Feeds:
Posts
Comments

Archive for the ‘Cell Biology, Signaling & Cell Circuits’ Category

New Mutant KRAS Inhibitors Are Showing Promise in Cancer Clinical Trials: Hope For the Once ‘Undruggable’ Target, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

New Mutant KRAS Inhibitors Are Showing Promise in Cancer Clinical Trials: Hope For the Once ‘Undruggable’ Target

Curator: Stephen J. Williams, Ph.D.

UPDATED 08/31/2026

From the FDA: https://www.fda.gov/news-events/press-announcements/fda-approves-first-class-targeted-therapy-metastatic-pancreatic-cancer 

Press release from FDA:  FDA approves RAS inhibitor for metastatic pancreatic cancer, a cancer subtype with few successful therapies.  Initial clinical results were very positive.

FDA Approves First in Class Targeted Therapy for Metastatic Pancreatic Cancer

For Immediate Release:

The U.S. Food and Drug Administration today approved Rasonque (daraxonrasib), a RAS inhibitor for the most common form of pancreatic cancer—delivering a new treatment option to patients with advanced pancreatic cancer months ahead of schedule. 

This action demonstrates the agency’s commitment to moving with urgency, reducing unnecessary delays and advancing innovative treatments that can make a meaningful difference in the lives of American patients and their families.

Rasonque, a tablet taken once daily, targets multiple forms of a protein called RAS, a key driver of tumor growth in most patients with pancreatic adenocarcinoma, which arises from cells lining the ducts of the pancreas.  

“Today’s approval provides a critical new option for patients facing an extraordinarily difficult and historically hard-to-treat cancer. It is our fundamental duty to deliver more cures and meaningful treatments to patients as quickly as possible,” said Acting FDA Commissioner Kyle Diamantas, J.D. “I am immensely proud of the dedicated FDA scientists whose fast, thorough review and relentless commitment made this groundbreaking milestone a reality.”

The approval is for the treatment of adults with metastatic pancreatic adenocarcinoma who have received at least one prior systemic therapy or are not candidates for multiagent systemic therapy.

Approximately 90% to 95% of the 67,000 new cases of pancreatic cancer diagnosed in the United States each year are pancreatic adenocarcinoma, according to the National Cancer Institute. Despite representing roughly 3.2% of all cancer diagnoses, pancreatic adenocarcinoma accounts for a disproportionately high share of cancer deaths, owing to its typically late detection, aggressive disease course, and historically limited treatment options.

In a randomized, open-label, multicenter clinical trial involving 500 adults with previously treated metastatic pancreatic adenocarcinoma, Rasonque improved median overall survival to 13.2 months compared to 6.7 months for standard chemotherapy.

“This drug showed unprecedented results in an area of high unmet need,” said Angelo de Claro, M.D., director of the FDA’s Oncology Center of Excellence. “The approval was granted 6.5 months before the user fee deadline, demonstrating the FDA’s commitment to accelerating the approval of new cancer treatments for patients with serious and life-threatening conditions.”

The FDA granted Rasonque Breakthrough Therapy and Orphan Drug designations. Rasonque received Priority Review for this indication. The application was also reviewed under the Commissioner’s National Priority Voucher pilot program, which is intended to help accelerate the review of therapies that address national public health priorities.

UPDATED 07/12/2026

From: KIRIA Advisory Partners and Sadhna Bokhiria 

🔵 Roche reported that its investigational KRAS G12C inhibitor, divarasib, significantly improved both overall survival (OS) and progression-free survival (PFS) compared with:
⤷ Lumakras® (sotorasib) from Amgen

⤷ Krazati® (adagrasib) from Bristol Myers Squibb

🔵 The Phase 3 Krascendo-1 trial enrolled patients with previously treated KRAS G12C-mutated non-small cell lung cancer (NSCLC).

🔵 Roche reported no new safety findings of concern, with most treatment-related side effects described as manageable and reversible.

🔵 Detailed efficacy data have not yet been released and will be presented at a future medical meeting.

The KRAS G12C market has evolved rapidly since the first approval in 2021.

Until now, Amgen and Bristol Myers Squibb have defined this treatment landscape.

Roche’s positive head-to-head Phase 3 results suggest the competitive landscape may be expanding.

The next question isn’t simply whether divarasib works.

💡 It is how much better it performs once the full clinical dataset is presented, and what that could mean for physician adoption, future treatment guidelines, and competition in targeted lung cancer.

From: PharmaDive 

Source: https://www.biopharmadive.com/news/roche-kras-lung-cancer-data-head-to-head/824344/ 

Roche KRAS drug succeeds in head-to-head lung cancer study

The Phase 3 trial compared Roche’s divarasib with treatments sold by Amgen and Bristol Myers in patients with non-small cell lung cancer.

Exterior view of the building housing the headquarters of Roche France, French subsidiary of the Swiss pharmaceutical group F. Hoffmann-La Roche
Exterior view of the building housing the headquarters of Roche France, French subsidiary of the Swiss pharmaceutical group F. Hoffmann-La Roche, Jan 15, 2024. The upped its customer analytics game by reengineering its data strategy. Getty Images

UPDATED 10/2/2021

A Newly Identified Mutant RAS G12C  GTPase Activating Protein (GAP) may lead to discovery of new class of RAS inhibitors 

From the journal Science in Filling in the GAPs in understanding RAS: A newly identified regulator increases the efficacy of a new class of targeted anti-RAS drugs.

Source:

SCIENCE•8 Oct 2021•Vol 374, Issue 6564•pp. 152-153•DOI: 10.1126/science.abl3639

    According to canonical thinking, mutationally activated RAS (constitutive activation) is insensitive to GTPase-activating proteins (GAPs).  GAPs accelerated the conversion of GTP bound to RAS to GDP, a critical step in the inactivation of RAS signaling cycle.  However a small molecule has just been FDA approved, sotorasib, in cancer cells by binding only the GDP bound KRAS G12C mutant. A few non small cell lung cancers (NSCLC) are resistant to another such inhibitor, adagrasib.  In the same Science issue, another paper by Li et. al. explains the potential that a GAP, RGS3, may play in this conundrum and demonstrates that certain other inhibitors of the RAS cycle, mainly the GEF (guanine exchange factor) SOS1, in combination with MEK inhibitors may circumvent this resistance.

Inhibitors of mutant KRAS
The KRASG12C (Gly12→Cys) mutant is refractory to canonical GAPs, p120 RASGAP and neurofibromin, but not RGS3, which promotes GTP hydrolysis. The resulting GDP-bound KRASG12C is an anticancer drug target. Combination with inhibitors of SOS1 or with inhibitors of downstream signaling may further improve efficacy.
GRAPHIC: C. BICKEL/SCIENCE
 
First a discussion of the RAS signalling cycle is shown below in a good review of RAS activation and signal termination.
 
PMCID: PMC3124093
NIHMSID: NIHMS294865
PMID: 21102635
Ras superfamily GEFs and GAPs: validated and tractable targets for cancer therapy?

Abstract

There is now considerable and increasing evidence for a causal role of aberrant activity of the Ras superfamily of small GTPases in human cancers. These GTPases act as GDP-GTP-regulated binary switches that control many fundamental cellular processes. A common mechanism of GTPase deregulation in cancer is the deregulated expression and/or activity of their regulatory proteins, guanine nucleotide exchange factors (GEFs) that promote formation of the active GTP-bound state and GTPase activating proteins (GAPs) that return the GTPase to its GDP-bound inactive state. We assess the association of GEFs and GAPs with cancer and their druggability for cancer therapeutics.

Box 1

Ras superfamily of small GTPases

The human Ras superfamily comprised of over 150 members which is divided into five major branches on the basis of sequence and functional similarities, . In addition to the three Ras isoforms, other members of the Ras family with important roles in cancer include Rheb and Ral proteins. The ~20 kDa core G domain (corresponding to Ras residues 4–166) is conserved among all Ras superfamily proteins and is involved in GTP binding and hydrolysis. This domain is comprised of five conserved guanine nucleotide consensus sequence elements (Ras residue numbering) involved in binding phosphate/Mg2+ (PM) or the guanine base (G). The switch I (Ras residues 30–38) and II (59–76) regions change in conformation during GDP-GTP cycling and contribute to preferential effector binding to the GTP-bound state and the core effector domain (E; Ras residues 32–40). Ras and Rho family proteins have additional C-terminal hypervariable (HV) sequences that commonly terminate with a CAAX motif that signals for farnesyl or geranylgeranyl isoprenoid addition to the cysteine residue, proteolytic removal of the AAX residues and carboxylmethylation of the prenylated cysteine. Some are modified additionally by a palmitate fatty acid to cysteine residues in the HV sequence that contributes to membrane association. Rab proteins also contain a C-terminal HV region that terminates with cysteine-containing motifs that are modified by addition of geranylgeranyl lipids, with some undergoing carboxylmethylation. Arf family proteins are characterized by an N-terminal extension involved in membrane interaction, with some cotranslationally modified by addition of a myristate fatty acid. Ran is not lipid modified but contains a C-terminal extension that is essential for function. Rho proteins are characterized by an up to 13 amino acid “Rho insert” sequence positioned between Ras residues 122 and 123 involved in effector regulation.

