Beyond the genome

FORUM Epigenomics. Roadmap for regulation. Diseases mapped

My suggestion for today. Have a look at the papers in Nature on epigenome, and at the following figure:

The Roadmap Epigenomics Project has produced reference epigenomes that provide information on key functional elements controlling gene expression in 127 human tissues and cell types, and encompassing embryonic and adult tissues, from healthy individuals and those with disease. a, Many of the adult tissues investigated were broken down by cell type or region — blood into several types of immune cell, for instance, and the brain into regions including the hippocampus and dorsolateral prefrontal cortex. Tissue samples and cells were subjected to a range of epigenomic analyses, along with genome sequencing and genome-wide association studies (GWAS). b, Embryonic stem (ES) cells, which are taken from the embryo at the 'blastocyst' stage and can give rise to almost every cell type in the body, were used to analyse, for example, the differentiation of stem cells into different neuronal lineages. The ES-cell-derived cell lines underwent the same epigenomic analyses as the tissue samples.

The key article, here.Tissues and cell types profiled:

For decades, biomedical science has focused on ways of identifying the genes that contribute to a particular trait, or phenotype. Approaches such as genome-wide association studies (GWAS) identify locations in thhuman genome at which variations in DNA sequence are linked to specific phenotypes, but if the variant is located in a region of DNA that does not encode a protein, such studies rarely provide insights into the regulatory mechanisms underlying the association. In these cases, comprehensive epigenomic analyses can provide the missing link between genomic variation and cellular phenotype.

If this is so, why are governments reluctant to introduce a ban on genetic tests with spurious associations between genome and diseases?




PS. Manel Esteller in DM.

Market access for expensive therapies (or how to overcome prices) (2)

 Gene and Cell Therapies-Market Access and Funding

Contents:

Chapter 1        Introduction to cell and gene therapies concepts and definitions in US and EU

Chapter 2        cell and gene therapies: genuine products and potential for dramatic value

Chapter 3        cell and gene therapies: Regulatory aspects in US and EU

Chapter 4        the need for new HTA reference case for cell and gene therapies

Chapter 5        How to mitigate cell and gene therapies uncertainties and HTA risk adverse attitude?

Chapter 6        Cell and gene therapies funding: challenges and solutions for patients’ access

Chapter 7        Conclusion

A disease-producing organism

Disease Selection. The Way Disease Changed the World


Understanding human life is a great undertaking. After all these years the origins of our cells are not so clear. But let me quote a recent book and its suggested approach:

Evolutionary biologists have looked for some time for a suitable prokaryotic cell that when engulfed by another would form the nucleus of the nascent eukaryotic cell, but none has been identified that matches all the required criteria. However, Luis Villarreal, working with viruses, has come to the astounding conclusion that the primitive cell nucleus could have originated from a complex virus. The vaccinia virus, for example, seems to have all the same mechanisms that are required by a eukaryotic cell nucleus. The virus that formed the nucleus brought with it all the basic genes – thought to number about 324 – that are necessary to form the cell.
It requires a little time, and perhaps rereading of what has just been said, to realize that every cell in our bodies has a nucleus that was derived from a virus. We are the result of a very early disease process!

So not only is the nucleus of our cells derived from a virus but the mitochondria are from a parasitic bacterium. There can be no closer link between us and disease-producing organisms.

Canviant la funció de producció de les proves diagnòstiques

 Real-Time, Multiplexed SHERLOCK for in Vitro Diagnostics

Durant la pandèmia es va produir una innovació notable a la tecnologia de proves diagnòstiques. Va passar desapercebuda per alguns, però no pels que llegiu aquest blog. Es tracta d'utilitzar CRISPR que inicialment s'ha desenvolupat per a edició genètica, per a la detecció d'àcids nucleïcs en un sol pas, i per tant també de Sars-COV-2. La dificultat d'aplicació que tenia aquella prova, que va rebre el vist-i-plau de la FDA era que el procés no es podia automatitzar, i per tant calia amplificació prèvia. Ara acaba de publicar-se la prova definitiva que pot capgirar moltes coses, i que per tant pot canviar la funció de producció dels laboratoris. La troballa d'enzims termoestables ha estat la qüestió clau per a l'èxit de l'equip de Feng Zhang.

