nucleotide base substitutions in the protein-coding sequences of 520 cancer census genes calculated with the pan-cancer background model. Codon mutability spans two orders of magnitude, and silent mutations have significantly higher average mutability values (mean = 5.68 x 10 −6 ) than nonsense (mean = 3.44 x 10 −6 ) or missense mutations (mean = 3.29 x 10 −6 ) (Kruskal-Wallis test p < 0.01 and Dunn’s post hoc test p < 0.01 for all comparisons). These differences in codon mutabilities could be a reflection of the degeneracy of genetic code, where multiple silent nucleotide substitutions in the same codon may increase its mutability. However, degeneracy of genetic code should not affect the calculation of nucleotide mutability. While the differences between types of mutations are less pronounced for nucleotide mutability (Fig 3C), silent mutations are still characterized by the highest nucleotide mutability values (mean = 3.91 x 10 −6 for silent, 3.10 x 10 −6 for nonsense and 3.17 x 10 −6 for missense mutations, Kruskal-Wallis test p < 0.01 and Dunn’s post hoc test p < 0.01 for all comparisons).

(A) Cumulative distribution of codon mutability of silent (green), nonsense (red) and missense (blue) mutations. (C) Cumulative distribution of nucleotide mutability for silent, nonsense and missense mutations. Inset shows the probability density distributions of mutability by mutation type. Significance was determined by Dunn’s test difference with p < 0.01 is marked with a double asterisk. (B) and (D) are codon and nucleotide mutability respectively binned by frequency in the COSMIC v85 pan-cancer cohort. ‘0’, ‘1’, ‘2’ and ‘3+’ refer to mutations that were not observed (including all possible point mutations), observed once, twice, or in three or more cancer samples. See S1 Table for the number of mutations in each category.

Background mutability significantly contributes to shaping the observed mutational spectrum

Under the null model of all mutations arising as a result of neutral background mutational processes, somatic mutations should accumulate with respect to their mutation rate and one would expect a positive correlation between mutability and observed mutational frequency of individual mutations. As Fig 3B and 3D show, there is a trend for silent and nonsense mutations. To further investigate this relationship, in the pan-cancer COSMIC v85 cohort we calculated both Spearman’s rank, a non-parametric test taking into account that mutability is not normally distributed, and Pearson linear correlation coefficients between codon mutability and frequencies of mutations across all 520 cancer census genes. We also explored this association for each gene with at least ten unique mutations of each type: silent, nonsense, and missense (Fig 4).

Histograms show the Spearman rank correlation coefficients between the reoccurrence frequency and mutability across cancer genes with at least 10 observed mutations of each type: (A) missense (blue), (B) nonsense (red) and (C) silent (green). Filled bars in the left column denote genes with significant correlation at p < 0.01. Bar graphs show Spearman correlation coefficient for genes with significant correlation at p < 0.01. Genes with bold font are tumor suppressors (TSG), underlined genes are oncogenes, and genes in plain font were either categorized as both TSG and oncogene or fusion genes. (D-F) Scatterplots with regression lines and confidence intervals show the linear relationship between mutability and reoccurrence frequency of each type of mutation for several representative genes. Adjusted R 2 are shown to convey goodness of fit. Mutation reoccurrence frequencies were taken from the pan-cancer COSMIC v85 cohort.

Overall, we found 84 and 137 genes with significant (p < 0.01) positive Spearman and Pearson correlations, respectively, for at least one mutation type (S2 Table). Among the genes with significant correlations, 41 belong to tumor suppressor genes, 28 are oncogenes, and 15 genes are classified as either fusion genes or both oncogene and tumor suppressor. For some genes, including TP53 (first column, Fig 4E) and tumor suppressor CASP8 (second column, Fig 4E), a rather strong linear relationship between mutability and recurrence frequency of observed mutations (R 2 > 0.5) was observed. Breaking up all codon changes into silent, nonsense and missense reveals the highest correlations for silent (ρ = 0.15, r = 0.1, p < 0.01) and nonsense (ρ = 0.20, r = 0.15, p < 0.01) mutations (S2 Fig).

Relationship between mutability and observed frequency is different for tumor suppressor and oncogenes

The effects of mutations on protein function, with respect to their cancer transforming ability, can drastically differ in tumor suppressor genes (TSG) and oncogenes, therefore we performed our analysis separately for these two categories (Fig 5). In general, mutations in TSG can cause cancer through the inactivation of their products, whereas mutations in oncogenes may result in protein activation. We used COSMIC gene classification separating genes into tumor suppressors and oncogenes. Genes which were annotated as both TSG and oncogenes were excluded from this analysis. Gene ontology (GO) analysis found that top GO annotations in TSG for cellular compartments were “nucleus”, “chromosome”, and “nuclear part” and for molecular functions were “protein”, “DNA”, and “enzyme binding”. For the oncogenes, the top GO annotations for cellular components were “nucleoplasm”, “nucleus”, and “nuclear lumen” and for molecular function “heterocyclic compound binding”, “organic cyclic compound biding” and “sequence-specific DNA binding”. A full list of genes and the associated GO terms is available in Supplemental S3 Table. In addition, we used COSMIC classification into genes with dominant or recessive mutations, but overall results were similar to the ones produced using classification into TSG and oncogenes (S3 Fig).

Genes grouped into oncogene and tumor suppressor (TSG) by their role in cancer. Mutations were binned by their reoccurrence frequency in COSMIC v85 cohort. Boxplots show codon mutability calculated with pan-cancer model. See S1 Table for counts.

We observed a weak but statistically significant correlation between codon mutability and recurrence frequency in TSG (ρ = 0.17, r = 0.13, p < 0.01) while oncogenes showed a weaker Spearman correlation and no significant Pearson correlation (ρ = 0.13, p < 0.01 r = 0, p = 0.61) (S2B and S2C Fig). This correlation mostly arises from neutral mutations as shown in the following section. An inverse U-shaped trend was detected for missense and silent mutations in oncogenes: highly recurrent mutations (observed in three and more samples) were characterized by low average mutability values (Fig 5). In the latter case, selection may be a more important factor compared to background mutation rate explaining reoccurrence of these mutations. Functionally conserved sites overall were found to be more frequently mutated in oncogenes [29], and our analysis did not find a straightforward association between mutability and evolutionary conservation.

Neutral mutations have higher mutability values than non-neutral

We complied a combined dataset of experimentally annotated missense mutations in cancer genes from several sources. Mutations were categorized as ‘non-neutral’ or ‘neutral’ based on their experimental effects on protein function, transforming effects, and other characteristics (see Methods and S4 Table). For all mutations in combined dataset, whether they were observed in MSK-IMPACT or the COSMIC v85 cohorts, the codon mutability values of neutral mutations were significantly higher (mean = 2.71 x 10 −6 ) (Mann-Whitney-Wilcoxon test, p < 0.01) than for non-neutral mutations (mean = 1.74 x 10 −6 ) (Fig 6A). Binning the mutations by their reoccurrence frequency also showed differences between ‘neutral’ and ‘non-neutral’, with the frequency of neutral mutation depending on their mutability. For neutral mutations, mutations that were observed in three or more samples had higher background mutability (meanMSK = 6.39 x 10 −6 , meanCOSMIC = 6.22 x 10 −6 ) compared to mutations which were not observed (meanMSK = 2.46 x 10 −6 , meanCOSMIC = 2.54 x 10 −6 ). In contrast, the background mutability of non-neutral mutations did not vary with the reoccurrence frequency (Fig 6B), suggesting that background mutability was much less important in driving reoccurrence of non-neutral mutations.

(A) Mutations from the combined dataset were categorized as neutral and non-neutral. Significant differences with p < 0.01 are marked with a double asterisk. Mutability was calculated with pan-cancer background model (B) Mutations binned by their reoccurrence frequency in both MSK-IMPACT (green) and COSMIC v85 (blue) cohorts. In both cohorts, reoccurrence frequency of neutral mutations depends on mutability, whereas for non-neutral mutations, reoccurrence frequency does not scale with background mutability.

Accounting for context-dependent mutability in ranking of mutations

In the previous sections we explored the contribution of background mutational processes in understanding the observed mutational patterns in cancer. With our finding that background mutability differs between neutral mutations and non-neutral mutations, we explored if background mutability could be used to facilitate the detection of cancer driver mutations or provide a reasonable ranking in terms of their potential carcinogenic effects. We tested different ways to calculate codon mutability and if it could help to differentiate between experimentally annotated neutral, or putatively passenger mutations, and non-neutral driver mutations. We found that a simple and intuitive measure, B-score, calculated (see next section) performed the best on the combined experimental test set. A similar measure was used previously to identify mutational hot spots [21, 30]. Hotspots are defined for sites, whereas our approach assesses specific mutations, and different mutations from the same hotspot can be drivers or passengers. For instance, TP53 Tyr236 site is annotated as a hotspot in [21, 30], however p.Tyr236Phe mutation in this site is experimentally characterized as neutral in the IARC database.

We compared the performance of B-score to six state-of-the-art computational methods which distinguish driver from passenger mutations in cancer: CHASM [31], CHASMplus [32], VEST[33], REVEL[34], CanDrAplus[35]and FatHMM[36]. Table 1 shows the performance of the various computational predictors at classifying mutations from the combined dataset observed in two sets of cancer cohorts. To compare across methods, which use different thresholds for calling neutral versus non-neutral mutations, we calculated the Matthew’s correlation coefficient (MCC) across a range of thresholds for each method and reported the maximal MCC value. Based on the MCC, the best classifiers are CHASMplus, B-score and CanDrAplus (MCC = 0.64, 0.61, and 0.58 respectively) (Table 1). Surprisingly, mutation reoccurrence frequency alone performs very well, with MCC of 0.49 in the COSMIC v85 cohort and 0.51 in the MSK-Impact cohort. B-Score is able to provide a correction to reoccurrence frequency using codon mutability and yields a much better performance than frequency alone. Intriguingly, inverse mutability alone performs better than random, emphasizing the fundamental quality of non-neutral mutations in cancer: mutability of driver mutations is lower than the mutability of passengers (Fig 6).