 

An external file that holds a picture, illustration, etc.
Object name is nihms294865u1a.jpg

 

The GDP-GTP cycle

Ras superfamily proteins possess intrinsic guanine nucleotide exchange and GTP hydrolysis activities. However, these activities are too low to allow efficient and rapid cycling between their active GTP-bound and inactive GDP-bound states. GEFs and GAPs accelerate and regulate these intrinsic activities. Members of the different branches of the superfamily are regulated by GEFs and GAPs with structurally distinct catalytic domains, , –. Here we have utilized the Rho family as an example to illustrate the complexity of this process, where multiple GEFs and GAPs may regulate one specific GTPase. For the 20 human Rho GTPases there are 83 GEFs and 67 GAPs and a subset of Rho GTPases are not likely regulated by GEFs and GAPs (e.g., Rnd3/RhoE). Rho GTPases are activated by distinct RhoGEF families. Dbl family RhoGEFs (68) possesses a tandem Dbl homology (DH) catalytic and pleckstrin homology (PH) regulatory domain topology. DOCK family RhoGEFs (11) are characterized by two regions of high sequence conservation that are designated Dock-homology region regulatory DHR-1 and catalytic DHR-2 domains. Two other RhoGEFs have been described (SWAP70 and SLAT) contain a PH but no DH domain (2) and smgGDS (1) is an unusual GEF in that it functions as a GEF for some Rho as well as non-Rho family GTPases. At least 24 Dbl RhoGEFs have been reported to activate RhoA. Rho (and Rab) GTPases are also controlled by a third class of regulatory proteins, Rho dissociation inhibitors (RhoGDI) (of which there are 3) whose main function involves regulation of Rho GTPase membrane association by masking the isoprenoid group.

 

An external file that holds a picture, illustration, etc.
Object name is nihms294865u2.jpg

So GEFs like SOS 1,2,3 are responsible for activation of the G Protien RAS upon Receptor Tyrosine Kinase (RTK) activation by a multitude of growth factors.  The GAPs are responsible for termination of the activated RAS cycle and now RAS, in its normal unmuted version, can now be activated again.  The G12C mutant keeps RAS in its activated state as GAPs cannot fascilitate the hydroylsis of GTP.  

Li et al. (in this issue) theorized that there must be cellular factors that stimulate formation of GDP-bound KRAS G12C, enabling its vulnerability to KRAS G12C inhibitors, and discovered a regulator of G protein signaling 3 (RGS3), which had GAP activity previously for heterotrimeric G proteins GαI and Gαq with unexpected GAP activity for the KRAS G12C mutant.   They also found the fully activated GTP bound KRAS G12C is dependent on RTK-mediated activation of SOS1.  This suggested that SOS1 inhibitors with MEK inhibitors could be effective against mutant KRAS.

The paper by Li is summarized below:

The G protein signaling regulator RGS3 enhances the GTPase activity of KRAS

SCIENCE•8 Oct 2021•Vol 374, Issue 6564•pp. 197-201•DOI: 10.1126/science.abf1730

Abstract

Recently reported to be effective in patients with lung cancer, KRASG12C inhibitors bind to the inactive, or guanosine diphosphate (GDP)–bound, state of the oncoprotein and require guanosine triphosphate (GTP) hydrolysis for inhibition. However, KRAS mutations prevent the catalytic arginine of GTPase-activating proteins (GAPs) from enhancing an otherwise slow hydrolysis rate. If KRAS mutants are indeed insensitive to GAPs, it is unclear how KRASG12C hydrolyzes sufficient GTP to allow inactive state–selective inhibition. Here, we show that RGS3, a GAP previously known for regulating G protein–coupled receptors, can also enhance the GTPase activity of mutant and wild-type KRAS proteins. Our study reveals an unexpected mechanism that inactivates KRAS and explains the vulnerability to emerging clinically effective therapeutics.

UPDATED 09/26/2021

The KRAS G12C Inhibitor MRTX849 Provides Insight toward Therapeutic Susceptibility of KRAS-Mutant Cancers in Mouse Models and Patients

Source: Hallin J, Engstrom LD, Hargis L, Calinisan A, Aranda R, Briere DM, Sudhakar N, Bowcut V, Baer BR, Ballard JA, Burkard MR, Fell JB, Fischer JP, Vigers GP, Xue Y, Gatto S, Fernandez-Banet J, Pavlicek A, Velastagui K, Chao RC, Barton J, Pierobon M, Baldelli E, Patricoin EF 3rd, Cassidy DP, Marx MA, Rybkin II, Johnson ML, Ou SI, Lito P, Papadopoulos KP, Jänne PA, Olson P, Christensen JG. The KRASG12C Inhibitor MRTX849 Provides Insight toward Therapeutic Susceptibility of KRAS-Mutant Cancers in Mouse Models and Patients. Cancer Discov. 2020 Jan;10(1):54-71. doi: 10.1158/2159-8290.CD-19-1167. Epub 2019 Oct 28. PMID: 31658955; PMCID: PMC6954325.

Abstract

Despite decades of research, efforts to directly target KRAS have been challenging. MRTX849 was identified as a potent, selective, and covalent KRASG12C inhibitor that exhibits favorable drug-like properties, selectively modifies mutant cysteine 12 in GDP-bound KRASG12C, and inhibits KRAS-dependent signaling. MRTX849 demonstrated pronounced tumor regression in 17 of 26 (65%) KRASG12C-positive cell line- and patient-derived xenograft models from multiple tumor types, and objective responses have been observed in patients with KRASG12C-positive lung and colon adenocarcinomas. Comprehensive pharmacodynamic and pharmacogenomic profiling in sensitive and partially resistant nonclinical models identified mechanisms implicated in limiting antitumor activity including KRAS nucleotide cycling and pathways that induce feedback reactivation and/or bypass KRAS dependence. These factors included activation of receptor tyrosine kinases (RTK), bypass of KRAS dependence, and genetic dysregulation of cell cycle. Combinations of MRTX849 with agents that target RTKs, mTOR, or cell cycle demonstrated enhanced response and marked tumor regression in several tumor models, including MRTX849-refractory models. SIGNIFICANCE: The discovery of MRTX849 provides a long-awaited opportunity to selectively target KRASG12C in patients. The in-depth characterization of MRTX849 activity, elucidation of response and resistance mechanisms, and identification of effective combinations provide new insight toward KRAS dependence and the rational development of this class of agents.

Introduction

KRAS is one of the most frequently mutated oncogenes in cancer; however, efforts to directly target KRAS have been largely unsuccessful due to its high affinity for GTP/GDP and the lack of a clear binding pocket (1–4). More recently, compounds were identified that covalently bind to KRASG12C at the cysteine 12 residue, lock the protein in its inactive GDP-bound conformation, inhibit KRAS-dependent signaling, and elicit antitumor responses in tumor models (5–7). Advances on early findings demonstrated that the binding pocket under the switch II region was exploitable for drug discovery, culminating in the identification of more potent KRASG12C inhibitors with improved physiochemical properties that are now entering clinical trials. The identification of KRASG12C inhibitors provides a renewed opportunity to develop a more comprehensive understanding of the role of KRAS as a driver oncogene and to explore the clinical utility of direct KRAS inhibition.

KRASG12C mutations are present in lung and colon adenocarcinomas as well as smaller fractions of other cancers. The genetic context of KRASG12C alteration can vary significantly among tumors and is predicted to affect response to KRAS inhibition. KRAS mutations are often enriched in tumors due to amplification of mutant or loss of wild-type allele (8, 9). In addition, KRAS mutations often co-occur with other key genetic alterations including TP53 and CDKN2A in multiple cancers, KEAP1 and/or STK11 in lung adenocarcinoma, or APC and PIK3CA in colon cancer (3, 8–12). Whether differences in KRAS-mutant allele fraction or co-occurrence with other mutations influence response to KRAS blockade is not yet well understood. In addition, due to the critical importance of the RAS pathway in normal cellular function, there is extensive pathway isoform redundancy and a comprehensive regulatory network in normal cells to ensure tight control of temporal pathway signaling. RAS pathway negative feedback signaling is mediated by ERK1/2 and receptor tyrosine kinases (RTK) as well as by ERK pathway target genes including dual-specificity phosphatases (DUSP) and Sprouty (SPRY) proteins (13–17). One important clinically relevant example is provided by the reactivation of ERK signaling observed following treatment of BRAFV600E-mutant cancers with selective BRAF inhibitors (18–20). The observed intertumoral heterogeneity and extensive feedback signaling network in KRAS-mutant cancers may necessitate strategies to more comprehensively block oncogenic signal transduction and deepen the antitumor response in concert with KRAS blockade (15, 21, 22).

Potential strategies to augment the response to KRASG12C inhibitor treatment are evident at multiple nodes of the signaling pathway regulatory machinery. RAS proteins are small GTPases that normally cycle between an active, GTP-bound state and an inactive, GDP-bound state. RAS proteins are loaded with GTP through guanine nucleotide exchange factors (e.g., SOS1) which are activated by upstream RTKs, triggering subsequent interaction with effector proteins that activate RAS-dependent signaling. RAS proteins hydrolyze GTP to GDP through their intrinsic GTPase activity, which is dramatically enhanced by GTPase-activating proteins (GAP). Mutations at codons 12 and 13 in RAS proteins impair GAP-stimulated GTP hydrolysis, leaving RAS predominantly in the GTP-bound, active state.