Cal dir que la companyia rival, Mammoth Biosciences, de Jennifer Doudna,  també té en marxa una prova similar: DETECTR BOOST.

I ara ja ha començat el procés de patentar enzims. Tant per una empresa com per l'altre. I això esdevé inadmisible si el que es pretén patentar la natura o variacions sobre la natura. Però ningú se n'està adonant , als USA ja s'ha fet tard, però a Europa encara hi som a temps.

N'estic segur que les patents, una vegada més, limitaran l'accés a aquesta gran innovació.

Cartier-Bresson

PS. Per si voleu saber com funciona DETECTR BOOST

PS. Per tal de comprendre l'abast del que signifiquen les proves diagnòstiques basades en CRISPR el millor és consultar un article de revisió com aquest. I la taula següent conté les dades bàsiques:

Characteristics of reported CRISPR-based diagnostics

From: CRISPR-based diagnostics

Name	Enzyme	Preamplification	Assay timea (min)	Sample preparation	Readout	Applications	LODc (mol l−1)	LODc (copies per ml)3	References
CRISPR type II
NASBACCb	Cas9	NASBA	120–360 (one pot)	Column-based or crude extraction	Colometry	Discrimination between African and American ZIKV	1.0 × 10–15	6.0 × 105	25
CRISPR–Chip	Cas9	–	15	Column-based	Electrochemical	Detection of gDNA from cell lines and DMD patients	2.3 × 10–15	1.4 × 106	45
CRISDA	Cas9 nickase	SDA	90	Column-based	Fluorescence	Detection of gDNA; breast-cancer-associated SNPs in cell lines	2.5 × 10–19	1.5 × 102	27
FLASH	Cas9	PCR	NS	Column-based	NGS	Detection of gDNA; antimicrobial resistance genes in clinical samples	1.9 × 10–18	1.1 × 103	71
CAS-EXPAR	Cas9	EXPAR	60	Chemical (phase separation)	Fluorescence	Sensing of methylated DNA; L. monocytogenes mRNA	8.2 × 10–19	4.9 × 102	91
Cas9nAR	Cas9 nickase	Strand-displacing DNA polymerase	60	Column-based	Fluorescence	Detection of bacteria (S. typhimurium, E. coli, M. smegmatis, S. erythraea); detection of KRAS SNPs in cell lines	1.7 × 10–19	1.0 × 102	111
CRISPR type V
DETECTR	Cas12a	RPA	10 (RPA) and 60–120 (CRISPR)	Crude extraction	Fluorescence	Detection of HPV16 and HPV18 in human samples	1.0 × 10–18	6.0 × 102	36
Cas14-DETECTR	Cas14 (Cas12f)	PCR	NS (PCR) and 120 (CRISPR)	Crude extraction	Fluorescence	Detection of HERC2 SNPs in human samples	n.s.	6.0 × 103	41
HOLMES	Cas12a	PCR	88 (PCR) and 15 (CRISPR)	Column-based	Fluorescence	SNP discrimination in cell lines and human samples; detection of viruses (PRV, JEV); virus-strain discrimination	1.0 × 10–17	6.6 × 103	37,38
CRISPR-materials	Cas12a	RPA	40 (RPA) and 240 (CRISPR)	Synthetic targets	Fluorescence or μPAD (visual and electronic)	Detection of EBOV synthetic RNA	1.0 × 10–17	6.6 × 103	79,80
CDetection	Cas12b	RPA	10 (RPA) and 60–180 (CRISPR)	Synthetic targets or crude extraction	Fluorescence	Detection of HPV16; human ABO blood genotyping; BRCA1 and TP53 SNPs	1.0 × 10–18	6.0 × 102	112
HOLMESv2	Cas12b	LAMP	40 (LAMP) and 35 (CRISPR) or 120 (one pot)	NS	Fluorescence	SNP discrimination in cell lines; RNA virus detection (JEV); human mRNA and circular RNA detection; DNA methylation	1.0 × 10–17	6.0 × 103	39
E-CRISPR	Cas12a	–	30–180	Synthetic targets (nucleic acids)	Electrochemical	Detection of viruses (HPV16, PB19) and protein (TGF-ß1)	5.0 × 10–11	3.0 × 1010	77
CRISPR type VI
–	Cas13	–	NS	NS	Fluorescence	Detection of human mRNA; detection of bacteriophage λ-RNA	1.0 × 10–12	6.0 × 108	30,31
SHERLOCK	Cas13	NASBA or RPA	132 (NASBA) or 120 (RPA) and 60–180 (CRISPR)	Column-based or crude extraction	Fluorescence	Detection of viruses (ZIKV, DENV) and bacteria (E. coli, K. pneumoniae, P. aeruginosa, M. tuberculosis, S. aureus); discrimination between virus strains; detection of SNPs	2.0 × 10–18	1.2 × 103	32
SHERLOCKv2b	Cas13	RPA	60 (RPA) and 60–180 (CRISPR) or 60–180 (one pot)	Column-based or crude extraction	Fluorescence or lateral flow	Detection of viruses (ZIKV, DENV) and bacteria (P. aeruginosa, S. aureus); discrimination between virus strains; detection of SNPs	8.0 × 10–21	4.8	22,34
SHINEb	Cas13	RPA	50 (one pot)	Crude extraction	Fluorescence or lateral flow	Detection of SARS-CoV-2	8.3 × 10–18	5.0 × 103	62
STOPCovidb	Cas12b	LAMP	60 (one pot)	Crude extraction	Fluorescence or lateral flow	Detection of SARS-CoV-2	3.3 × 10–18	2.0 × 103	63
CARMEN	Cas13	PCR or RPA	20 (RPA) and 180 (CRISPR)	Column-based	Fluorescence	Detection of 169 viruses; subtyping of influenza A strains; detection of HIV drug-resistant mutations	9 × 10–19	5.4 × 102	67
APC-Cas	Cas13	Allosteric-probe-initiated amplification with DNA polymerase	110 (APC) and 30 (CRISPR)	None	Fluorescence	Detection of S. enteritidis	One colony-forming unit	–	92
	Cas13	–	<240	Column-based	Electrochemical	Detection of microRNAs (miR-19b and miR-20a)	1 × 10–11	6.0 × 109	46
PECL-CRISPR	Cas13	EXPAR	30 (CRISPR), 30 (phosphorylation of pre-trigger), 30 (EXPAR)	Column-based	Electrochemiluminescence	Detection of microRNAs (miR-17, let‐7 family miRNAs)	1.0 × 10–15	6.0 × 105	78