From combined experimental dataset. Mutations were observed in corresponding cancer cohorts. See S6 and S7 Tables for results on rare and all mutations. Maximum Matthew’s correlation is reported for each predictor which are ranked with respect to the maximum Matthew’s correlation coefficient. B-Score for each cohort is calculated with the respective cohort size: COSMIC v85 cohort 12,013 MSK-Impact 9,228. For CHASM the background model yielding best performance was chosen.

We also explored the performance of methods in classifying mutations that were not observed or observed only once in the COSMIC v85 cohort or MSK-Impact cohort (S6 Table). For mutations which were not observed in the COSMIC v85 cohort B-Score classification performance is low but better than random (AUC = 0.65). On mutations which were observed in only one cancer sample in the cohort (207 passenger and 157 driver mutations), B-Score still performed better than VEST and CHASM (MCC = 0.46, 0.42, and 0.36 respectively). On the combined set which includes all experimentally verified mutations, whether they were observed or not observed in cancer patients, B-score ranks fourth after CHASMplus, REVEL and FatHMM (S7 Table).

B-score also allows to break ties for mutations observed in the same number of patients. For example in the TP53 gene, mutations p.Glu11Lys and p.Cys135Gly have been observed in two patients each in the COSMIC v85 cohort. However, p.Glu11Lys (mutability of 1.18 x 10 −5 ) is predicted a passenger mutation and p.Cys135Gly (mutability of 2.20 x 10 −7 ) is predicted as a driver mutation which is consistent with the annotations from the experimental combined dataset.

Variability of mutation rates across genes

Even though our probabilistic model indirectly incorporates different factors affecting mutation rate, we checked explicitly if large-scale factors, allowing mutations of the same type to have different mutational probabilities in different genes, affected retrieval performance on the combined test set. Several methods have been developed to estimate gene weights (see Methods), which consider the overall number of mutations or the number of silent mutations affecting a gene. Additionally, we estimated the gene weights based on the number of SNPs in the vicinity of a gene. We also examined the effects of several large-scale confounding factors such as gene expression levels, replication timing, and chromatin accessibility (provided in the gene covariates in MutSigCV [37]) on gene weights. We used gene weights to adjust mutability values and explored whether any of the gene weight models were helpful in distinguishing between experimentally determined neutral and non-neutral mutations. We found that “no-outlier”-based weight (r = 0.66,p = 0.004) and “silent mutation”-based weight (r = 0.65,p = 0.004) significantly correlated with the gene expression levels. No other correlations of gene weights with confounding factors were found. Overall, using gene weight as an adjustment for varying background mutational rates across genes did not improve classification performance of mutations in the experimental benchmark. Only a SNP-based weight affected the AUC-ROC, but the gain was minimal, and no gene weight affected the MCC (S8 Table). It is consistent with the previous studies that found local DNA sequence context as a dominant factor explaining the largest proportion of mutation rate variation [10, 16].

Ranking of cancer point mutations in MutaGene

MutaGene webserver provides a collection of cancer-specific context-dependent mutational profiles [38]. It allows to calculate nucleotide and codon mutability and B-Score for missense, nonsense and silent mutations for any given protein coding DNA sequence and background mutagenesis model using the “Analyze gene” option. Following the analysis presented in this study, we added options to provide a ranking of mutations observed in cancer samples based on the B-Score or the multiple-testing adjusted q-values. Using the combined dataset as a performance benchmark (Table 1, S7 Table), we calibrated two thresholds: the first corresponds to the maximum of MCC, and the second corresponds to 10% FPR. Mutations with the B-Score below the first threshold are predicted to be “cancer drivers”, whereas mutations with scores in between two thresholds are predicted to be “potential drivers”. All mutations with scores above the second threshold are predicted as “passengers”. Importantly, calculations are not limited to pan-cancer and can be performed using a mutational profile for any particular cancer type, the latter would result in a cancer-specific ranking of mutations and could be useful for identification of driver mutations in a particular type of cancer. An example of prediction of driver mutations status for EGFR is shown in Fig 7. MutaGene Python package allows to rank mutations in a given sample or cohort in a batch mode using pre-calculated or user-provided mutational profiles or signatures and is available at https://www.ncbi.nlm.nih.gov/research/mutagene/gene.

Snapshots from the MutaGene server show the results of analysis of EGFR gene with a Pan-cancer model. (A) Scatterplot with expected mutability versus observed mutational frequencies. (B) Top list of mutations ranked by their B-Scores. (C) EGFR nucleotide and translated protein sequence shows per-nucleotide site mutability per codon mutability as well as mutabilities of nucleotide and codon substitutions (heatmaps). Mutations observed in tumors from ICGC repository are shown as circles colored by their prediction status: Driver, Potential driver, and Passenger. Missense mutation p.Arg252Pro is shown with a blue arrow.


Annual Review of Vision Science

2020 Release of Journal Citation Reports

The 2020 Edition of the Journal Citation Reports® (JCR) published by Clarivate Analytics provides a combination of impact and influence metrics from 2019 Web of Science source data. This measure provides a ratio of citations to a journal in a given year to the citable items in the prior two years.

Download Annual Reviews 2020 Edition JCR Rankings in Excel format.

Annual Review of: Rank Category Name Ranked Journals in Category Impact Factor Cited Half-Life Immediacy Index
Analytical Chemistry 6 Chemistry, Analytical 86 7.023 7.1 2.042
Analytical Chemistry3Spectroscopy427.0237.12.042
Animal Biosciences2Zoology1686.0914.13.125
Animal Biosciences17Biotechnology and Applied Microbiology1566.0914.13.125
Animal Biosciences1Agriculture, Dairy, and Animal Sciences636.0914.13.125
Animal Biosciences2Veterinary Science1426.0914.13.125
Anthropology6Anthropology903.17515.60.240
Astronomy and Astrophysics1Astronomy and Astrophysics6832.96310.85.133
Biochemistry3Biochemistry and Molecular Biology29725.78712.34.933
Biomedical Engineering2Biomedical Engineering8715.5419.01.524
Biophysics3Biophysics7111.6856.63.130
Cancer Biology53Oncology2445.4132.02.826
Cell and Developmental Biology13Cell Biology19514.66710.50.552
Cell and Developmental Biology1Developmental Biology4114.66710.50.552
Chemical and Biomolecular Engineering1Chemistry, Applied719.5615.60.941
Chemical and Biomolecular Engineering5Engineering, Chemical1439.5615.60.941
Clinical Psychology1Psychology, Clinical (Social Sciences)13113.6927.93.304
Clinical Psychology4Psychology (Science)7713.6927.93.304
Condensed Matter Physics6Physics, Condensed Matter6914.8334.97.273
Criminology1Criminology & Penology696.3481.40.955
Earth and Planetary Sciences4Geosciences, Multidisciplinary2009.08914.22.727
Earth and Planetary Sciences5Astronomy and Astrophysics689.08914.22.727
Ecology, Evolution, and Systematics2Evolutionary Biology5014.04117.40.440
Ecology, Evolution, and Systematics2Ecology16814.04117.40.440
Economics39Economics3713.5916.40.686
Entomology1Entomology10113.79614.34.762
Environment and Resources5Environmental Studies (Social Science)1238.0659.60.563
Environment and Resources14Environmental Sciences (Science)2658.0659.60.563
Financial Economics36Business, Finance1082.0577.00.167
Financial Economics107Economics3712.0577.00.167
Fluid Mechanics1Physics, Fluids and Plasmas3416.30615.49.190
Fluid Mechanics1Mechanics13616.30615.49.190
Food Science and Technology3Food Science & Technology1398.9605.22.615
Genetics5Genetics & Heredity17711.14610.80.500
Genomics and Human Genetics15Genetics & Heredity1777.2439.10.955
Immunology4Immunology15819.90010.75.875
Law and Social Science18Law1542.5887.70.233
Law and Social Science20Sociology1502.5887.70.233
Linguistics23Linguistics1872.0263.31.000
Marine Science2Geochemistry & Geophysics8516.3596.67.050
Marine Science1Marine & Freshwater Biology10616.3596.67.050
Marine Science1Oceanography6616.3596.67.050
Materials Research19Materials Science, Multidisciplinary31412.53110.62.267
Medicine6Medicine, Research & Experimental1389.7168.63.829
Microbiology9Microbiology13511.00013.70.967
Neuroscience9Neurosciences27112.54713.62.130
Nuclear and Particle Science2Physics, Nuclear198.7789.81.000
Nuclear and Particle Science3Physics, Particles and Fields298.7789.81.000
Nutrition2Nutrition & Dietetics8910.89714.20.714
Organizational Psychology and Organizational Behavior2Psychology, Applied8410.9234.41.222
Organizational Psychology and Organizational Behavior2Management22610.9234.41.222
Pathology: Mechanisms of Disease1Pathology7816.7507.26.500
Pharmacology and Toxicology1Toxicology9211.25011.45.793
Pharmacology and Toxicology5Pharmacology & Pharmacy27011.25011.45.793
Physical Chemistry19Chemistry, Physical15910.63812.13.667
Physiology2Physiology8119.55611.14.769
Phytopathology4Plant Sciences23412.62312.70.478
Plant Biology1Plant Sciences23419.54013.04.586
Political Science8Political Science1804.00011.30.750
Psychology2Psychology (Science)7718.15612.36.367
Psychology3Psychology, Multidisciplinary (Social Science)13818.15612.36.367
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Public Health3Public, Environmental & Occup. Health (Science)19316.4639.53.880
Resource Economics70Economics3712.7455.80.167
Resource Economics48Environmental Studies (Social Science)1162.7455.80.167
Resource Economics4Agricultural Economics and Policy (Science)212.7455.80.167
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Statistics and Its Application4Mathematics, Interdisciplinary Applications1065.0953.21.350
Statistics and Its Application2Statistics and Probability1245.0953.21.350
Virology2Virology378.0213.61.172
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Vision Science5Ophthalmology605.8973.40.391

AIMS AND SCOPE OF JOURNAL: The Annual Review of Vision Science reviews progress in the visual sciences, a cross-cutting set of disciplines which intersect psychology, neuroscience, computer science, cell biology and genetics, and clinical medicine. The journal covers a broad range of topics and techniques, including optics, retina, central visual processing, visual perception, eye movements, visual development, vision models, computer vision, and the mechanisms of visual disease, dysfunction, and sight restoration. The study of vision is central to progress in many areas of science, and this new journal will explore and expose the connections that link it to biology, behavior, computation, engineering, and medicine.