Potent covalent KRASG12C inhibitors described to date bind only GDP-bound KRAS (5–7). Although codon 12 and 13 mutations decrease the fraction of GDP-bound KRAS, recent biochemical analyses revealed that KRASG12C exhibits the highest intrinsic GTP hydrolysis rate and highest nucleotide exchange rate among KRAS mutants (23). Furthermore, the nucleotide-bound state of KRASG12C can be shifted toward the GDP-bound state by pharmacologically modulating upstream signaling with RTK inhibitors that increase the activity of KRASG12C inhibitors (7, 22, 24). Likewise, SHP2 is a phosphatase that positively transduces RTK signaling to KRAS. Accordingly, SHP2 inhibitors are active in cancers driven by KRAS mutations that are dependent on nucleotide cycling, including KRASG12C (25–27).

MRTX849 is among the first KRASG12C inhibitors to advance to clinical trials. The comprehensive and durable inhibition of KRASG12C by MRTX849 provides a unique opportunity to understand the extent to which KRAS functions as an oncogenic driver. In addition, the observation that the response to blockade of KRAS is markedly different in vitro and in vivo indicates that evaluation of the consequences of KRAS blockade in in vivo model systems is critical to understand the role of KRAS-driven tumor progression. The demonstration of partial responses in patients with lung and colon adenocarcinomas treated with MRTX849 in clinical trials indicates that results observed in tumor models extend to KRASG12C-positive human cancers. Our comprehensive molecular characterization of multiple tumor models at baseline and during response to KRAS inhibition has provided further insight toward the contextual role of KRAS mutation in the setting of genetic and tumoral heterogeneity. Finally, further interrogation of these genetic alterations and signaling pathways utilizing functional genomics strategies including CRISPR and combination approaches uncovered regulatory nodes that sensitize tumors to KRAS inhibition when cotargeted.

Introduction

KRAS is one of the most frequently mutated oncogenes in cancer; however, efforts to directly target KRAS have been largely unsuccessful due to its high affinity for GTP/GDP and the lack of a clear binding pocket (1–4). More recently, compounds were identified that covalently bind to KRASG12C at the cysteine 12 residue, lock the protein in its inactive GDP-bound conformation, inhibit KRAS-dependent signaling, and elicit antitumor responses in tumor models (5–7). Advances on early findings demonstrated that the binding pocket under the switch II region was exploitable for drug discovery, culminating in the identification of more potent KRASG12C inhibitors with improved physiochemical properties that are now entering clinical trials. The identification of KRASG12C inhibitors provides a renewed opportunity to develop a more comprehensive understanding of the role of KRAS as a driver oncogene and to explore the clinical utility of direct KRAS inhibition.

KRASG12C mutations are present in lung and colon adenocarcinomas as well as smaller fractions of other cancers. The genetic context of KRASG12C alteration can vary significantly among tumors and is predicted to affect response to KRAS inhibition. KRAS mutations are often enriched in tumors due to amplification of mutant or loss of wild-type allele (8, 9). In addition, KRAS mutations often co-occur with other key genetic alterations including TP53 and CDKN2A in multiple cancers, KEAP1 and/or STK11 in lung adenocarcinoma, or APC and PIK3CA in colon cancer (3, 8–12). Whether differences in KRAS-mutant allele fraction or co-occurrence with other mutations influence response to KRAS blockade is not yet well understood. In addition, due to the critical importance of the RAS pathway in normal cellular function, there is extensive pathway isoform redundancy and a comprehensive regulatory network in normal cells to ensure tight control of temporal pathway signaling. RAS pathway negative feedback signaling is mediated by ERK1/2 and receptor tyrosine kinases (RTK) as well as by ERK pathway target genes including dual-specificity phosphatases (DUSP) and Sprouty (SPRY) proteins (13–17). One important clinically relevant example is provided by the reactivation of ERK signaling observed following treatment of BRAFV600E-mutant cancers with selective BRAF inhibitors (18–20). The observed intertumoral heterogeneity and extensive feedback signaling network in KRAS-mutant cancers may necessitate strategies to more comprehensively block oncogenic signal transduction and deepen the antitumor response in concert with KRAS blockade (15, 21, 22).

Potential strategies to augment the response to KRASG12C inhibitor treatment are evident at multiple nodes of the signaling pathway regulatory machinery. RAS proteins are small GTPases that normally cycle between an active, GTP-bound state and an inactive, GDP-bound state. RAS proteins are loaded with GTP through guanine nucleotide exchange factors (e.g., SOS1) which are activated by upstream RTKs, triggering subsequent interaction with effector proteins that activate RAS-dependent signaling. RAS proteins hydrolyze GTP to GDP through their intrinsic GTPase activity, which is dramatically enhanced by GTPase-activating proteins (GAP). Mutations at codons 12 and 13 in RAS proteins impair GAP-stimulated GTP hydrolysis, leaving RAS predominantly in the GTP-bound, active state.

Potent covalent KRASG12C inhibitors described to date bind only GDP-bound KRAS (5–7). Although codon 12 and 13 mutations decrease the fraction of GDP-bound KRAS, recent biochemical analyses revealed that KRASG12C exhibits the highest intrinsic GTP hydrolysis rate and highest nucleotide exchange rate among KRAS mutants (23). Furthermore, the nucleotide-bound state of KRASG12C can be shifted toward the GDP-bound state by pharmacologically modulating upstream signaling with RTK inhibitors that increase the activity of KRASG12C inhibitors (7, 22, 24). Likewise, SHP2 is a phosphatase that positively transduces RTK signaling to KRAS. Accordingly, SHP2 inhibitors are active in cancers driven by KRAS mutations that are dependent on nucleotide cycling, including KRASG12C (25–27).

MRTX849 is among the first KRASG12C inhibitors to advance to clinical trials. The comprehensive and durable inhibition of KRASG12C by MRTX849 provides a unique opportunity to understand the extent to which KRAS functions as an oncogenic driver. In addition, the observation that the response to blockade of KRAS is markedly different in vitro and in vivo indicates that evaluation of the consequences of KRAS blockade in in vivo model systems is critical to understand the role of KRAS-driven tumor progression. The demonstration of partial responses in patients with lung and colon adenocarcinomas treated with MRTX849 in clinical trials indicates that results observed in tumor models extend to KRASG12C-positive human cancers. Our comprehensive molecular characterization of multiple tumor models at baseline and during response to KRAS inhibition has provided further insight toward the contextual role of KRAS mutation in the setting of genetic and tumoral heterogeneity. Finally, further interrogation of these genetic alterations and signaling pathways utilizing functional genomics strategies including CRISPR and combination approaches uncovered regulatory nodes that sensitize tumors to KRAS inhibition when cotargeted.

Results

MRTX849 Is a Potent and Selective Inhibitor of KRASG12C, KRAS-Dependent Signal Transduction, and Cell Viability In Vitro

A structure-based drug design approach, including optimization for favorable drug-like properties, led to the discovery of MRTX849 as a potent, covalent KRASG12C inhibitor (Fig. 1A; Supplementary Table S1). An LC/MS-based KRASG12C protein modification assay revealed that MRTX849 demonstrated much greater modification of KRASG12C when preloaded with GDP compared with GTP (Supplementary Table S2), supporting that MRTX849 binds to and stabilizes the inactive GDP-bound form of KRASG12C. Indeed, introducing a comutation that impairs the GTPase activity of KRASG12C (24) attenuated the inhibitory activity of MRTX1257, a close analogue of MRTX849 (Supplementary Fig. S1A). Secondary mutations that modulate the nucleotide exchange function of KRASG12C also affected inhibition by MRTX1257, supporting that the MRTX compound series is dependent on KRASG12C nucleotide cycling.

Figure 1.

 

MRTX849 is a potent, covalent KRASG12C inhibitor in vitro. A, Structure of MRTX849. B, Immunoblot protein Western blot analyses of KRAS pathway targets in MIA PaCa-2 cells treated from 1 hours to 72 hours with MRTX849 at 100 nmol/L. C, Immunoblot protein Western blot analyses of KRAS pathway targets in MIA PaCa-2 cells treated for 24 hours with MRTX849 over a 13-point dose response. D, Left y-axis shows active RAS ELISA assay to determine the reduction in RAS-GTP abundance following MRTX849 treatment in MIA PaCa-2 cells for 24 hours. The vehicle value was normalized to 1 by dividing all average values by the vehicle value. Right y-axis shows quantitation of KRAS band shift by MRTX849 treatment in MIA PaCa-2 cells for 24 hours as assessed by Western blot and densitometry. E, In-cell Western blot assay to evaluate modulation of pERK in MIA PaCa-2 cells grown in standard tissue-culture conditions treated with MRTX849 over a time course. F, CellTiter-Glo assay to evaluate cell viability performed on seven KRASG12C-mutant cell lines and three non–KRASG12C-mutant cell lines grown in 2-D tissue-culture conditions in a 3-day assay (left plot) or 3-D conditions using 96-well, ULA plates in a 12-day assay (right plot).