1. NS, not specified; APC-Cas, allosteric probe-initiated catalysis and CRISPR-Cas13a system; BRCA1, breast cancer 1 gene; circRNA, circular RNA; Cas9nAR, Cas9 nickase-based amplification reaction; CRISDA, CRISPR–Cas9-triggered nicking endonuclease-mediated strand-displacement amplification; DENV, dengue virus; DMD, Duchenne muscular dystrophy; EBOV, Ebola virus; E. coli, Escherichia coli; HERC2, HECT and RLD domain containing E3 ubiquitin protein ligase 2 gene; HPV, human papillomavirus; JEV, Japanese encephalitis virus; K. pneumoniae, Klebsiella pneumoniae; KRAS, KRAS proto-oncogene GTPase; M. smegmatis, Mycobacterium smegmatis; M. tuberculosis, Mycobacterium tuberculosis; PECL, portable electrochemiluminescence chip; P. aeruginosa, Pseudomonas aeruginosa; PB19, parvovirus B19; PRV, pseudorabies virus; S. erythraea, Saccharopolyspora erythraea; S. aureus, Staphylococcus aureus; S. enteritidis, Salmonella enteritidis; S. typhimurium, Salmonella typhimurium; TP53, tumour protein P53 gene.
2. aAssay time indicates the approximate incubation time most frequently used in the referred study (different assay times can be reported, depending on the intended sensitivity and the readout).
3. bPOC compatibility indicates whether the entire assay as reported—including sample preparation (that is, crude extraction) and readout—can be performed at POC or in the field with minimal equipment.
4. cLimits of detection cannot always be directly compared across studies, in particular because some studies did not report how the LOD was determined, or reported the target concentration either in the transport media of the sample or in the final reaction. The LODs shown here reflect the optimal LODs reported. In general, LODs depend on the type of input material (raw or synthetic), type of readout and incubation time.