No Time to Age: Uncoupling Aging from Chronological Time

Multicellular life evolved from simple unicellular organisms that could replicate indefinitely, being essentially ageless. At this point, life split into two fundamentally different cell types: the immortal germline representing an unbroken lineage of cell division with no intrinsic endpoint and the mortal soma, which ages and dies. In this review, we describe the germline as clock-free and the soma as clock-bound and discuss aging with respect to three DNA-based cellular clocks (telomeric, DNA methylation, and transposable element). The ticking of these clocks corresponds to the stepwise progressive limitation of growth and regeneration of somatic cells that we term somatic restriction. Somatic restriction acts in opposition to strategies that ensure continued germline replication and regeneration. We thus consider the plasticity of aging as a process not fixed to the pace of chronological time but one that can speed up or slow down depending on the rate of intrinsic cellular clocks. We further describe how germline factor reprogramming might be used to slow the rate of aging and potentially reverse it by causing the clocks to tick backward. Therefore, reprogramming may eventually lead to therapeutic strategies to treat degenerative diseases by altering aging itself, the one condition common to us all.

Toward a unified theory of aging and regeneration

Growing evidence supports the antagonistic pleiotropy theory of mammalian aging. Accordingly, changes in gene expression following the pluripotency transition, and subsequent transitions such as the embryonic–fetal transition, while providing tumor suppressive and antiviral survival benefits also result in a loss of regenerative potential leading to age-related fibrosis and degenerative diseases. However, reprogramming somatic cells to pluripotency demonstrates the possibility of restoring telomerase and embryonic regeneration pathways and thus reversing the age-related decline in regenerative capacity. A unified model of aging and loss of regenerative potential is emerging that may ultimately be translated into new therapeutic approaches for establishing induced tissue regeneration and modulation of the embryo-onco phenotype of cancer.

Clonal derivation of white and brown adipocyte progenitor cell lines from human pluripotent stem cells

BACKGROUND

The role of brown fat in non-shivering thermogenesis and the discovery of brown fat depots in adult humans has made it the subject of intense research interest. A renewable source of brown adipocyte (BA) progenitors would be highly valuable for research and therapy. Directed differentiation of human pluripotent stem (hPS) cells to white or brown adipocytes is limited by lack of cell purity and scalability. Here we describe an alternative approach involving the identification of clonal self-renewing human embryonic progenitor (hEP) cell lines following partial hPS cell differentiation and selection of scalable clones.

METHODS

We screened a diverse panel of hPS cell-derived clonal hEP cell lines for adipocyte markers following growth in adipocyte differentiation medium. The transcriptome of the human hES-derived clonal embryonic progenitor cell lines E3, C4ELS5.1, NP88, and NP110 representing three class of definitive adipocyte progenitors were compared to the relatively non-adipogenic line E85 and adult-derived BAT and SAT-derived cells using gene expression microarrays, RT-qPCR, metabolic analysis and immunocytochemistry. Differentiation conditions were optimized for maximal UCP1 expression.

RESULTS

Many of the differentiated hEP cell lines expressed the adipocyte marker, FAPB4, but only a small subset expressed definitive adipocyte markers including brown adipocyte marker, UCP1. Class I cells (i.e., E3) expressed CITED1, ADIPOQ, and C19orf80 but little to no UCP1. Class II (i.e., C4ELS5.1) expressed CITED1 and UCP1 but little ADIPOQ and LIPASIN. Class III (i.e., NP88, NP110) expressed CITED1, ADIPOQ, C19orf80, and UCP1 in a similar manner as fetal BAT-derived (fBAT) cells. Differentiated NP88 and NP110 lines were closest to fBAT cells morphologically in adiponectin and uncoupling protein expression. But they were more metabolically active than fBAT cells, had higher levels of 3-hydroxybutyrate, and lacked expression of fetal/adult marker, COX7A1. The hEP BA progenitor lines were scalable to 17 passages without loss of differentiation capacity and could be readily rederived.

CONCLUSIONS

Taken together, these data demonstrate that self-renewing adipocyte progenitor cells can be derived from hES cells and that they are functionally like BAT cells but with unique properties that might be advantageous for basic research and for development of cell-based treatments for metabolic diseases.

Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells

Here we present the application of deep neural network (DNN) ensembles trained on transcriptomic data to identify the novel markers associated with the mammalian embryonic-fetal transition (EFT). Molecular markers of this process could provide important insights into regulatory mechanisms of normal development, epimorphic tissue regeneration and cancer. Subsequent analysis of the most significant genes behind the DNNs classifier on an independent dataset of adult-derived and human embryonic stem cell (hESC)-derived progenitor cell lines led to the identification of COX7A1 gene as a potential EFT marker. COX7A1, encoding a cytochrome C oxidase subunit, was up-regulated in post-EFT murine and human cells including adult stem cells, but was not expressed in pre-EFT pluripotent embryonic stem cells or their in vitro-derived progeny. COX7A1 expression level was observed to be undetectable or low in multiple sarcoma and carcinoma cell lines as compared to normal controls. The knockout of the gene in mice led to a marked glycolytic shift reminiscent of the Warburg effect that occurs in cancer cells. The DNN approach facilitated the elucidation of a potentially new biomarker of cancer and pre-EFT cells, the embryo-onco phenotype, which may potentially be used as a target for controlling the embryonic-fetal transition.

Transcriptome-Wide Analyses of Human Neonatal Articular Cartilage and Human Mesenchymal Stem Cell-Derived Cartilage Provide a New Molecular Target for Evaluating Engineered Cartilage

Cellular differentiation comprises a progressive, multistep program that drives cells to fabricate a tissue with specific and site distinctive structural and functional properties. Cartilage constitutes one of the potential differentiation lineages that mesenchymal stem cells (MSCs) can follow under the guidance of specific bioactive agents. Single agents such as transforming growth factor beta (TGF-β) and bone morphogenetic protein 2 in unchanging culture conditions have been historically used to induce in vitro chondrogenic differentiation of MSCs. Despite the expression of traditional chondrogenic biomarkers such as type II collagen and aggrecan, the resulting tissue represents a transient cartilage rather than an in vivo articular cartilage (AC), differing significantly in structure, chemical composition, cellular phenotypes, and mechanical properties. Moreover, there have been no comprehensive, multicomponent parameters to define high-quality and functional engineered hyaline AC. To address these issues, we have taken an innovative approach based on the molecular interrogation of human neonatal articular cartilage (hNAC), dissected from the knees of 1-month-old cadaveric specimens. Subsequently, we compared hNAC-specific transcriptional regulatory elements and differentially expressed genes with adult human bone marrow (hBM) MSC-derived three-dimensional cartilage structures formed in vitro. Using microarray analysis, the transcriptome of hNAC was found to be globally distinct from the transient, cartilage-like tissue formed by hBM-MSCs in vitro. Specifically, over 500 genes that are highly expressed in hNAC were not expressed at any time point during in vitro human MSC chondrogenesis. The analysis also showed that the differences were less variant during the initial stages (first 7 days) of the in vitro chondrogenic differentiation program. These observations suggest that the endochondral fate of hBM-MSC-derived cartilage may be rerouted at earlier stages of the TGF-β-stimulated chondrogenic differentiation program. Based on these analyses, several key molecular differences (transcription factors and coded cartilage-related proteins) were identified in hNAC that will be useful as molecular inductors and identifiers of the in vivo AC phenotype. Our findings provide a new gold standard of a molecularly defined AC phenotype that will serve as a platform to generate novel approaches for AC tissue engineering.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

Design of efficient computational workflows for in silico drug repurposing

Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.

Evaluating the genomic and sequence integrity of human ES cell lines; comparison to normal genomes

Copy number variation (CNV) is a common chromosomal alteration that can occur during in vitro cultivation of human cells and can be accompanied by the accumulation of mutations in coding region sequences. We describe here a systematic application of current molecular technologies to provide a detailed understanding of genomic and sequence profiles of human embryonic stem cell (hESC) lines that were derived under GMP-compliant conditions. We first examined the overall chromosomal integrity using cytogenetic techniques to determine chromosome count, and to detect the presence of cytogenetically aberrant cells in the culture (mosaicism). Assays of copy number variation, using both microarray and sequence-based analyses, provide a detailed view genomic variation in these lines and shows that in early passage cultures of these lines, the size range and distribution of CNVs are entirely consistent with those seen in the genomes of normal individuals. Similarly, genome sequencing shows variation within these lines that is completely within the range seen in normal genomes. Important gene classes, such as tumor suppressors and genetic disease genes, do not display overtly disruptive mutations that could affect the overall safety of cell-based therapeutics. Complete sequence also allows the analysis of important transplantation antigens, such as ABO and HLA types. The combined application of cytogenetic and molecular technologies provides a detailed understanding of genomic and sequence profiles of GMP produced ES lines for potential use as therapeutic agents.