 

We next determined the cellular activity of MRTX849 utilizing the KRASG12C-mutant H358 lung and MIA PaCa-2 pancreatic cancer cell lines. In both models, MRTX849 demonstrated an upward electrophoretic mobility shift of KRASG12C protein band migration by immunoblot, indicative of covalent modification of KRASG12C. A maximal mobility shift was observed by 1 hour, was maintained through 72 hours (Fig 1B; Supplementary Fig. S1B), and was evident at concentrations as low as 2 nmol/L with near-maximal modification observed at 15.6 nmol/L (Fig. 1C; Supplementary Fig. S1C). Comparable inhibition of active RAS was observed as determined by a RAF RAS-binding domain capture ELISA assay (Fig. 1D; 1D). MRTX849 also inhibited KRAS-dependent signaling targets including ERK1/2 phosphorylation (pERK; Thr202/Tyr204 ERK1), S6 phosphorylation (pS6; RSK-dependent Ser235/236), and expression of the ERK-regulated DUSP6, each with IC50 values in the single-digit nanomolar range in both cell lines (Fig. 1B and C; Supplementary Fig. S1B and S1C). The evaluation of pERK over a time course of 48 hours indicated maximal inhibition was observed at 24 hours (Fig. 1E; Supplementary Fig. S1E). Treatment with the des-acrylamide version of MRTX849, which is unable to covalently bind to KRASG12C, did not demonstrate significant inhibition of ERK phosphorylation (Supplementary Fig. S1F). The H358 cell line was selected for determination of MRTX849 cysteine selectivity utilizing an LC/MS-based proteomics approach able to detect approximately 6,000 cysteine-containing peptides. After treatment for 3 hours, decreased KRASG12C Cys12-free peptide was detected with treated-to-control ratios of 0.029 and 0.008 determined at 1 and 10 μmol/L, respectively, indicating near-complete engagement of the intended target (Supplementary Table S3). In contrast, the only other peptides identified were from lysine-tRNA ligase (KARS) at Cys209 near the detection limit, indicating a high degree of selectivity toward KRASCYS12.

To evaluate the breadth of MRTX849 activity, its effect on cell viability was determined across a panel of 17 KRASG12C-mutant and 3 non–KRASG12C-mutant cancer cell lines using 2-D (3-day, adherent cells) and 3-D (12-day, spheroids) cell-growth conditions. MRTX849 potently inhibited cell growth in the vast majority of KRASG12C-mutant cell lines with IC50 values ranging between 10 and 973 nmol/L in the 2-D format and between 0.2 and 1,042 nmol/L in the 3-D format (Supplementary Table S4; Fig. 1F). In agreement with prior KRASG12C inhibitor studies (5), MRTX849 demonstrated improved potency in the 3-D assay format, as all but one KRASG12C-mutant cell line exhibited an IC50 value below 100 nmol/L. Although MRTX849 was broadly effective in inhibiting viability of KRASG12C-mutant cell lines, IC50 values varied across the cell panel by 100-fold, suggesting a differential degree of sensitivity to treatment. All three non–KRASG12C-mutant cell lines tested demonstrated IC50 values greater than 1 μmol/L in 2-D conditions and greater than 3 μmol/L in 3-D conditions, suggesting the effect of MRTX849 on cell viability was dependent on the presence of KRASG12C.

To determine whether the difference in sensitivity across the cell panel correlated with the ability of MRTX849 to bind to KRAS or inhibit KRAS-dependent signal transduction, seven KRASG12C-mutant cancer cell lines were selected from the panel for further evaluation. In each cell line, MRTX849 demonstrated a very similar concentration-dependent electrophoretic mobility shift (IC50) for KRASG12C protein migration, suggesting that the ability to bind to and modify KRASG12C does not readily account for differences in response in viability studies (Fig. 1B and C; Supplementary Figs. S1B and S1C and S2A and S2B). The effect of MRTX849 on selected phosphoproteins implicated in mediating KRAS-dependent signaling was also evaluated across the cell panel by immunoblot and/or reverse-phase protein array (RPPA) following treatment for 6 or 24 hours. Notably, the concentration–response relationship and maximal effect of MRTX849 on inhibition of ERK and S6S235/236 phosphorylation varied across the cell panel (Supplementary Fig. S2A and S2C; Supplementary Table S7). MRTX849 demonstrated only partial inhibition of pERK in KYSE-410 and SW1573 cells and a minimal effect on pS6S235/236 in SW1573, H2030, and KYSE-410 cells (Supplementary Fig. S2A and S2C). Each of these cell lines were among those that exhibited a submaximal response to MRTX849 in both 2-D and 3-D viability assays (Fig. 1F). Although KRAS is implicated in mediating signal transduction through the PI3K and mTOR pathways, there was minimal evidence of a significant and/or durable effect of MRTX849 on AKT (S473, T308) or 4E-BP1 (T37/T46, S65, T70) phosphorylation at any time point in any cell lines evaluated (Supplementary Fig. S2D). However, MRTX849 demonstrated concentration-dependent partial inhibition of the mTOR-dependent signaling targets p70 S6 kinase (T412) and/or pS6 (S240/44) in the H358, MIA PaCa-2,H2122, and H1373 cell lines, each of which exhibited a maximal response to treatment. Together, these data suggest that maximizing inhibition of KRAS-dependent ERK and S6 signaling may be required to elicit a robust response in tumor-cell viability assays.

MRTX849 Treatment In Vivo Leads to Dose-Dependent KRASG12C Modification, KRAS Pathway Inhibition, and Antitumor Efficacy

Studies were conducted to evaluate MRTX849 antitumor activity along with its pharmacokinetic and pharmacodynamic properties in vivo, both to understand the clinical utility of this agent and to provide insight toward response to treatment. MRTX849 demonstrated moderate plasma clearance and prolonged half-life following oral administration (Supplementary Table S1; Supplementary Fig. S3). To evaluate the pharmacodynamic response to MRTX849 and to correlate drug exposure with target inhibition, MRTX849 was administered via oral gavage over a range of dose levels to H358 xenograft–bearing mice, and plasma and tumors were collected at defined time points. The fraction of covalently modified KRASG12C protein was proportional to the plasma concentration of MRTX849 (Fig. 2A). When evaluated over time after a single oral dose at 30 mg/kg, the modified fraction of KRASG12C was 74% at 6 hours after dose and gradually decreased to 47% by 72 hours (Fig. 2B). This extended pharmacodynamic effect, despite declining levels of MRTX849 in plasma, was consistent with the irreversible inhibition of KRASG12C by MRTX849 and the relatively long half-life for the KRASG12C protein (∼24–48 hours; Supplementary Table S5). The modification of KRASG12C was maximized after repeated daily dosing for 3 days at 30 mg/kg (Fig. 2B), and higher dose levels did not demonstrate additional KRASG12C modification in multiple tumor models (data not shown). The maximum level of modification of approximately 80%, despite increasing dose and plasma levels of MRTX849, suggests that accurate measurement of complete inhibition of KRASG12C utilizing LC/MS may not be attainable, potentially due to a pool of active, noncycling, or unfolded KRASG12C protein in tumors. Together, these studies demonstrated a dose-dependent increase in covalent modification of KRASG12C by MRTX849 and that the majority of targetable KRAS was covalently modified by MRTX849 over a repeated administration schedule at dose levels at or exceeding 30 mg/kg.

Figure 2.

 

MRTX849 modifies KRASG12C and inhibits KRAS signaling and tumor growth in vivo. A, MRTX849 was administered orally as a single dose to mice bearing established H358 xenografts (average tumor volume ∼350 mm3) at 10, 30, and 100 mg/kg. KRAS modification and MRTX849 plasma concentration data from n = 3 mice are shown as mean ± SD. KRASG12C modification was statistically significant versus vehicle control using the two-tailed Student t test. **, P < 0.01. B, MRTX849 was administered orally as a single dose or daily (QD) for 3 days to mice bearing established H358 xenografts (average tumor volume ∼350 mm3) at 30 mg/kg. Plasma was collected at 0.5, 2, 6, 24, 48, and 72 hours after administration of the last dose, and tumors were collected at 6, 24, 48, and 72 hours after dose. KRASG12C modification and MRTX849 plasma concentration data are shown from n = 3 mice as mean ± SD. Induction of modified KRASG12C protein at all time points was determined to be statistically significant versus vehicle control using two-way ANOVA. In addition, induction of modified KRASG12C protein at 72 hours in day 1 samples and 48 and 72 hours in day 3 samples was statistically significant versus the 6-hour time point. Brackets indicate P < 0.05 as compared with left-most sample. C, MRTX849 was administered as in A. Tumors were collected 6 hours after dose, and total and phosphorylated ERK1/2 and total and phosphorylated S6 were analyzed by immunoblot and quantified by densitometric analysis. Relative fluorescence intensity of pERK1/2 and pS6 was normalized by dividing pERK1/2 and pS6 by total ERK1/2 and total S6, respectively. Vehicle-treated tumors were normalized to 1 by dividing all average values by the vehicle value. Average pERK1/2 and pS6 values were divided by the average value in vehicle-treated tumors. Data shown represent the average of 2 to 3 tumors per treatment group plus SD. Reduction of pS6 relative fluorescence intensity was determined to be statistically significant versus vehicle control using the two-tailed Student t test. Brackets indicate P < 0.05 compared with left-most sample. D, MRTX849 was administered as in B. Tumors were collected at 6, 24, 48, or 72 hours after administration of the last dose, and total and phosphorylated ERK1/2 and total and phosphorylated S6 were analyzed as in C. Data shown represent the average of 3 to 4 tumors per treatment group plus SD. Reduction of pS6 relative fluorescence intensity on day 3 was determined to be statistically significant versus vehicle control using two-way ANOVA. Brackets indicate P < 0.05 compared with left-most sample. E, MRTX849 was administered via daily oral gavage at the doses indicated to mice bearing established MIA PaCa-2 xenografts. Dosing was initiated when tumors were approximately 350 to 400 mm3.MRTX849 was administered to mice daily until day 16. Data are shown as mean tumor volume ± SEM. Tumor volumes at day 16 were determined to be statistically significant versus vehicle control via two-tailed Student t test. **, P < 0.01; *, P < 0.05.