Annual Review of Cancer Biology

2020 Release of Journal Citation Reports

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Annual Review of: Rank Category Name Ranked Journals in Category Impact Factor Cited Half-Life Immediacy Index
Analytical Chemistry 6 Chemistry, Analytical 86 7.023 7.1 2.042
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Animal Biosciences2Zoology1686.0914.13.125
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Animal Biosciences1Agriculture, Dairy, and Animal Sciences636.0914.13.125
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Biochemistry3Biochemistry and Molecular Biology29725.78712.34.933
Biomedical Engineering2Biomedical Engineering8715.5419.01.524
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Cell and Developmental Biology13Cell Biology19514.66710.50.552
Cell and Developmental Biology1Developmental Biology4114.66710.50.552
Chemical and Biomolecular Engineering1Chemistry, Applied719.5615.60.941
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Clinical Psychology1Psychology, Clinical (Social Sciences)13113.6927.93.304
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Criminology1Criminology & Penology696.3481.40.955
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Ecology, Evolution, and Systematics2Ecology16814.04117.40.440
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Environment and Resources5Environmental Studies (Social Science)1238.0659.60.563
Environment and Resources14Environmental Sciences (Science)2658.0659.60.563
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Financial Economics107Economics3712.0577.00.167
Fluid Mechanics1Physics, Fluids and Plasmas3416.30615.49.190
Fluid Mechanics1Mechanics13616.30615.49.190
Food Science and Technology3Food Science & Technology1398.9605.22.615
Genetics5Genetics & Heredity17711.14610.80.500
Genomics and Human Genetics15Genetics & Heredity1777.2439.10.955
Immunology4Immunology15819.90010.75.875
Law and Social Science18Law1542.5887.70.233
Law and Social Science20Sociology1502.5887.70.233
Linguistics23Linguistics1872.0263.31.000
Marine Science2Geochemistry & Geophysics8516.3596.67.050
Marine Science1Marine & Freshwater Biology10616.3596.67.050
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Materials Research19Materials Science, Multidisciplinary31412.53110.62.267
Medicine6Medicine, Research & Experimental1389.7168.63.829
Microbiology9Microbiology13511.00013.70.967
Neuroscience9Neurosciences27112.54713.62.130
Nuclear and Particle Science2Physics, Nuclear198.7789.81.000
Nuclear and Particle Science3Physics, Particles and Fields298.7789.81.000
Nutrition2Nutrition & Dietetics8910.89714.20.714
Organizational Psychology and Organizational Behavior2Psychology, Applied8410.9234.41.222
Organizational Psychology and Organizational Behavior2Management22610.9234.41.222
Pathology: Mechanisms of Disease1Pathology7816.7507.26.500
Pharmacology and Toxicology1Toxicology9211.25011.45.793
Pharmacology and Toxicology5Pharmacology & Pharmacy27011.25011.45.793
Physical Chemistry19Chemistry, Physical15910.63812.13.667
Physiology2Physiology8119.55611.14.769
Phytopathology4Plant Sciences23412.62312.70.478
Plant Biology1Plant Sciences23419.54013.04.586
Political Science8Political Science1804.00011.30.750
Psychology2Psychology (Science)7718.15612.36.367
Psychology3Psychology, Multidisciplinary (Social Science)13818.15612.36.367
Public Health2Public, Environmental & Occup. Health (Social Science)17016.4639.53.880
Public Health3Public, Environmental & Occup. Health (Science)19316.4639.53.880
Resource Economics70Economics3712.7455.80.167
Resource Economics48Environmental Studies (Social Science)1162.7455.80.167
Resource Economics4Agricultural Economics and Policy (Science)212.7455.80.167
Sociology 1Sociology1506.40017.70.767
Statistics and Its Application4Mathematics, Interdisciplinary Applications1065.0953.21.350
Statistics and Its Application2Statistics and Probability1245.0953.21.350
Virology2Virology378.0213.61.172
Vision Science34Neurosciences2715.8973.40.391
Vision Science5Ophthalmology605.8973.40.391

AIMS AND SCOPE OF JOURNAL: The Annual Review of Cancer Biology reviews a range of subjects in cancer research that represent important and emerging areas in the field. With recent advances in our understanding of the basic mechanisms of cancer development and the translation of an increasing number of these findings into the clinic in the form of targeted treatments for the disease, the Annual Review of Cancer Biology is divided into three broad themes: Cancer Cell Biology, Tumorigenesis and Cancer Progression, and Translational Cancer Science. Volume 4 (2020) has been converted from gated to open access through Annual Reviews&rsquo Subscribe to Open program, with all articles published under a CC BY license. The back volumes, dating from 2017, are now freely available.


10.1 Cloning and Genetic Engineering

Biotechnology is the use of artificial methods to modify the genetic material of living organisms or cells to produce novel compounds or to perform new functions. Biotechnology has been used for improving livestock and crops since the beginning of agriculture through selective breeding. Since the discovery of the structure of DNA in 1953, and particularly since the development of tools and methods to manipulate DNA in the 1970s, biotechnology has become synonymous with the manipulation of organisms’ DNA at the molecular level. The primary applications of this technology are in medicine (for the production of vaccines and antibiotics) and in agriculture (for the genetic modification of crops). Biotechnology also has many industrial applications, such as fermentation, the treatment of oil spills, and the production of biofuels, as well as many household applications such as the use of enzymes in laundry detergent.

Manipulating Genetic Material

To accomplish the applications described above, biotechnologists must be able to extract, manipulate, and analyze nucleic acids.

Review of Nucleic Acid Structure

To understand the basic techniques used to work with nucleic acids, remember that nucleic acids are macromolecules made of nucleotides (a sugar, a phosphate, and a nitrogenous base). The phosphate groups on these molecules each have a net negative charge. An entire set of DNA molecules in the nucleus of eukaryotic organisms is called the genome. DNA has two complementary strands linked by hydrogen bonds between the paired bases.

Unlike DNA in eukaryotic cells, RNA molecules leave the nucleus. Messenger RNA (mRNA) is analyzed most frequently because it represents the protein-coding genes that are being expressed in the cell.

Isolation of Nucleic Acids

To study or manipulate nucleic acids, the DNA must first be extracted from cells. Various techniques are used to extract different types of DNA (Figure 10.2). Most nucleic acid extraction techniques involve steps to break open the cell, and then the use of enzymatic reactions to destroy all undesired macromolecules. Cells are broken open using a detergent solution containing buffering compounds. To prevent degradation and contamination, macromolecules such as proteins and RNA are inactivated using enzymes. The DNA is then brought out of solution using alcohol. The resulting DNA, because it is made up of long polymers, forms a gelatinous mass.

RNA is studied to understand gene expression patterns in cells. RNA is naturally very unstable because enzymes that break down RNA are commonly present in nature. Some are even secreted by our own skin and are very difficult to inactivate. Similar to DNA extraction, RNA extraction involves the use of various buffers and enzymes to inactivate other macromolecules and preserve only the RNA.

Gel Electrophoresis

Because nucleic acids are negatively charged ions at neutral or alkaline pH in an aqueous environment, they can be moved by an electric field. Gel electrophoresis is a technique used to separate charged molecules on the basis of size and charge. The nucleic acids can be separated as whole chromosomes or as fragments. The nucleic acids are loaded into a slot at one end of a gel matrix, an electric current is applied, and negatively charged molecules are pulled toward the opposite end of the gel (the end with the positive electrode). Smaller molecules move through the pores in the gel faster than larger molecules this difference in the rate of migration separates the fragments on the basis of size. The nucleic acids in a gel matrix are invisible until they are stained with a compound that allows them to be seen, such as a dye. Distinct fragments of nucleic acids appear as bands at specific distances from the top of the gel (the negative electrode end) that are based on their size (Figure 10.3). A mixture of many fragments of varying sizes appear as a long smear, whereas uncut genomic DNA is usually too large to run through the gel and forms a single large band at the top of the gel.

Polymerase Chain Reaction

DNA analysis often requires focusing on one or more specific regions of the genome. It also frequently involves situations in which only one or a few copies of a DNA molecule are available for further analysis. These amounts are insufficient for most procedures, such as gel electrophoresis. Polymerase chain reaction (PCR) is a technique used to rapidly increase the number of copies of specific regions of DNA for further analyses (Figure 10.4). PCR uses a special form of DNA polymerase, the enzyme that replicates DNA, and other short nucleotide sequences called primers that base pair to a specific portion of the DNA being replicated. PCR is used for many purposes in laboratories. These include: 1) the identification of the owner of a DNA sample left at a crime scene 2) paternity analysis 3) the comparison of small amounts of ancient DNA with modern organisms and 4) determining the sequence of nucleotides in a specific region.