Accurate sequencing by hybridization for DNA diagnostics and individual genomics

Medical DNA diagnostics will increasingly rely on an accurate and inexpensive identification of mutations that affect the function of a gene. To validate diagnostic sequencing by hybridization (SBH), a number of p53 samples were analyzed with the complete set of 8192 noncomplementary 7-mer oligonucleotides. In four repeated, blind experiments we accurately sequenced 1.1 kb per each of 12 homozygote and heterozygote samples possessing base substitutions, insertions, and deletions. This SBH variant offers a high throughput platform to inexpensively sequence individual gene or pathogen genome samples within the clinical laboratory setting.

Gene-representing cDNA clusters defined by hybridization of 57,419 clones from infant brain libraries with short oligonucleotide probes

Diverse biochemical and computational procedures and facilities have been developed to hybridize thousands of DNA clones with short oligonucleotide probes and subsequently to extract valuable genetic information. This technology has been applied to 73,536 cDNA clones from infant brain libraries. By a mutual comparison of 57,419 samples that were successfully scored by 200-320 probes, 19,726 genes have been identified and sorted by their expression levels. The data indicate that an additional 20,000 or more genes may be expressed in the infant brain. Representative clones of the found genes create a valuable resource for complete sequencing and functional studies of many novel genes. These results demonstrate the unique capacity of hybridization technology to identify weakly transcribed genes and to study gene networks involved in organismal development, aging, or tumorigenesis by monitoring the expression of every gene in related tissues, whether known or still undiscovered.

DNA sequence determination by hybridization: a strategy for efficient large-scale sequencing

The concept of sequencing by hybridization (SBH) makes use of an array of all possible n-nucleotide oligomers (n-mers) to identify n-mers present in an unknown DNA sequence. Computational approaches can then be used to assemble the complete sequence. As a validation of this concept, the sequences of three DNA fragments, 343 base pairs in length, were determined with octamer oligonucleotides. Possible applications of SBH include physical mapping (ordering) of overlapping DNA clones, sequence checking, DNA fingerprinting comparisons of normal and disease-causing genes, and the identification of DNA fragments with particular sequence motifs in complementary DNA and genomic libraries. The SBH techniques may accelerate the mapping and sequencing phases of the human genome project.

Sequencing by hybridization: towards an automated sequencing of one million M13 clones arrayed on membranes

An immediately applicable variant of the sequencing by hybridization (SBH) method is under development with the capacity to determine up to 100 million base pairs per year. The proposed method comprises six steps: (i) arraying genomic or cDNA M13 clones in 864-well plates (wells of 2 mm); (ii) preparation of DNA samples for spotting by growth of the M13 clones or by polymerase chain reaction (PCR) of the inserts using standard 96-well plates, or plates having as many as 864 correspondingly smaller wells; (iii) robotic spotting of 13,824 samples on an 8 x 12 cm nylon membrane, or correspondingly more, on up to 6 times larger filters, by offset printing with a 96 or 864 0.4 mm pin device; (iv) hybridization of dotted samples with 200-2000 32P-labeled probes comprising 16-256 10-mers having a common 8-mer, 7-mer, or 6-mer in the middle (20 probes per day, each hybridized with 250,000 dots); (v) scoring hybridization signals of 5 million sample-probe pairs per day using storage phosphor plates; and (vi) computing clone order and partial-to-complete DNA sequences using various heuristic algorithms. Genome sequencing based on a combination of this method and gel sequencing techniques may be significantly more economical than gel methods alone.