 

To evaluate the effect of MRTX849 on KRAS-dependent signal transduction in vivo, a single dose of MRTX849 at 10, 30, or 100 mg/kg was administered to H358 tumor–bearing mice. Dose-dependent inhibition of ERK1/2 and pS6S235/36 phosphorylation was observed at 6 hours after dose based on immunoblot and densitometric analysis (Fig. 2C). MRTX849 also demonstrated marked inhibition of ERK1/2 and S6S235/36 phosphorylation after one or three daily doses at 6 or 24 hours, and levels gradually recovered by 72 hours after the final dose (Fig. 2D). pERK1/2 and pS6S235/36 were further evaluated in formalin-fixed, paraffin-embedded sections from vehicle-treated and MRTX849-treated xenografts in four tumor models utilizing IHC methods coupled with image analysis algorithms. These studies demonstrated increased pERK1/2 and pS6 in nontumor/stromal cells following MRTX849 administration, indicating that immunoblotting studies with bulk tumor lysate likely underrepresent the degree of pathway inhibition in tumor cells, whereas IHC-based evaluation may more accurately reflect both the degree and spatial impact of pathway inhibition. Maximal inhibition was observed for both ERK and S6S235/36 phosphorylation after a single dose at the 6-hour time point, with a rebound in signaling evident 24 hours after single dose in each model (Supplementary Fig. S4). Marked inhibition of ERK phosphorylation was observed at 6 hours after administration, with 89%, 94%, and 94% inhibition observed compared with vehicle controls in MIA PaCa-2, H1373, and H2122 tumors, respectively (H358 pERK not quantifiable). This indicates that dose levels at or exceeding 30 mg/kg dose maximized inhibition of ERK phosphorylation in multiple models (Supplementary Fig. S4A and S4B). Inhibition of S6 phosphorylation at 6 hours was more variable, with percent inhibition values of 76%, 50%, 86%, and 56% observed in MIA PaCa-2, H1373, H358, and H2122 tumors, respectively (Supplementary Fig. S4B). Together, these data indicate that consistent acute (6 hours) inhibition of KRAS-dependent ERK phosphorylation was maximized in all evaluated models, whereas inhibition of S6S235/36 was more variable, presumably due to varying degrees of KRAS-independent activation of this pathway in different tumor models.

MIA PaCa-2 and H358 were selected as MRTX849-responsive tumor models, thereby enabling a high-resolution understanding of dose–response relationships. Significant, dose-dependent, antitumor activity was observed at the 3, 10, 30, and 100 mg/kg dose levels in the MIA PaCa-2 model (Fig. 2E). Evidence of rapid tumor regression was observed at the earliest post-treatment tumor measurement, and animals in the 30 and 100 mg/kg cohorts exhibited evidence of a complete response at study day 15. Dosing was stopped at study day 16, and all 4 mice in the 100 mg/kg cohort and 2 of 7 mice in the 30 mg/kg cohort remained tumor-free through study day 70 (Supplementary Fig. S5A). In a second MIA PaCa-2 study, dose-dependent antitumor efficacy was observed at the 5, 10, and 20 mg/kg dose levels, and 2 of 5 mice at the 20 mg/kg dose level exhibited complete tumor regression (Supplementary Fig. S5B). Significant dose-dependent antitumor efficacy was also observed in the H358 model, including 61% and 79% tumor regression at the 30 and 100 mg/kg dose levels, respectively, at day 22 (Supplementary Fig. S5C). MRTX849 was well tolerated, and no effect on body weight was observed at all dose levels evaluated (Supplementary Fig. S5D). These studies indicated that MRTX849 demonstrated dose-dependent antitumor efficacy over a well-tolerated dose range and that the maximally efficacious dose of MRTX849 is between 30 and 100 mg/kg/day.

MRTX849 Demonstrates Broad-Spectrum Tumor Regression in KRASG12C Cell Line and Patient-Derived Xenograft Models

To evaluate the breadth of antitumor activity across genetically and histologically heterogeneous KRASG12C-mutant cancer models, MRTX849 was evaluated at a fixed dose of 100 mg/kg/day (a dose projected to demonstrate near-maximal target inhibition in most models) in a panel of human KRASG12C-mutant cell line–derived xenograft (CDX) and patient-derived xenograft (PDX) models. MRTX849 demonstrated tumor regression exceeding 30% volume reduction from baseline in 17 of 26 models (65%) at approximately 3 weeks of treatment (Fig. 3A; Supplementary Table S6). By comparison, MRTX849 did not exhibit significant antitumor activity at 100 mg/kg in three non–KRASG12C-mutant models (Fig. 3A; Supplementary Table S6). Together, these results indicate that KRASG12C-mutant tumors are broadly dependent upon mutant KRAS for tumor-cell growth and survival and that MRTX849 elicits antitumor activity through a KRASG12C-dependent mechanism.

Figure 3.

 

Antitumor activity of MRTX849 in KRASG12C-mutant and non–KRASG12C-mutant human tumor xenograft models. A, MRTX849 was administered via oral gavage at 100 mg/kg every day to mice bearing the CDX or PDX model indicated. Dosing was initiated when tumors were, on average, approximately 250 to 400 mm3. MRTX849 was formulated as a free base and resuspended as a solution in 10% Captisol and 50 mmol/L citrate buffer, pH 5.0. The percent change from baseline control was calculated at days 19 to 22 for most models. Statistical significance was determined for each model and is shown in Supplementary Table S6. Status of mutations and alterations in key genes is shown below each model. MAF (%), percent KRASG12C-mutant allele fraction by RNA-seq; CNV, copy-number variation; * denotes very high CDK4 expression by RNA-seq and possible amplification. HER family status was determined by averaging EGFR, ERBB2, and ERBB3 RNA-seq expression for CDX (CCLE) or PDX (Crown huBase) models. Positive HER family calls denote greater than the median expression of the models tested. CDX and PDX model HER family calls were determined independently. B, Tumor-growth inhibition plots from representative xenograft models that were categorized as sensitive, partially sensitive, or treatment refractory.

 

Although MRTX849 exhibited marked antitumor responses in the majority of models tested, a response pattern ranging from delayed tumor growth to complete regression was observed across the xenograft panel. The response to treatment was categorized as sensitive, partially sensitive, or treatment refractory (Fig. 3B). Rank order and Pearson statistical analyses were performed to evaluate the correlation between in vitro potency (IC50 in 2-D or 3-D viability assays) and antitumor response in vivo (% regression or progression on day 22), and a significant correlation between response in cell lines compared with tumor models was not observed (Supplementary Fig. S6A and S6B). Thus, we focused on a comprehensive analysis of correlates with MRTX849 tumor response in vivo, including tumor histology, co-occurring genetic alterations, as well as baseline or drug-induced changes in expression of KRAS-related genes [RNA sequencing (RNA-seq)] and/or protein signaling networks (RPPA in 18 models, ref. 28; Supplementary Fig. S7). No individual genetic alteration, including but not limited to KRAS-mutant allele frequency, TP53, STK11, or CDKN2A, predicted the antitumor activity of MRTX849. Interestingly, baseline gene and/or protein expression of selected members of the HER family of RTKs and of regulators of early cell-cycle transition did exhibit a trend with the degree of antitumor response, suggesting these pathways may influence the response to KRAS inhibitors (Supplementary Fig. S7A). Together, these data indicate that there are no individual binary biomarkers that clearly predict therapeutic response and that the molecular complexity and heterogeneity present in distinct KRAS-mutated tumors may contribute to the response to target blockade.

MRTX849 Antitumor Activity Translates to RECIST Responses in Patients with Cancer

A 45-year-old female former smoker diagnosed with stage IV lung adenocarcinoma and refractory to multiple lines of therapy including carboplatin/pemetrexed/pembrolizumab, docetaxel, and investigational treatment with binimetinib and palbociclib was enrolled onto the MRTX849-001 phase Ib clinical trial with two bilateral lung lesions and mediastinal lymph node as target lesions. Targeted next-generation sequencing (NGS) demonstrated a KRASG12C mutation (c.34G>T). In addition, loss-of-function KEAP1 (K97M) and STK11 (E223*) mutations were detected and are predicted to be deleterious to their respective proteins. The patient was administered MRTX849 (600 mg twice a day) and had marked clinical improvement within 2 weeks, including complete resolution of baseline cough and oxygen dependency. A RECIST-defined partial response of 33% reduction of target lesions was observed at cycle 3 day 1 (45 days), and the patient continues on study (Fig. 4A).