Cloning

In general, cloning means the creation of a perfect replica. Typically, the word is used to describe the creation of a genetically identical copy. In biology, the re-creation of a whole organism is referred to as “reproductive cloning.” Long before attempts were made to clone an entire organism, researchers learned how to copy short stretches of DNA—a process that is referred to as molecular cloning.

Molecular Cloning

Cloning allows for the creation of multiple copies of genes, expression of genes, and study of specific genes. To get the DNA fragment into a bacterial cell in a form that will be copied or expressed, the fragment is first inserted into a plasmid. A plasmid (also called a vector in this context) is a small circular DNA molecule that replicates independently of the chromosomal DNA in bacteria. In cloning, the plasmid molecules can be used to provide a "vehicle" in which to insert a desired DNA fragment. Modified plasmids are usually reintroduced into a bacterial host for replication. As the bacteria divide, they copy their own DNA (including the plasmids). The inserted DNA fragment is copied along with the rest of the bacterial DNA. In a bacterial cell, the fragment of DNA from the human genome (or another organism that is being studied) is referred to as foreign DNA to differentiate it from the DNA of the bacterium (the host DNA).

Plasmids occur naturally in bacterial populations (such as Escherichia coli) and have genes that can contribute favorable traits to the organism, such as antibiotic resistance (the ability to be unaffected by antibiotics). Plasmids have been highly engineered as vectors for molecular cloning and for the subsequent large-scale production of important molecules, such as insulin. A valuable characteristic of plasmid vectors is the ease with which a foreign DNA fragment can be introduced. These plasmid vectors contain many short DNA sequences that can be cut with different commonly available restriction enzymes . Restriction enzymes (also called restriction endonucleases) recognize specific DNA sequences and cut them in a predictable manner they are naturally produced by bacteria as a defense mechanism against foreign DNA. Many restriction enzymes make staggered cuts in the two strands of DNA, such that the cut ends have a 2- to 4-nucleotide single-stranded overhang. The sequence that is recognized by the restriction enzyme is a four- to eight-nucleotide sequence that is a palindrome. Like with a word palindrome, this means the sequence reads the same forward and backward. In most cases, the sequence reads the same forward on one strand and backward on the complementary strand. When a staggered cut is made in a sequence like this, the overhangs are complementary (Figure 10.5).

Because these overhangs are capable of coming back together by hydrogen bonding with complementary overhangs on a piece of DNA cut with the same restriction enzyme, these are called “sticky ends.” The process of forming hydrogen bonds between complementary sequences on single strands to form double-stranded DNA is called annealing . Addition of an enzyme called DNA ligase, which takes part in DNA replication in cells, permanently joins the DNA fragments when the sticky ends come together. In this way, any DNA fragment can be spliced between the two ends of a plasmid DNA that has been cut with the same restriction enzyme (Figure 10.6).

Plasmids with foreign DNA inserted into them are called recombinant DNA molecules because they contain new combinations of genetic material. Proteins that are produced from recombinant DNA molecules are called recombinant proteins . Not all recombinant plasmids are capable of expressing genes. Plasmids may also be engineered to express proteins only when stimulated by certain environmental factors, so that scientists can control the expression of the recombinant proteins.

Reproductive Cloning

Reproductive cloning is a method used to make a clone or an identical copy of an entire multicellular organism. Most multicellular organisms undergo reproduction by sexual means, which involves the contribution of DNA from two individuals (parents), making it impossible to generate an identical copy or a clone of either parent. Recent advances in biotechnology have made it possible to reproductively clone mammals in the laboratory.

Natural sexual reproduction involves the union, during fertilization, of a sperm and an egg. Each of these gametes is haploid, meaning they contain one set of chromosomes in their nuclei. The resulting cell, or zygote, is then diploid and contains two sets of chromosomes. This cell divides mitotically to produce a multicellular organism. However, the union of just any two cells cannot produce a viable zygote there are components in the cytoplasm of the egg cell that are essential for the early development of the embryo during its first few cell divisions. Without these provisions, there would be no subsequent development. Therefore, to produce a new individual, both a diploid genetic complement and an egg cytoplasm are required. The approach to producing an artificially cloned individual is to take the egg cell of one individual and to remove the haploid nucleus. Then a diploid nucleus from a body cell of a second individual, the donor, is put into the egg cell. The egg is then stimulated to divide so that development proceeds. This sounds simple, but in fact it takes many attempts before each of the steps is completed successfully.

The first cloned agricultural animal was Dolly, a sheep who was born in 1996. The success rate of reproductive cloning at the time was very low. Dolly lived for six years and died of a lung tumor (Figure 10.7). There was speculation that because the cell DNA that gave rise to Dolly came from an older individual, the age of the DNA may have affected her life expectancy. Since Dolly, several species of animals (such as horses, bulls, and goats) have been successfully cloned.

There have been attempts at producing cloned human embryos as sources of embryonic stem cells. In the procedure, the DNA from an adult human is introduced into a human egg cell, which is then stimulated to divide. The technology is similar to the technology that was used to produce Dolly, but the embryo is never implanted into a surrogate mother. The cells produced are called embryonic stem cells because they have the capacity to develop into many different kinds of cells, such as muscle or nerve cells. The stem cells could be used to research and ultimately provide therapeutic applications, such as replacing damaged tissues. The benefit of cloning in this instance is that the cells used to regenerate new tissues would be a perfect match to the donor of the original DNA. For example, a leukemia patient would not require a sibling with a tissue match for a bone-marrow transplant.

Visual Connection

Why was Dolly a Finn-Dorset and not a Scottish Blackface sheep?

Genetic Engineering

Using recombinant DNA technology to modify an organism’s DNA to achieve desirable traits is called genetic engineering . Addition of foreign DNA in the form of recombinant DNA vectors that are generated by molecular cloning is the most common method of genetic engineering. An organism that receives the recombinant DNA is called a genetically modified organism (GMO). If the foreign DNA that is introduced comes from a different species, the host organism is called transgenic . Bacteria, plants, and animals have been genetically modified since the early 1970s for academic, medical, agricultural, and industrial purposes. These applications will be examined in more detail in the next module.

Concepts in Action

Watch this short video explaining how scientists create a transgenic animal.

Although the classic methods of studying the function of genes began with a given phenotype and determined the genetic basis of that phenotype, modern techniques allow researchers to start at the DNA sequence level and ask: "What does this gene or DNA element do?" This technique, called reverse genetics , has resulted in reversing the classical genetic methodology. One example of this method is analogous to damaging a body part to determine its function. An insect that loses a wing cannot fly, which means that the wing’s function is flight. The classic genetic method compares insects that cannot fly with insects that can fly, and observes that the non-flying insects have lost wings. Similarly in a reverse genetics approach, mutating or deleting genes provides researchers with clues about gene function. Alternately, reverse genetics can be used to cause a gene to overexpress itself to determine what phenotypic effects may occur.

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    Results and discussion

    The HOXAgene cluster as a test locus

    The HOX clusters encode transcription factors that are important for embryonic development and hematopoietic lineage regulation [39, 40]. Aberrant HOX expression is found in various types of human cancers including lung cancer [41], breast cancer [42], melanoma [43], and leukemia [44]. HOXA9 and 10 for instance are oncogenes overexpressed in various leukemia types and are direct targets of MLL fusion oncoproteins [45–47]. In mammals, there are 39 HOX genes organized into 13 paralogue groups and divided into four clusters named A, B, C, and D located on different chromosomes [48, 49]. The human HOXA cluster spans over 100 kbp on chromosome 7 and encodes 11 transcription factors (Figure 1A). To determine whether chromatin architecture can be used to classify disease, we mapped the organization of a region containing the HOXA cluster with the chromosome conformation capture carbon copy (5C) technology (Figure 1B). The 5C method is a member of the so-called ‘3C technologies’ used to measure genome organization in vivo at high-resolution [50, 51]. 5C captures chromatin conformation by converting chemically cross-linked chromatin segments into unique ligation products, which are then detected high-throughput using a modified version of ligation mediated amplification (LMA).

    Experimental design used to generate the training set for 3D-SP. (A) Linear schematic representation of the human HOXA cluster region characterized in this study. Genes are illustrated as left-facing arrows to indicate transcription direction and highlight the 3’ to 5’ end orientation of the cluster. The 11 paralogue groups are color-coded and identified above each gene. BglII restriction fragments of the HOXA region characterized here are shown below and identified by numbers from left to right. (B) Diagram of the 5C technology. 5C quantitatively measures chromatin contacts using primers that are complementary to predicted junctions in 3C libraries. Annealed 5C primers are ligated with Taq DNA ligase and products are amplified by PCR using oligos recognizing the universal tails of 5C primers. In this study, amplification was done using a fluorescently labeled reverse PCR primer and amplified 5C libraries were hybridized onto custom microarrays. (C) Leukemia cell panel used to train 3D-SP. Cell lines are organized by leukemia type and MLL status. AF9 AF9 gene (ALL1-fused gene from chromosome 9), ENL ENL gene (eleven-nineteen-leukemia), wt wild type, AML acute myeloid leukemia, ALL acute lymphoblastic leukemia, EC embryonic carcinoma. (D) Distribution of leukemia cell samples used to train 3D-SP. Left pie chart indicates the distribution of leukemias expressing either MLL fusions or the wt protein (Leukemia types). Right pie chart shows the distribution of MLL types (MLL fusion types).