DNA sequencing by hybridization: 100 bases read by a non-gel-based method

Determination of the sequences of human and other complex genomes requires much faster and less expensive sequencing processes than the methods in use today. Sequencing by hybridization is potentially such a process. In this paper we present hybridization data sufficient to accurately read a known sequence of 100 base pairs. In independent reactions, octamer and nonamer oligonucleotides derived from the sequence hybridized more strongly to this DNA than to controls. The 93 consecutive overlapping probes were derived from a 100-base-pair segment of test DNA and additional probes were generated by incorporation of a noncomplementary base at one of the ends of 12 of the basic probes. These 12 additional probes also had a full-match target in one of the control DNAs. The test and one of five control DNAs spotted on nylon filters were hybridized with 83 octamers and 22 nonamers under low-temperature conditions. A stronger signal in DNA containing a full-match target compared to DNA with only mismatched targets was obtained with all 105 probes. In 3 cases (2.9%), the difference of signals was not significant (less than 2-fold) due to inefficient hybridization and the consequently higher influence of background. The hybridization pattern obtained enabled us to resequence the 100 base pairs by applying an algorithm that tolerates an error rate much higher than was observed in the experiment. With this result, the technological components of large-scale DNA sequencing using the sequencing by hybridization method are in place.

An algorithm for the DNA sequence generation from k-tuple word contents of the minimal number of random fragments

An algorithm is described for generation of the long sequence written in a four letter alphabet from the constituent k-tuple words in the minimal number of separate, randomly defined fragments of the starting sequence. It is primarily intended for use in sequencing by hybridization (SBH) process- a potential method for sequencing human genome DNA (Drmanac et al., Genomics 4, pp. 114-128, 1989). The algorithm is based on the formerly defined rules and informative entities of the linear sequence. The algorithm requires neither knowledge on the number of appearances of a given k-tuple in sequence fragments, nor the information on which k-tuple words are on the ends of a fragment. It operates with the mixed content of k-tuples of the various lengths. The concept of the algorithm enables operations with the k-tuple sets containing false positive and false negative k-tuples. The content of the false k-tuples primarily affects the completeness of the generated sequence, and its correctness in the specific cases only. The algorithm can be used for the optimization of SBH parameters in the simulation experiments, as well as for the sequence generation in the real SBH experiments on the genomic DNA.

Reliable hybridization of oligonucleotides as short as six nucleotides

Although there are many new applications for hybridizing short, synthetic oligonucleotide probes to DNA, such applications have not included determining unknown sequences of DNA. The lack of clear discrimination in hybridization of oligo probes shorter than 11 nucleotides and the lack of a theoretical understanding of factors influencing hybridization of short oligos have hampered the development of their use. We have found conditions for reliable hybridization of oligonucleotides as short as seven nucleotides to cloned DNA or to oligonucleotides attached to filters. Low-temperature hybridization and washing conditions, in contrast to the high stringency conditions currently used in hybridization experiments, have the potential for allowing the simple use of all oligos of six nucleotides or longer in meaningful hybridizations. We also present the hybridization discrimination theory that provides the conceptual framework for understanding these results.

Sequencing of megabase plus DNA by hybridization: theory of the method

A mismatch-free hybridization of oligonucleotides containing from 11 to 20 monomers to unknown DNA represents, in essence, a sequencing of a complementary target. Realizing this, we have used probability calculations and, in part, computer simulations to estimate the types and numbers of oligonucleotides that would have to be synthesized in order to sequence a megabase plus segment of DNA. We estimate that 95,000 specific mixes of 11-mers, mainly of the 5'(A,T,C,G)(A,T,C,G)N8(A,T,C,G)3' type, hybridized consecutively to dot blots of cloned genomic DNA fragments would provide primary data for the sequence assembly. An optimal mixture of representative libraries in M13 vector, having inserts of (i) 7 kb, (ii) 0.5 kb genomic fragments randomly ligated in up to 10-kb inserts, and (iii) tandem "jumping" fragments 100 kb apart in the genome, will be needed. To sequence each million base pairs of DNA, one would need hybridization data from about 2100 separate hybridization sample dots. Inevitable gaps and uncertainties in alignment of sequenced fragments arising from nonrandom or repetitive sequence organization of complex genomes and difficulties in cloning "poisonous" sequences in Escherichia coli, inherent to large sequencing by any method, have been considered and minimized by choice of libraries and number of subclones used for hybridization. Because it is based on simpler biochemical procedures, our method is inherently easier to automate than existing sequencing methods. The sequence can be derived from simple primary data only by extensive computing. Phased experimental tests and computer simulations increasing in complexity are needed before accurate estimates can be made in terms of cost and speed of sequencing by the new approach. Nevertheless, sequencing by hybridization should show advantages over existing methods because of the inherent redundancy and parallelism in its data gathering.