Figure 4.

 

Activity of MRTX849 in patients with lung and colon cancers. A, Pretreatment and 6-week scans of a heavily pretreated patient with a KRASG12C mutation–positive lung adenocarcinoma indicating 33% reduction of target lesions. Patient continues on study. The top plots show a coronal view, and bottom plots an axial view of CT chest images prior to MRTX849 treatment (left) and after two cycles of MRTX849 treatment (right). B, Baseline, 6-week (Cycle 2), and 12-week (Cycle 4) scans of a patient with a KRASG12C mutation–positive colon adenocarcinoma. Partial response (PR) was confirmed at Cycle 4, and patient continues on study. Four lesions (TL1–4) are shown with axial views of CT images prior to MRTX849 treatment (top), after two cycles of MRTX849 treatment (center), and after four cycles of MRTX849 treatment (bottom).

 

A 47-year-old female never-smoker with metastatic adenocarcinoma of the left colon who exhibited progressive disease after receiving multiple lines of systemic therapy, including FOLFOX plus bevacizumab, single-agent capecitabine, FOLFIRI plus bevacizumab, and an investigational antibody–drug conjugate, was enrolled into the MRTX849-001 phase Ib clinical trial. This patient had extensive metastases involving the liver, peritoneum, ovaries, and lymph nodes. Targeted NGS identified a KRASG12C mutation. The patient was administered MRTX849 (600 mg twice a day) and demonstrated marked clinical improvement within 3 weeks and a visible decrease in size of her umbilical Sister Mary Joseph nodule. Her carcinoembryonic antigen levels decreased from 77 ng/mLat baseline to 11 ng/mL at cycle 2 day 1 and 3 ng/mL by cycle 3 day 1 (normal range, 0–5 ng/mL). A RECIST-defined partial response with a 37% reduction of target lesions and complete response of a nontarget lesion was observed at cycle 3 day 1(day 42). Confirmatory CT scans were conducted at cycle 5, day 1 (day 84) and indicated a confirmed RECIST partial response with further reduction of target lesions at −47% from baseline (Fig. 4B). The patient remains on treatment through Cycle 6.

Temporal Effects of MRTX849 on KRAS-Dependent Signaling and Feedback Pathways and Relationship to Antitumor Activity Following Repeat Dosing in Xenograft Models

A comprehensive analysis was conducted to evaluate MRTX849-induced temporal molecular changes to further interrogate mechanisms of drug response across sensitive and partially sensitive models. To evaluate temporal changes in global gene expression, xenograft-bearing mice were administered vehicle or 100 mg/kg MRTX849, and RNA-seq was performed on tumors at 6 and 24 hours after treatment. Gene expression was evaluated at day 1 and day 5 for the sensitive models MIA PaCa-2 and H1373 to ensure sufficient tissue availability from regressing tumors, or at day 7 in the partially sensitive models H358, H2122, and H2030 to coordinate with tumor stasis plateau. The top differentially expressed gene set enrichment analysis (GSEA) hallmark gene sets, regardless of tumor response, in all five models were several KRAS-annotated gene sets confirming MRTX849 selectively inhibits multiple genes directly related to KRAS signaling. MYC, mTOR, cell cycle, and apoptosis/BCL2 pathway gene sets were also strongly differentially expressed, confirming MRTX849 broadly affected multiple well-established, KRAS-regulated pathways, several of which have proved difficult to directly inhibit with previous targeted therapies (Fig. 5A and B; Supplementary Fig. S8A—S8D). The marked impact of MRTX849 on a large number of genes that regulate cell cycle and apoptosis provides further insight into molecular mechanisms which mediate its antitumor activity.

Figure 5.

 

MRTX849 treatment in vivo regulates KRAS-dependent oncogenic signaling and feedback-inhibitory pathways. A, Volcano plots displaying differentially expressed genes in xenograft tumors 24 hours after oral administration of vehicle or 100 mg/kg MRTX849 in a representative MRTX849-sensitive (H1373) and MRTX849-partially sensitive (H358) model. Significance denoted in the legend (Padj < 0.01). B, GSEA heat maps depicting hallmark signature pathways differentially regulated in at least one model 24 hours following oral administration of a single 100 mg/kg MRTX849 dose compared with vehicle. Normalized enrichment score shown in all models 6 or 24 hours after a single dose (QD × 1) or 5 (QD × 5) or 7 (QD × 7) days dosing. C, Genes that feedback-inhibit MAP kinase signaling are downregulated following MRTX849 treatment in all five cell line xenografts assessed by RNA-seq. TPM, Transcripts Per Kilobase Million.

 

Targeted RNA-seq analysis was performed on genes implicated in the temporal regulation of external signaling inputs and feedback pathways which collectively temper signaling flux through the RAS–RAF–MEK–ERK MAP kinase (MAPK) pathway including DUSP, SPRY, and PHLDA family genes (13, 18). These MAPK pathway–negative regulators were each ranked among the most strongly decreased genes following MRTX849 treatment, providing evidence that ERK-dependent transcriptional output is blocked and that pathways involved in reactivation of RTK- and ERK-dependent signaling were activated (Fig. 5C; Supplementary Fig. S4A).

On the basis of the observation of dynamic changes in transcriptional programs linked to KRAS pathway reactivation, IHC plus quantitative imaging of tumor cell–specific pERK and pS6 was evaluated over a range of time points. In the sensitive MIA PaCa-2 and H1373 tumor models, treatment with MRTX849 (100 mg/kg) demonstrated ≥90% inhibition of ERK phosphorylation at 6 and 24 hours on both days 1 and 5 (Supplementary Fig. S4). In contrast, in the partially sensitive H358 and H2122 models, robust inhibition of ERK phosphorylation was observed at 6 hours after a single dose; however, marked recovery of ERK phosphorylation was observed at 24 hours after single dose and at both 6 and 24 hours following 7 days of repeat-dose administration. Because DUSP, SPRY, and ETV family transcripts remain downregulated through 5 to 7 days in all models, it is evident that other independent factors contribute to temporal reactivation of ERK (Fig. 5C). Similar to what was observed with single-dose administration, the effect of MRTX849 on pS6 was variable over time and did not track with the antitumor activity of MRTX849. Together, these results suggest that the extent and duration of inhibition of pERK may track with the magnitude of antitumor efficacy of KRASG12C inhibitors and that further evaluation of the role of S6 is required to understand if it plays a role in drug sensitivity.

The effect of MRTX849 on cell proliferation and apoptosis was characterized by IHC analysis of Ki-67 or cleaved caspase-3 after a single dose or repeat administration. The fraction of Ki-67–positive cells was significantly reduced in tumors after repeat administration in all four models tested, further supporting a broadly operative antiproliferative mechanism, independent of the magnitude of MRTX849 antitumor response (Supplementary Fig. S4). Induction of apoptosis as determined by cleaved caspase-3 immunostaining was also evident on day 1 of treatment (6 and/or 24 hours after treatment) in the sensitive H358, MIA PaCa-2, and H1373 models (79%–100% maximal regression) but not in the partially sensitive H2122 model (Supplementary Fig. S4). An expanded RPPA-based pathway analysis of several models also indicated a correlation between antitumor activity of MRTX849 and decreased survivin (statistically significant at days 5/7 in 7 models evaluated; Supplementary Fig. S7B) and a trend toward increased cleaved caspase-3 induction (day 1, P = 0.08, 16 models), supporting the induction of apoptosis as a key mediator of a cytoreductive antitumor response (Supplementary Fig. S7C). Interestingly, the magnitude of reduction of MYC and cyclin B1 protein levels at days 5/7 also closely correlated with MRTX849 antitumor activity, consistent with their roles as critical regulators of KRAS-mediated cell growth and survival pathways (Supplementary Fig. S7B). Collectively, these data support that durable inhibition of ERK activity and maximal inhibition of ERK-regulated outputs including MYC and E2F-mediated transcription are associated with induction of apoptosis and maximal response to MRTX849 treatment.