    Using an experimental design previously described [38] we measured chromatin contacts throughout the HOXA cluster region in a panel of leukemia cell lines (Figure 1C). This panel, which is detailed in Additional file 1: Supplementary Materials and methods, includes 20 samples expressing MLL fusions and 10 with only the wt protein (Figure 1D, left). The panel featured AML and ALL caused by a fusion between MLL and the AF9 gene (MLL-AF9 AML), MLL and the ENL gene (MLL-ENL ALL), or expressing the wild-type (wt) protein (Figure 1D, right). We also included three embryonic carcinomas (EC) samples in this training set to increase diversity. These are known to encode only the wt MLL protein and express no HOXA genes ([52] and Additional file 2: Table S1). The normalized 5C data from these samples were derived as detailed in Additional file 1: Supplementary Materials and methods and shown in Additional file 3: Figure S1. The 5C datasets are presented in heatmap form in Additional file 4: Figures S2, and show a very high degree of variability between all samples, regardless of whether they express MLL fusions, the wt protein are AML or ALL. When comparing the average HOXA interaction frequencies (IFs) in leukemia samples expressing MLL fusions to those encoding only wt MLL, we could find marked differences in contact frequencies between neighbors (heatmap diagonal) and between distal fragments interacting long-range (Figure 2A). For example, we observed higher local contacts around the HOXA3 gene in MLL fusion samples, and more long-range interactions between the 5’ end, the middle and the 3’ end of the cluster in samples where only the wt MLL protein is expressed (Figure 2A, right). These results indicate that the HOXA chromatin conformation in leukemia cell lines expressing MLL fusions and MLL wt might differ sufficiently to be used for classification.

    The HOXA cluster conformation can be used to classify leukemia cell samples. (A) Averaged 5C interaction frequencies from MLL-fusion leukemia cell samples (left), cells encoding only the wt MLL protein (middle), and difference between the two MLL leukemia types (right). The data from the left panel are the average of the 5C datasets presented in Additional file 4: Figure S2A, and the middle panel contains the averaged data from Additional file 4: Figure S2B. Normalized pair-wise interaction frequencies are color-coded according to the scale shown on the bottom left of each heatmap. Numbers above and on the right of each heatmap identify BglII restriction fragments corresponding to the restriction pattern shown below the HOXA diagrams. Intersecting column and row numbers identify DNA contacts. (B) Classification results of the 3D-SP trained to distinguish between samples expressing MLL fusions and the wt MLL protein. (C) Classification results of the 3D-SP trained to distinguish between samples expressing either MLL-AF9 or MLL-ENL. For B and C, the leukemia training set shown in Figure 1C was used to train 3D-SP. Results shown are from a leave-one-out cross-validation of the 3D-SPs. The pie chart on the right of each table shows the overall accuracy of the corresponding 3D-SP. Matthews Correlation Coefficient (MCC) = 0.64.

    Development, training, and performance of 3D-SP

    Although significant differences could be observed between averaged IFs, this type of ‘direct’ measurement does not reliably identify contacts that most consistently describe a particular leukemia type and that could be used for classification. Indeed, average or greater IFs in a given sample set might simply originate from the presence of a few samples where these contacts are high. To more robustly distinguish between leukemia types, we developed a support vector machine (SVM [53]) classifier called ‘3D-SP’ (3-Dimensional DNA Disease-Signature Predictor), which uses the complete IF data from a 5C experiment as basis for classification. We opted for an SVM since they were previously shown to yield good accuracy classifiers even for high-dimensional data [54].

    3D-SP was evaluated using leave-one-out cross-validation on the set S of 30 samples shown in Figure 1C. Specifically, for each sample s in S, a classifier was trained on the 29 remaining samples (S - ) and then used to predict the class of s. The result of this cross-validation procedure is then reported as one entry in the confusion matrix shown in Figure 2B. This ensures that no predictor was trained using the sample on which it is asked to make a prediction. Using this approach, we found that leukemia samples expressing either MLL fusions or the wt protein could be classified with 83% accuracy by 3D-SP (Figure 2B). Training 3D-SP to recognize features specific to MLL fusion subtypes also yielded good classification results by leave-one-out cross-validation albeit with a lower accuracy of 73% (Figure 2C). These results demonstrate that the HOXA cluster organization can be used to classify different leukemia types.

    Identifying highly predictive chromatin contacts

    We next wondered which HOXA contacts showed the greatest difference between classes and conferred the largest amount of predictive power in the classification of leukemias expressing either MLL fusions or the wt protein. By measuring the information gain score of each pair-wise interaction, we found that over 20 different contacts contributed information that enhanced the classification performance (Figure 3A Student t-test P value <0.01). The information gain estimates the reduction of entropy in the classification achieved by each contact, and can therefore be used to identify discriminatory interactions. As expected, there were much fewer contacts than those displaying large averaged IFs differences (compare Figures 2A and 3A). For instance, the predictor did not retain most neighboring interactions, which were strong IF values that differed greatly between leukemia sets.

    Distinct contact patterns identify MLL fusion from wt MLL leukemias. (A) Heatmap representation of the information gain scores of contacts throughout the HOXA cluster. The values are color-coded according to the scale shown at the bottom of the heatmap. Numbers on the left and right of heatmap identify BglII restriction fragments corresponding to the restriction pattern shown below the HOXA diagram. Intersecting column and row numbers identify DNA contacts. (B) Averaged interaction frequencies of 22 contacts with high information gain scores (IF values between MLL fusions and wt MLL are statistically different, P <0.01). Interaction frequencies in MLL fusion datasets are the averaged values from Additional file 4: Figure S2A, and wt MLL values are from Additional file 4: Figure S2B. Error bars represent the standard error of the mean. (C) Distribution of informative contacts for classification, and binding of CTCF and cohesin in THP-1 cells along the human HOXA cluster region examined. The y-axis shows the number of CTCF ‘Tags per 10 millions’ obtained by ChIP-seq after normalization against input, across the region characterized (x-axis). CTCF peaks are numbered from left to right (CTCF1 to 7). Regions forming contacts with high predictive power are highlighted in orange.

    Interestingly, we observed a significant difference between the average IF values of informative contacts in leukemias expressing MLL fusions compared to the wt protein (Figure 3B). Specifically, we found that a region downstream of HOXA13 at the cluster 5’ end preferentially interacts with its 3’ part in wt MLL samples (Figure 3C, fragments 31 to 35). In contrast, more contacts were observed between the HOXA11 gene (fragments 26 and 27) and the cluster, suggesting that these two regions are differentially regulated in leukemias expressing MLL fusions. This result was interesting in light of our previous report that differentiation of THP-1 promyelomonocytic leukemia cells into macrophages is accompanied by transcription repression of 5’ end genes and the formation of long-range contacts between the ends of the cluster [38]. Given that MLL fusions appear to alter organization, perhaps by modifying the chromatin at specific regions along the cluster, this result might also provide insight on how the fusions activate transcription. Whether DNA sequences at the HOXA11 and HOXA13 regions are important for the observed conformational changes is unclear but mapping of CTCF and cohesin by ChIP-seq shows that the two proteins bind to these regions (Figure 3C, bottom). CTCF and cohesin are known to form long-range interactions and it will be interesting to see whether their association with the chromatin or binding to each other to form loops are specifically targeted by MLL fusions.

    In a similar manner, we looked for HOXA contacts showing the greatest informative differences between the MLL fusion subtype classes (Figure 4). For this, we measured the information gain value of each feature for the subtype prediction task and found 20 contacts with significant predictive value (Student t-test P value <0.01) (Figure 4A). In contrast to the predictive features of MLL leukemia types, the contacts distinguishing MLL-ENL from MLL-AF9 leukemias were distributed throughout the cluster (Figure 4B,C) and did not particularly cluster at sites bound by CTCF and cohesin. These were generally stronger in MLL-AF9 samples, except for 14-15 and 32-33 that were also identified as good predictors of wt MLL samples. We do not think that stronger contacts in MLL-AF9 can be explained simply by more expression of the cluster in either of the sample sets since each featured high and low expressers (Additional file 2: Table S1). Also, transcriptional activity does not appear to be a defining parameter in classification (see below and Additional file 5: Figure S4). Thus, we favor a model whereby different MLL fusions lead to distinct chromatin conformations by specifically recruiting proteins and modifying the chromatin at the cluster.

    Contacts throughout the HOXA cluster distinguish fusion subtypes. (A) The information gain scores of HOXA contacts distinguishing MLL-AF9 from MLL-ENL leukemias are represented in heatmap form. The values are color-coded according to the scale shown at the bottom of the heatmap. Numbers on the left and right of heatmap identify BglII restriction fragments corresponding to the restriction pattern shown below the HOXA diagram. Intersecting column and row numbers identify DNA contacts. (B) Averaged interaction frequencies of 20 contacts with high information gain scores (IF values between MLL fusions are statistically different, P <0.01). Interaction frequencies in MLL fusion datasets are the averaged values from Additional file 4: Figure S2. Error bars represent the standard error of the mean. (C) Distribution of informative contacts for classification, and binding of CTCF and cohesin in THP-1 cells along the human HOXA cluster region examined. The y-axis shows the number of CTCF ‘Tags per 10 millions’ obtained by ChIP-seq after normalization against input, across the region characterized (x-axis). CTCF peaks are numbered from left to right (CTCF1 to 7).

    De novoclassification of MLL leukemia samples with 3D-SP

    All the analyses with 3D-SP presented above were performed using a leave-one-out cross-validation approach and we wanted to confirm that the classifier would generalize to new samples. To this end, we generated 5C interaction maps for a test leukemia cell panel (Figure 5A and Additional file 6: Figure S3), and used the 3D-SP previously trained to distinguish between MLL wt and fusions with the training set (Figure 1C) to classify these data. The test leukemia set included leukemia cell lines expressing MLL-AF6, MLL-AFX, and MLL-AF4 and a new cell line expressing the MLL-AF9 fusion. Cell lines encoding wt MLL included AML, ALL, and the EC cell line NT2D1 induced with retinoic acid for 24 h. We added this sample because although it does not express an MLL fusion, the 3’ end genes are expressed and we expect the cluster to adopt an open configuration [52]. We found that 3D-SP classified the test leukemia cell lines expressing MLL fusions or wt MLL with perfect accuracy. Furthermore, 3D-SP also correctly classified five biological replicates of the MLL-AF9 expressing THP-1 samples produced in another study [55]. Even the EC sample expressing 3’ end HOXA genes was correctly classified suggesting transcription and opening of the cluster were not determining parameters in the classification by 3D-SP.