CRISPR/Cas9 Screen Identifies Vulnerabilities and Modifiers of Response to MRTX849 in KRASG12C-Mutant Cancer Cell Lines In Vitro and In Vivo

The correlative analysis of genomic or proteomic markers with response to MRTX849 in the defined panel of models provided only limited insight toward mechanism of therapeutic response or resistance. Therefore, we directly interrogated the role of selected genes in mediating therapeutic response utilizing a focused CRISPR/Cas9 knockout screen targeting approximately 400 genes including many genes involved in KRAS signaling. This was conducted in H358 and H2122 cells in vitro and in H2122 xenografts in vivo in the presence and absence of MRTX849 treatment (Supplementary Fig. S9A–S9F). In MRTX849-anchored screens in vitro, single guide RNAs (sgRNA) that target RAS signaling pathway genes including MYC, SHP2 (H2122), mTOR pathway (MTOR and RPS6), and cell-cycle genes (CDK1, CDK2, CDK4/6, and RB1) were identified to affect cell fitness. sgRNAs that target KEAP1 and CBL were enriched in the H2122 model, demonstrating cell-specific genetic routes toward improved fitness through loss of classic tumor-suppressor genes, including in the context of MRTX849 treatment. KRAS sgRNA dropout was less pronounced in the MRTX849-treated cells compared with DMSO control–treated cells, as would be expected with redundant depletion of the drug target (Supplementary Fig. S9C and S9D). To evaluate whether a distinct KRAS dependence or modulation of MRTX849 therapeutic response was observed in vitro versus in vivo, xenograft-bearing mice bearing H2122 cells (∼250 mm3) transduced with the sgRNA library were orally administered vehicle or MRTX849 for 2 weeks (Supplementary Fig. S9A, S9E, and S9F). In MRTX849-treated xenografts, sgRNAs targeting cell cycle, SHP2, MYC, and mTOR pathway genes remained among the top depleted sgRNAs, demonstrating that inhibition of these targets in vivo, in the context of KRAS inhibition, leads to further tumor-growth inhibition over and above the effects of KRAS inhibition alone (Supplementary Fig. S9E and S9F). sgRNAs targeting the tumor suppressor KEAP1 were enriched in MRTX849-treated xenografts, suggesting loss of KEAP1 may represent a mechanism of intrinsic or acquired resistance. Interestingly, NRAS was one of the top enriched genes in the vehicle-treated xenografts, suggesting NRAS functions as a tumor suppressor in this context; however, enrichment was not as pronounced in the MRTX849-treated xenografts, suggesting NRAS may compensate for KRAS in the context of KRAS inhibition (Supplementary Fig. S9F). Collectively, these data demonstrate the importance of selected proteins that regulate RTK- and RAS-dependent signaling and cell-cycle transition in mediating the oncogenic effects of mutant KRAS, and also provide a catalog of potentially druggable vulnerabilities that complement KRAS blockade.

Cancer Therapeutic Combination Screen to Identify Rational and Clinically Tractable Strategies to Address Feedback and Resistance Pathways

To further interrogate pathways that mediate the antitumor response to MRTX849 and to identify combinations capable of enhancing response to MRTX849, a combination screen was conducted in vitro using a focused library of small-molecule inhibitors across a panel of cell lines (Supplementary Fig. S10A and S10B; Supplementary Table S8). Approximately 70 compounds targeting relevant pathways (RTKs, MAPK/ERK, PI3K, mTOR, cell cycle) were tested in a 3- or 7-day viability assay, and synergistic combinations were identified and ranked. Multiple hits from this screen were then identified for additional evaluation in combination studies with MRTX849, including the HER family inhibitor afatinib, the CDK4/6 inhibitor palbociclib, the SHP2 inhibitor RMC-4550, and mTOR pathway inhibitors.

Combination Strategies That Target Upstream Signaling Pathways Implicated in Extrinsic Regulation of KRAS Nucleotide Cycling and Feedback/Bypass Pathways

MRTX849 in combination with HER family inhibitors synergistically inhibited tumor-cell viability in the majority of cell lines evaluated and were the top hit in the combination screen in vitro (Supplementary Fig. S10). Cell lines with the highest (top 50th percentile) average composite baseline RNA expression values of selected HER family members exhibited the highest synergy scores to these combinations (Supplementary Fig. S11A). Afatinib was selected as a prototype HER family inhibitor based on its broad in vitro combination activity. Combination studies were conducted with MRTX849 and afatinib in five tumor models that were partially sensitive or treatment refractory to single-agent MRTX849. The MRTX849 and afatinib combination demonstrated significantly greater antitumor efficacy compared with either single agent in all five models evaluated, including multiple models exhibiting complete or near-complete responses to the combination (Fig. 6A; Supplementary Fig. S11B).

Figure 6.

 

HER family and SHP2 inhibitor combinations further inhibit KRAS signaling and exhibit increased antitumor responses. A, MRTX849 at 100 mg/kg, afatinib at 12.5 mg/kg, or the combination was administered daily via oral gavage to mice bearing the H2122 or KYSE-410 cell line xenografts (n = 5). Combination treatment led to a statistically significant decrease in tumor growth compared with either single-agent treatment. *, Padj < 0.01. B, Quantification of KRAS mobility shift and pERK in H2122 cells treated for 24 hours with MRTX849 (0.1–73 nmol/L), afatinib (200 nmol/L), or the combination assessed by Western blot densitometry. C, MRTX849 at 100 mg/kg, afatinib at 12.5 mg/kg, or the combination was administered once or daily for 7 days via oral gavage to mice bearing H2122 cell line xenografts (n = 3/group). Tumors were harvested at 6 and 24 hours following the final dose. Tumor sections were stained for pERK and pS6 via IHC methods. Quantitation of images shown by H-score in tumor tissue. Reduction of pERK or pS6 staining intensity was determined to be statistically significant relative to vehicle or either single agent using one-way ANOVA. Brackets indicate P < 0.05 compared with left-most sample. D, Quantitation of KRAS band shift and pERK after 24-hour treatment with MRTX849 (0.1–73 nmol/L), RMC-4550 (1 μmol/L), or the combination in H358 cells assessed by Western blot densitometry. E, MRTX849 at 100 mg/kg, RMC-4550 at 30 mg/kg, or the combination was administered daily via oral gavage to mice bearing the KYSE-410 or H358 cell line xenografts (n = 5/group). Combination treatment led to a statistically significant reduction in tumor growth compared with either single agent on the last day of dosing. *, Padj < 0.05. F, MRTX849 at 100 mg/kg, RMC-4550 at 30 mg/kg, or the combination was administered via oral gavage to mice bearing KYSE-410 cell line xenografts (n = 3/group), and tumors were harvested at 6 and 24 hours post-dose. Tumor sections were stained with pERK or pS6 via IHC methods. Quantitation of images shown by H-score in tumor tissue. Reduction of pERK staining intensity was determined to be statistically significant relative to RMC-4550 alone using one-way ANOVA. Brackets indicate P < 0.05 compared with left-most sample.

 

To evaluate whether afatinib affected covalent modification of KRASG12C by MRTX849, partially sensitive H2122 cells were treated with increasing concentrations of MRTX849 alone or in the presence of afatinib (200 nmol/L, IC90), and the mobility shift in KRAS protein was densitometrically determined from immunoblots. A clear shift in the concentration response to MRTX849 was apparent in the presence of afatinib, indicating that the combination increased the fraction of modified KRASG12C consistent with the putative role of HER family receptors in extrinsic regulation of KRASG12C GTP loading (Fig. 6B). The concentration–response relationship for inhibition of ERK phosphorylation was also clearly shifted in the presence of afatinib. To further evaluate the effect of the combination on KRAS-dependent signaling, four cell lines (H2030, H2122, H358, and KYSE-410) were treated with a range of MRTX849 concentrations in the presence or absence of afatinib for 6 or 24 hours, and key signaling molecules were evaluated by RPPA. Afatinib demonstrated clear inhibition of EGFR (pY1068) and HER2 (pY1248) activity and partial inhibition of ERK, AKT (S473), and p70S6K phosphorylation at both time points (Supplementary Fig. S11C). The effect of afatinib on S6 (S235/236, S240/244) and p90 RSK (S380) phosphorylation was variable and exhibited only minimal inhibition in most of the cell lines evaluated. The combination of afatinib and MRTX849 demonstrated markedly enhanced concentration-dependent inhibition and/or a greater magnitude of effect on ERK, RSK, p70 S6K, and S6 (S235/236) phosphorylation compared with MRTX849 alone at both 6 and 24 hours. Of note, neither afatinib nor MRTX849 alone inhibited S6 phosphorylation at the S240/244 site regulated by mTOR/S6K, whereas the combination demonstrated marked inhibition at 24 hours.

In vivo, the combination also exhibited a trend toward increased pERK and pS6 (S235/236) inhibition in the partially sensitive H2122 model in combination groups as determined by quantitation of immunostaining after 1- or 7-day administration (Fig. 6C). Similar results were observed in the MRTX849-refractory KYSE-410 model, and the combination also increased the number of apoptotic cells in this model (Supplementary Fig. S12A–S12C). Collectively, these data indicate that upstream baseline HER family activation may limit the ability of MRTX849 to achieve robust inhibition of the ERK and mTOR–S6 signaling pathways. Accordingly, the combination of afatinib and MRTX849 can limit feedback reactivation of ERK and demonstrate complementary inhibition of AKT–mTOR–S6 signaling, resulting in significantly improved antitumor activity.