    3D-SP correctly classifies MLL leukemia types de novo . (A) Leukemia cell panel used to test 3D-SP. Cell lines are organized by MLL status and leukemia type. (B) Classification results of the test leukemia cell panel by the 3D-SP trained to distinguish between MLL fusion and wt MLL (Figure 1C). Results shown are from de novo classification by the 3D-SP. Matthews Correlation Coefficient (MCC) = 1.0.

    Indeed, transcription activity at the cluster did not seem to be a deciding factor in the classification since three of the four cell lines expressing MLL wt (HL60, U937, MOLT-4) expressed significant levels of HOXA genes, while one of the MLL fusion leukemia cell lines (Karpas-45) did not express the genes at all and yet, all were correctly classified (Additional file 2: Table S1). In fact, we found that 5C performed much better than gene expression when we compared SVM classification of a representative cell panel based on HOXA9 expression (48%), all HOXA gene expression (62%) or on the 5C data (93% Additional file 5: Figure S4B and C). Prediction based on gene expression was improved when a decision tree classifier was used instead of an SVM (HOXA9 86%, all HOXA 83%) but remained slightly lower than 5C classification with an SVM (Additional file 5: Figure S4D). Although our data do not definitively show that chromatin conformation is more robust than gene expression, 5C data do appear to contain additional information not present in gene expression datasets that improve classification. Together, these results provide the very first proof of principle that 3D chromatin organization of the HOXA cluster can be used to classify MLL fusion leukemia cell types.


    Table of Contents

    1 Evolution, the Themes of Biology, and Scientific Inquiry

    Inquiring About Life

    CONCEPT 1.1 The study of life reveals common themes 

    CONCEPT 1.2 The Core Theme: Evolution accounts for the unity and diversity of life 

    CONCEPT 1.3 In studying nature, scientists make observations and form and test hypotheses 

    CONCEPT 1.4 Science benefits from a cooperative approach and diverse viewpoints 

    UNIT 1 THE CHEMISTRY OF LIFE 

    2 The Chemical Context of Life

    A Chemical Connection to Biology

    CONCEPT 2.1 Matter consists of chemical elements in pure form and in combinations called compounds 

    CONCEPT 2.2 An element&rsquos properties depend on the structure of its atoms 

    CONCEPT 2.3 The formation and function of molecules depend on chemical bonding between atoms 

    CONCEPT 2.4 Chemical reactions make and break chemical bonds 

    3 Water and Life

    The Molecule That Supports All of Life

    CONCEPT 3.1 Polar covalent bonds in water molecules result in hydrogen bonding 

    CONCEPT 3.2 Four emergent properties of water contribute to Earth&rsquos suitability for life 

    CONCEPT 3.3 Acidic and basic conditions affect living organisms 

    4 Carbon and the Molecular Diversity of Life 

    Carbon: The Backbone of Life

    CONCEPT 4.1 Organic chemistry is the study of carbon compounds 

    CONCEPT 4.2 Carbon atoms can form diverse molecules by bonding to four other atoms 

    CONCEPT 4.3 A few chemical groups are key to molecular function 

    5 The Structure and Function of Large Biological Molecules

    The Molecules of Life

    CONCEPT 5.1 Macromolecules are polymers, built from monomers 

    CONCEPT 5.2 Carbohydrates serve as fuel and building material 

    CONCEPT 5.3 Lipids are a diverse group of hydrophobic molecules 

    CONCEPT 5.4 Proteins include a diversity of structures, resulting in a wide range of functions 

    CONCEPT 5.5 Nucleic acids store, transmit, and help express hereditary information 

    CONCEPT 5.6 Genomics and proteomics have transformed biological inquiry and applications 

    UNIT 2 THE CELL 

    6 A Tour of the Cell

    The Fundamental Units of Life

    CONCEPT 6.1 Biologists use microscopes and biochemistry to study cells 

    CONCEPT 6.2 Eukaryotic cells have internal membranes that compartmentalize their functions 

    CONCEPT 6.3 The eukaryotic cell&rsquos genetic instructions are housed in the nucleus and carried out by the ribosomes 

    CONCEPT 6.4 The endomembrane system regulates protein traffic and performs metabolic functions 

    CONCEPT 6.5 Mitochondria and chloroplasts change energy from one form to another 

    CONCEPT 6.6 The cytoskeleton is a network of fibers that organizes structures and activities in the cell 

    CONCEPT 6.7 Extracellular components and connections between cells help coordinate cellular activities 

    CONCEPT 6.8 A cell is greater than the sum of its parts

    7 Membrane Structure and Function

    Life at the Edge

    CONCEPT 7.1 Cellular membranes are fluid mosaics of lipids and proteins 

    CONCEPT 7.2 Membrane structure results in selective permeability 

    CONCEPT 7.3 Passive transport is diffusion of a substance across a membrane with no energy investment 

    CONCEPT 7.4 Active transport uses energy to move solutes against their gradients 

    CONCEPT 7.5 Bulk transport across the plasma membrane occurs by exocytosis and endocytosis 

    8 An Introduction to Metabolism

    The Energy of Life

    CONCEPT 8.1 An organism&rsquos metabolism transforms matter and energy, subject to the laws of thermodynamics 

    CONCEPT 8.2 The free-energy change of a reaction tells us whether or not the reaction occurs spontaneously 

    CONCEPT 8.3 ATP powers cellular work by coupling exergonic reactions to endergonic reactions 

    CONCEPT 8.4 Enzymes speed up metabolic reactions by lowering energy barriers 

    CONCEPT 8.5 Regulation of enzyme activity helps control metabolism 

    9 Cellular Respiration and Fermentation

    CONCEPT 9.1 Catabolic pathways yield energy by oxidizing organic fuels 

    CONCEPT 9.2 Glycolysis harvests chemical energy by oxidizing glucose to pyruvate 

    CONCEPT 9.3 After pyruvate is oxidized, the citric acid cycle completes the energy-yielding oxidation of organic molecules 

    CONCEPT 9.4 During oxidative phosphorylation, chemiosmosis couples electron transport to ATP synthesis 

    CONCEPT 9.5 Fermentation and anaerobic respiration enable cells to produce ATP without the use of oxygen 

    CONCEPT 9.6 Glycolysis and the citric acid cycle connect to many other metabolic pathways 

    10 Photosynthesis

    The Process That Feeds the Biosphere

    CONCEPT 10.1 Photosynthesis converts light energy to the chemical energy of food 

    CONCEPT 10.2 The light reactions convert solar energy to the chemical energy of ATP and NADPH 

    CONCEPT 10.3 The Calvin cycle uses the chemical energy of ATP and NADPH to reduce CO2 to sugar 

    CONCEPT 10.4 Alternative mechanisms of carbon fixation have evolved in hot, arid climates

    CONCEPT 10.5Life depends on photosynthesis  

    11 Cell Communication

    Cellular Messaging

    CONCEPT 11.1 External signals are converted to responses within the cell 

    CONCEPT 11.2 Reception: A signaling molecule binds to a receptor protein, causing it to change shape 

    CONCEPT 11.3 Transduction: Cascades of molecular interactions relay signals from receptors to target molecules in the cell 

    CONCEPT 11.4 Response: Cell signaling leads to regulation of transcription or cytoplasmic activities 

    CONCEPT 11.5 Apoptosis integrates multiple cell-signaling pathways 

    12 The Cell Cycle

    The Key Roles of Cell Division

    CONCEPT 12.1 Most cell division results in genetically identical daughter cells 

    CONCEPT 12.2 The mitotic phase alternates with interphase in the cell cycle 

    CONCEPT 12.3 The eukaryotic cell cycle is regulated by a molecular control system 

    UNIT 3 GENETICS 

    13 Meiosis and Sexual Life Cycles

    Variations on a Theme

    CONCEPT 13.1 Offspring acquire genes from parents by inheriting chromosomes 

    CONCEPT 13.2 Fertilization and meiosis alternate in sexual life cycles 

    CONCEPT 13.3 Meiosis reduces the number of chromosome sets from diploid to haploid 

    CONCEPT 13.4 Genetic variation produced in sexual life cycles contributes to evolution 

    14 Mendel and the Gene Idea

    Drawing from the Deck of Genes

    CONCEPT 14.1 Mendel used the scientific approach to identify two laws of inheritance 

    CONCEPT 14.2 Probability laws govern Mendelian inheritance 

    CONCEPT 14.3 Inheritance patterns are often more complex than predicted by simple Mendelian genetics 

    CONCEPT 14.4 Many human traits follow Mendelian patterns of inheritance 

    15 The Chromosomal Basis of Inheritance

    Locating Genes Along Chromosomes

    CONCEPT 15.1 Morgan showed that Mendelian inheritance has its physical basis in the behavior of chromosomes: scientific inquiry

    CONCEPT 15.2 Sex-linked genes exhibit unique patterns of inheritance 

    CONCEPT 15.3 Linked genes tend to be inherited together because they are located near each other on the same chromosome 

    CONCEPT 15.4 Alterations of chromosome number or structure cause some genetic disorders 

    CONCEPT 15.5 Some inheritance patterns are exceptions to standard Mendelian inheritance 

    16 The Molecular Basis of Inheritance

    Life&rsquos Operating Instructions

    CONCEPT 16.1 DNA is the genetic material 

    CONCEPT 16.2 Many proteins work together in DNA replication and repair 

    CONCEPT 16.3 A chromosome consists of a DNA molecule packed together with proteins 

    17 Gene Expression: From Gene to Protein

    The Flow of Genetic Information

    CONCEPT 17.1 Genes specify proteins via transcription and translation 

    CONCEPT 17.2 Transcription is the DNA-directed synthesis of RNA: a closer look

    CONCEPT 17.3 Eukaryotic cells modify RNA after transcription 

    CONCEPT 17.4 Translation is the RNA-directed synthesis of a polypeptide: a closer look

    CONCEPT 17.5 Mutations of one or a few nucleotides can affect protein structure and function 

    18 Regulation of Gene Expression

    Beauty in the Eye of the Beholder

    CONCEPT 18.1 Bacteria often respond to environmental change by regulating transcription 

    CONCEPT 18.2 Eukaryotic gene expression is regulated at many stages 

    CONCEPT 18.3 Noncoding RNAs play multiple roles in controlling gene expression 

    CONCEPT 18.4 A program of differential gene expression leads to the different cell types in a multicellular organism 

    CONCEPT 18.5 Cancer results from genetic changes that affect cell cycle control 

    A Borrowed Life

    CONCEPT 19.1 A virus consists of a nucleic acid surrounded by a protein coat 

    CONCEPT 19.2 Viruses replicate only in host cells 

    CONCEPT 19.3 Viruses and prions are formidable pathogens in animals and plants 

    20 DNA Tools and Biotechnology

    The DNA Toolbox

    CONCEPT 20.1 DNA sequencing and DNA cloning are valuable tools for genetic engineering and biological inquiry 

    CONCEPT 20.2 Biologists use DNA technology to study gene expression and function 

    CONCEPT 20.3 Cloned organisms and stem cells are useful for basic research and other applications 

    CONCEPT 20.4 The practical applications of DNA-based biotechnology affect our lives in many ways 

    21 Genomes and Their Evolution

    Reading the Leaves from the Tree of Life

    CONCEPT 21.1 The Human Genome Project fostered development of faster, less expensive sequencing techniques 

    CONCEPT 21.2 Scientists use bioinformatics to analyze genomes and their functions 

    CONCEPT 21.3 Genomes vary in size, number of genes, and gene density 

    CONCEPT 21.4 Multicellular eukaryotes have a lot of noncoding DNA and many multigene families 

    CONCEPT 21.5 Duplication, rearrangement, and mutation of DNA contribute to genome evolution 

    CONCEPT 21.6 Comparing genome sequences provides clues to evolution and development 

    UNIT 4 MECHANISMS OF EVOLUTION 

    22 Descent with Modification: A Darwinian View of Life

    Endless Forms Most Beautiful

    CONCEPT 22.1 The Darwinian revolution challenged traditional views of a young Earth inhabited by unchanging species

    CONCEPT 22.2 Descent with modification by natural selection explains the adaptations of organisms and the unity and diversity of life 

    CONCEPT 22.3 Evolution is supported by an overwhelming amount of scientific evidence 

    23 The Evolution of Populations

    The Smallest Unit of Evolution

    CONCEPT 23.1 Genetic variation makes evolution possible

    CONCEPT 23.2 The Hardy-Weinberg equation can be used to test whether a population is evolving 

    CONCEPT 23.3 Natural selection, genetic drift, and gene flow can alter allele frequencies in a population 

    CONCEPT 23.4 Natural selection is the only mechanism that consistently causes adaptive evolution 

    24 The Origin of Species

    That &ldquoMystery of Mysteries&rdquo

    CONCEPT 24.1 The biological species concept emphasizes reproductive isolation 

    CONCEPT 24.2 Speciation can take place with or without geographic separation 

    CONCEPT 24.3 Hybrid zones reveal factors that cause reproductive isolation 

    CONCEPT 24.4 Speciation can occur rapidly or slowly and can result from changes in few or many genes 

    25 The History of Life on Earth

    A Surprise in the Desert 

    CONCEPT 25.1 Conditions on early Earth made the origin of life possible 

    CONCEPT 25.2 The fossil record documents the history of life 

    CONCEPT 25.3 Key events in life&rsquos history include the origins of unicellular and multicellular organisms and the colonization of land 

    CONCEPT 25.4 The rise and fall of groups of organisms reflect differences in speciation and extinction rates 

    CONCEPT 25.5 Major changes in body form can result from changes in the sequences and regulation of developmental genes 

    CONCEPT 25.6 Evolution is not goal oriented 

    UNIT 5 THE EVOLUTIONARY HISTORY OF BIOLOGICAL DIVERSITY 

    26 Phylogeny and the Tree of Life

    Investigating the Tree of Life

    CONCEPT 26.1 Phylogenies show evolutionary relationships 

    CONCEPT 26.2 Phylogenies are inferred from morphological and molecular data

    CONCEPT 26.3 Shared characters are used to construct phylogenetic trees 

    CONCEPT 26.4 An organism&rsquos evolutionary history is documented in its genome 

    CONCEPT 26.5 Molecular clocks help track evolutionary time 

    CONCEPT 26.6 Our understanding of the tree of life continues to change based on new data 

    27 Bacteria and Archaea

    Masters of Adaptation

    CONCEPT 27.1 Structural and functional adaptations contribute to prokaryotic success 

    CONCEPT 27.2 Rapid reproduction, mutation, and genetic recombination promote genetic diversity in prokaryotes 

    CONCEPT 27.3 Diverse nutritional and metabolic adaptations have evolved in prokaryotes 

    CONCEPT 27.4 Prokaryotes have radiated into a diverse set of lineages 

    CONCEPT 27.5 Prokaryotes play crucial roles in the biosphere 

    CONCEPT 27.6 Prokaryotes have both beneficial and harmful impacts on humans 

    CONCEPT 28.1 Most eukaryotes are single-celled organisms 

    CONCEPT 28.2 Excavates include protists with modified mitochondria and protists with unique flagella 

    CONCEPT 28.3 SAR is a highly diverse group of protists defined by DNA similarities 

    CONCEPT 28.4 Red algae and green algae are the closest relatives of land plants 

    CONCEPT 28.5 Unikonts include protists that are closely related to fungi and animals 

    CONCEPT 28.6 Protists play key roles in ecological communities 

    29 Plant Diversity I: How Plants Colonized Land

    The Greening of Earth

    CONCEPT 29.1 Plants evolved from green algae 

    CONCEPT 29.2 Mosses and other nonvascular plants have life cycles dominated by gametophytes 

    CONCEPT 29.3 Ferns and other seedless vascular plants were the first plants to grow tall 

    30 Plant Diversity II: The Evolution of Seed Plants

    Transforming the World

    CONCEPT 30.1 Seeds and pollen grains are key adaptations for life on land 

    CONCEPT 30.2 Gymnosperms bear &ldquonaked&rdquo seeds, typically on cones 

    CONCEPT 30.3 The reproductive adaptations of angiosperms include flowers and fruits 

    CONCEPT 30.4 Human welfare depends on seed plants 

    Mighty Mushrooms

    CONCEPT 31.1 Fungi are heterotrophs that feed by absorption 

    CONCEPT 31.2 Fungi produce spores through sexual or asexual life cycles 

    CONCEPT 31.3 The ancestor of fungi was an aquatic, single-celled, flagellated protist 

    CONCEPT 31.4 Fungi have radiated into a diverse set of lineages 

    CONCEPT 31.5 Fungi play key roles in nutrient cycling, ecological interactions, and human welfare 

    32 An Overview of Animal Diversity

    A Kingdom of Consumers

    CONCEPT 32.1 Animals are multicellular, heterotrophic eukaryotes with tissues that develop from embryonic layers 

    CONCEPT 32.2 The history of animals spans more than half a billion years 

    CONCEPT 32.3 Animals can be characterized by &ldquobody plans&rdquo 

    CONCEPT 32.4 Views of animal phylogeny continue to be shaped by new molecular and morphological data

    33 An Introduction to Invertebrates

    A Dragon Without a Backbone

    CONCEPT 33.1 Sponges are basal animals that lack tissues 

    CONCEPT 33.2 Cnidarians are an ancient phylum of eumetazoans 

    CONCEPT 33.3 Lophotrochozoans, a clade identified by molecular data, have the widest range of animal body forms 

    CONCEPT 33.4 Ecdysozoans are the most species-rich animal group

    CONCEPT 33.5 Echinoderms and chordates are deuterostomes 

    34 The Origin and Evolution of Vertebrates

    Half a Billion Years of Backbones

    CONCEPT 34.1 Chordates have a notochord and a dorsal, hollow nerve cord 

    CONCEPT 34.2 Vertebrates are chordates that have a backbone 

    CONCEPT 34.3 Gnathostomes are vertebrates that have jaws 

    CONCEPT 34.4 Tetrapods are gnathostomes that have limbs 

    CONCEPT 34.5 Amniotes are tetrapods that have a terrestrially adapted egg 

    CONCEPT 34.6 Mammals are amniotes that have hair and produce milk 

    CONCEPT 34.7 Humans are mammals that have a large brain and bipedal locomotion 

    UNIT 6 PLANT FORM AND FUNCTION 

    35 Vascular Plant Structure, Growth, and Development

    Are Plants Computers?

    CONCEPT 35.1 Plants have a hierarchical organization consisting of organs, tissues, and cells 

    CONCEPT 35.2 Different meristems generate new cells for primary and secondary growth 

    CONCEPT 35.3 Primary growth lengthens roots and shoots 

    CONCEPT 35.4 Secondary growth increases the diameter of stems and roots in woody plants 

    CONCEPT 35.5 Growth, morphogenesis, and cell differentiation produce the plant body 

    36 Resource Acquisition and Transport in Vascular Plants

    A Whole Lot of Shaking Going On

    CONCEPT 36.1 Adaptations for acquiring resources were key steps in the evolution of vascular plants 

    CONCEPT 36.2 Different mechanisms transport substances over short or long distances 

    CONCEPT 36.3 Transpiration drives the transport of water and minerals from roots to shoots via the xylem 

    CONCEPT 36.4 The rate of transpiration is regulated by stomata 

    CONCEPT 36.5 Sugars are transported from sources to sinks via the phloem 

    CONCEPT 36.6 The symplast is highly dynamic 

    37 Soil and Plant Nutrition

    The Corkscrew Carnivore

    CONCEPT 37.1 Soil contains a living, complex ecosystem

    CONCEPT 37.2 Plant roots absorb essential elements from the soil

    CONCEPT 37.3 Plant nutrition often involves relationships with other organisms 

    38 Angiosperm Reproduction and Biotechnology

    Flowers of Deceit

    CONCEPT 38.1 Flowers, double fertilization, and fruits are key features of the angiosperm life cycle 

    CONCEPT 38.2 Flowering plants reproduce sexually, asexually, or both 

    CONCEPT 38.3 People modify crops by breeding and genetic engineering 

    39 Plant Responses to Internal and External Signals

    Stimuli and a Stationary Life

    CONCEPT 39.1 Signal transduction pathways link signal reception to response 

    CONCEPT 39.2 Plant hormones help coordinate growth, development, and responses to stimuli 

    CONCEPT 39.3 Responses to light are critical for plant success 

    CONCEPT 39.4 Plants respond to a wide variety of stimuli other than light

    CONCEPT 39.5 Plants respond to attacks by pathogens and herbivores 

    UNIT 7 ANIMAL FORM AND FUNCTION 

    40 Basic Principles of Animal Form and Function

    Diverse Forms, Common Challenges

    CONCEPT 40.1 Animal form and function are correlated at all levels of organization 

    CONCEPT 40.2 Feedback control maintains the internal environment in many animals 

    CONCEPT 40.3 Homeostatic processes for thermoregulation involve form, function, and behavior 

    CONCEPT 40.4 Energy requirements are related to animal size, activity, and environment 

    41 Animal Nutrition

    CONCEPT 41.1 An animal&rsquos diet must supply chemical energy, organic building blocks, and essential nutrients 

    CONCEPT 41.2 Food processing involves ingestion, digestion, absorption, and elimination 

    CONCEPT 41.3 Organs specialized for sequential stages of food processing form the mammalian digestive system 

    CONCEPT 41.4 Evolutionary adaptations of vertebrate digestive systems correlate with diet 

    CONCEPT 41.5 Feedback circuits regulate digestion, energy storage, and appetite 

    42 Circulation and Gas Exchange

    CONCEPT 42.1 Circulatory systems link exchange surfaces with cells throughout the body 

    CONCEPT 42.2 Coordinated cycles of heart contraction drive double circulation in mammals 

    CONCEPT 42.3 Patterns of blood pressure and flow reflect the structure and arrangement of blood vessels 

    CONCEPT 42.4 Blood components function in exchange, transport, and defense 

    CONCEPT 42.5 Gas exchange occurs across specialized respiratory surfaces 

    CONCEPT 42.6 Breathing ventilates the lungs 

    CONCEPT 42.7 Adaptations for gas exchange include pigments that bind and transport gases 

    Recognition and Response

    CONCEPT 43.1 In innate immunity, recognition and response rely on traits common to groups of pathogens 

    CONCEPT 43.2 In adaptive immunity, receptors provide pathogen-specific recognition 

    CONCEPT 43.3 Adaptive immunity defends against infection of body fluids and body cells 

    CONCEPT 43.4 Disruptions in immune system function can elicit or exacerbate disease 

    44 Osmoregulation and Excretion

    A Balancing Act

    CONCEPT 44.1 Osmoregulation balances the uptake and loss of water and solutes 

    CONCEPT 44.2 An animal&rsquos nitrogenous wastes reflect its phylogeny and habitat 

    CONCEPT 44.3 Diverse excretory systems are variations on a tubular theme 

    CONCEPT 44.4 The nephron is organized for stepwise processing of blood filtrate 

    CONCEPT 44.5 Hormonal circuits link kidney function, water balance, and blood pressure 

    45 Hormones and the Endocrine System

    The Body&rsquos Long-Distance Regulators 

    CONCEPT 45.1 Hormones and other signaling molecules bind to target receptors, triggering specific response pathways 

    CONCEPT 45.2 Feedback regulation and coordination with the nervous system are common in hormone pathways 

    CONCEPT 45.3 Endocrine glands respond to diverse stimuli in regulating homeostasis, development, and behavior 

    46 Animal Reproduction

    Let Me Count the Ways

    CONCEPT 46.1 Both asexual and sexual reproduction occur in the animal kingdom 

    CONCEPT 46.2 Fertilization depends on mechanisms that bring together sperm and eggs of the same species 

    CONCEPT 46.3 Reproductive organs produce and transport gametes 

    CONCEPT 46.4 The interplay of tropic and sex hormones regulates mammalian reproduction 

    CONCEPT 46.5 In placental mammals, an embryo develops fully within the mother&rsquos uterus 

    47 Animal Development

    A Body-Building Plan

    CONCEPT 47.1 Fertilization and cleavage initiate embryonic development 

    CONCEPT 47.2 Morphogenesis in animals involves specific changes in cell shape, position, and survival 

    CONCEPT 47.3 Cytoplasmic determinants and inductive signals regulate cell fate

    48 Neurons, Synapses, and Signaling

    Lines of Communication

    CONCEPT 48.1 Neuron structure and organization reflect function in information transfer 

    CONCEPT 48.2 Ion pumps and ion channels establish the resting potential of a neuron 

    CONCEPT 48.3 Action potentials are the signals conducted by axons 

    CONCEPT 48.4 Neurons communicate with other cells at synapses 

    49 Nervous Systems

    Command and Control Center

    CONCEPT 49.1 Nervous systems consist of circuits of neurons and supporting cells 

    CONCEPT 49.2 The vertebrate brain is regionally specialized 

    CONCEPT 49.3 The cerebral cortex controls voluntary movement and cognitive functions 

    CONCEPT 49.4 Changes in synaptic connections underlie memory and learning 

    CONCEPT 49.5 Many nervous system disorders can be explained in molecular terms 

    50 Sensory and Motor Mechanisms

    Sense and Sensibility

    CONCEPT 50.1 Sensory receptors transduce stimulus energy and transmit signals to the central nervous system 

    CONCEPT 50.2 In hearing and equilibrium, mechanoreceptors detect moving fluid or settling particles 

    CONCEPT 50.3 The diverse visual receptors of animals depend on light-absorbing pigments

    CONCEPT 50.4 The senses of taste and smell rely on similar sets of sensory receptors 

    CONCEPT 50.5 The physical interaction of protein filaments is required for muscle function

    CONCEPT 50.6 Skeletal systems transform muscle contraction into locomotion 

    51 Animal Behavior

    The How and Why of Animal Activity

    CONCEPT 51.1 Discrete sensory inputs can stimulate both simple and complex behaviors 

    CONCEPT 51.2 Learning establishes specific links between experience and behavior 

    CONCEPT 51.3 Selection for individual survival and reproductive success can explain diverse behaviors 

    CONCEPT 51.4 Genetic analyses and the concept of inclusive fitness provide a basis for studying the evolution of behavior 

    52 An Introduction to Ecology and the Biosphere

    Discovering Ecology

    CONCEPT 52.1 Earth&rsquos climate varies by latitude and season and is changing rapidly 

    CONCEPT 52.2 The distribution of terrestrial biomes is controlled by climate and disturbance 

    CONCEPT 52.3 Aquatic biomes are diverse and dynamic systems that cover most of Earth

    CONCEPT 52.4 Interactions between organisms and the environment limit the distribution of species 

    CONCEPT 52.5Ecological change and evolution affect one another over long and short periods of time

    53 Population Ecology

    CONCEPT 53.1 Biotic and abiotic factors affectpopulation density, dispersion, and demographics 

    CONCEPT 53.2 The exponential model describes population growth in an idealized, unlimited environment 

    CONCEPT 53.3 The logistic model describes how a population grows more slowly as it nears its carrying capacity 

    CONCEPT 53.4 Life history traits are products of natural selection 

    CONCEPT 53.5 Density-dependent factors regulate population growth

    CONCEPT 53.6 The human population is no longer growing exponentially but is still increasing rapidly 

    54 Community Ecology

    Communities in Motion

    CONCEPT 54.1 Community interactions are classified by whether they help, harm, or have no effect on the species involved 

    CONCEPT 54.2 Diversity and trophic structure characterize biological communities 

    CONCEPT 54.3 Disturbance influences species diversity and composition 

    CONCEPT 54.4 Biogeographic factors affect community diversity 

    CONCEPT 54.5 Pathogens alter community structure locally and globally 

    55 Ecosystems and Restoration Ecology

    Transformed to Tundra

    CONCEPT 55.1 Physical laws govern energy flow and chemical cycling in ecosystems 

    CONCEPT 55.2 Energy and other limiting factors control primary production in ecosystems 

    CONCEPT 55.3 Energy transfer between trophic levels is typically only 10% efficient 

    CONCEPT 55.4 Biological and geochemical processes cycle nutrients and water in ecosystems 

    CONCEPT 55.5 Restoration ecologists return degraded ecosystems to a more natural state 

    56 Conservation Biology and Global Change

    Psychedelic Treasure

    CONCEPT 56.1 Human activities threaten Earth&rsquos biodiversity 

    CONCEPT 56.2 Population conservation focuses on population size, genetic diversity, and critical habitat 

    CONCEPT 56.3 Landscape and regional conservation help sustain biodiversity 

    CONCEPT 56.4 Earth is changing rapidly as a result of human actions

    CONCEPT 56.5 Sustainable development can improve human lives while conserving biodiversity 


    Watch the video: 4 1 Ομοιόσταση (September 2022).


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