SHP2 inhibition has been shown to inhibit the growth of cells that harbor KRASG12C mutations, and this effect is likely mediated, in part, by decreasing KRAS GTP loading (25–27). To evaluate whether SHP2 inhibition enhanced covalent modification of KRASG12C by MRTX849, H358 and H2122 cells were incubated with increasing MRTX849 concentrations with or without the SHP2 inhibitor RMC-4550. In both cell lines, cotreatment with RMC-4550 (1 μmol/L, IC90) demonstrated a MRTX849 concentration-dependent increase in KRASG12C protein modification and a concomitant decrease in ERK phosphorylation compared with MRTX849 alone (Fig. 6D; Supplementary Fig. S13A). RPPA analysis of KRAS-dependent signaling was conducted at 6 or 24 hours after treatment in three cell lines (H358, H2030, H2122) over a range of MRTX849 concentrations in the presence or absence of RMC-4550. RMC-4550 demonstrated robust inhibition of ERK phosphorylation and partial inhibition of p90 RSK (S380) and p70 S6K (T412) at both time points (Supplementary Fig. S13B). The combination of RMC-4550 and MRTX849 demonstrated incrementally increased concentration-dependent inhibition of ERK and RSK phosphorylation in all cell lines at both 6 and 24 hours and markedly improved inhibition of S6 (S235/236) phosphorylation compared with MRTX849 alone in H2122 and H358 cells at 24 hours. In addition, the combination demonstrated near-complete inactivation of KRAS in MRTX849-refractory KYSE-410 xenografts as determined using an active RAS ELISA assay, and this was significant compared with single agents (Supplementary Fig. S13C). On the basis of these findings, combination studies were conducted with MRTX849 and RMC-4550 in six KRASG12C-mutated tumor models in vivo, and the combination demonstrated significantly greater antitumor efficacy compared with either single agent in 4 of 6 models evaluated (Fig. 6E; Supplementary Fig. S13D). Consistent with the in vitro data, the combination also demonstrated a significant decrease in ERK phosphorylation compared with either single agent in the KYSE-410 model as determined by quantitation of tumor-cell immunostaining on day 1 at 6 and 24 hours and day 7 at 6 hours after dose (Fig. 6F). Together, these data indicate that EGFR family and SHP2 blockade can augment the antitumor activity of KRASG12C inhibitors through enhancing covalent target modification and establishing a more comprehensive blockade of KRAS-dependent signaling.

Combinations That Inhibit Bypass Pathways Downstream of KRAS and Exhibit Increased Antitumor Activity in Xenograft Models

KRAS is implicated in regulation of the oncogenic S6 protein translation pathway through both ERK-dependent activation of RSK, which phosphorylates S6 at Ser235/236, and cross-talk with the PI3K and mTOR pathway that additionally phosphorylates S6 at Ser240/244 (29). However, the S6 pathway can also be activated independently of mutated KRAS in tumor cells through hyperactivated RTK signaling, PI3K activation, or STK11 mutations, each of which converge on mTOR-mediated activation of S6. In the in vitro combination screen, mTOR inhibitors demonstrated synergy in a subset of evaluated cell lines (Supplementary Fig. S14A). To further evaluate the effect of the combination on KRAS and mTOR pathway–dependent signaling, four cell lines were treated with MRTX849 in the presence or absence of the selective ATP-competitive mTOR inhibitor vistusertib (1 μmol/L), for 6 or 24 hours, and several signaling molecules were evaluated by RPPA. Vistusertib demonstrated clear and robust inhibition of several components of the PI3K–mTOR signaling pathway including AKT (S473), p70 S6K (T412), S6 (pS235/236, S240/244), and 4E-BP1 (S65, T70) phosphorylation in each cell line at both time points consistent with its mechanism of action (Supplementary Fig. S14B). MRTX849 alone did not affect 4E-BP1 or S6 (S240/244) activity, and it exhibited a variable and cell line–dependent effect on p70 S6K and S6 (pS235/236) phosphorylation in these cell lines. Vistusertib also demonstrated marked induction of ERK phosphorylation, often several-fold over vehicle control, at both time points in all four cell lines, consistent with prior reports (30). The combination of vistusertib and MRTX849 demonstrated a comparable level of inhibition of ERK phosphorylation compared with single-agent MRTX849, indicating that the activation of ERK signaling by vistusertib was impeded by the combination of the two agents. In addition, MRTX849 combined with vistusertib further inhibited p70 S6K and AKT S473 phosphorylation compared with either single agent. Near-complete inhibition of S6 (S235/236, S240/244) phosphorylation at limit of detection was observed for the combination in each cell line at evaluated time points.

Consequently, a cohort of tumor models was identified, and the combination of MRTX849 with the selective mTOR inhibitor vistusertib demonstrated marked tumor regression and significantly improved antitumor activity compared with either single agent in all six models evaluated (Fig. 7A; Supplementary Fig. S14C). MRTX849 in combination with a second, differentiated mTOR inhibitor, everolimus, which inhibits TORC1 but not TORC2, in the H2030 xenograft model also demonstrated a striking combination effect (Supplementary Fig. S14D). In the KRASG12C, STK11-mutant H2030 model, MRTX849 demonstrated marked inhibition of ERK phosphorylation through 24 hours, but exhibited only partial inhibition of pS6235/36 at 6 hours after dose, on days 1 and 7 (Fig. 7B and C). Vistusertib demonstrated marked inhibition of pS6235/36 at 6 hours after treatment with evidence of recovery by 24 hours. The combination of vistusertib and MRTX849 did not have a further effect on ERK phosphorylation but demonstrated a significant reduction in pS6235/36 on day 1 at 24 hours compared with vistusertib alone and a trend toward reduced pS6235/36 on both day 1 and day 7 at 6 hours compared with either single agent (Fig. 7B; Supplementary Fig. S14E). Together, these data indicate that MRTX849 and mTOR inhibitor combination demonstrates complementary inhibition of the ERK and mTOR–S6 signaling pathways, resulting in broad antitumor activity in KRASG12C-mutant tumor models.

Figure 7.

 

CDK4/6 and mTOR combinations suppress independently hyperactivated downstream pathways and exhibit increased antitumor responses. A, MRTX849 at 100 mg/kg, vistusertib at 15 mg/kg, or the combination was administered daily via oral gavage to mice bearing the H2122 or H2030 cell line xenografts (n = 5/group). Combination treatment led to a statistically significant decrease in tumor growth compared with either single-agent treatment. *, Padj < 0.05. B, MRTX849 at 100 mg/kg, vistusertib at 15 mg/kg, or the combination was administered once or daily for 7 days via oral gavage to mice bearing H2030 cell line xenografts (n = 3/group). Tumors were harvested at 6 and 24 hours following the final dose. Tumor sections were stained with pERK and pS6 via IHC methods. Quantitation of images shown by H-score in tumor tissue. Reduction of pERK or pS6 staining intensity was determined to be statistically significant relative to vehicle or either single agent using one-way ANOVA. Brackets indicate P < 0.05 compared with left-most sample. C, Protein Western blot analysis of KRAS pathway targets in H2030 xenografts treated with MRTX849 (100 mg/kg), vistusertib (15 mg/kg), or the combination, 6 or 24 hours after a single dose. D, Protein Western blot analysis of KRAS pathway and cell-cycle targets in H2122 cells treated for 24 hours with MRTX849, palbociclib, or the combination. E, Normalized RNA-seq gene-expression data on E2F targets in H2122 xenografts treated with MRTX849, palbociclib, or the combination, 6 and 24 hours after a single daily dose or seven daily doses. F, MRTX849 at 100 mg/kg, palbociclib at 130 mg/kg, or the combination was administered daily via oral gavage to mice bearing the H2122 or SW1573 cell line xenografts (n = 5). Combination treatment led to a statistically significant decrease in tumor growth compared with either single-agent treatment. *, Padj < 0.05.

 

Signaling through KRAS is known to mediate cell proliferation, at least in part, through the regulation of the cyclin D family and triggering RB/E2F-dependent entry of cells into cell cycle. Loss-of-function mutations and homozygous deletions in the cell-cycle tumor suppressor CDKN2A (p16) are coincident in a subset of KRAS-mutant non–small cell lung cancer (NSCLC) and hyperactivate CDK4/6-dependent RB phosphorylation and cell-cycle transition. In the CDKN2A-null H2122 and SW1573 cell lines in vitro, MRTX849 demonstrated concentration-dependent partial inhibition of RB phosphorylation (pRB pS807/811) and concurrent increase in p27 in H2122 cells, but not SW1573 cells, at 24 hours (Fig. 7D; Supplementary Fig. S15A). MRTX849 in combination with the CDK4/6 inhibitor palbociclib (1 μmol/L) demonstrated near-complete inhibition of pRB in both H2122 and SW1573 cells and further induced p27 in H2122 cells. Interestingly, pS6 (S235/236) was also much more effectively suppressed by the combination in both H2122 and SW1573 cells, which is consistent with a recent report (31). RNA expression of target genes and RPPA analysis of target protein signaling events were also used as a readout of cell-cycle inhibition in the H2122 tumor model in vivo, and the combination of MRTX849 and palbociclib significantly inhibited E2F1 and selected E2F family target genes, induced p27 protein expression to a greater degree compared with either single agent, and further reduced the number of Ki-67–positive cells after 7 days of administration (Fig. 7E; Supplementary Fig. S15B and S15C). In addition, the combination demonstrated a significant decrease in pRB (S780) compared with either single agent after 7 days of administration in SW1573 tumors in vivo (Supplementary Fig. S15D). This combination also induced tumor regression in five tumor xenograft models that was significant compared with either single-agent control (Fig. 7F; Supplementary Fig. S15E). Although not significant, a trend was noted in which models with CDKN2A homozygous deletion exhibited an increased antitumor response to the combination of MRTX849 and CDK4/6 inhibition compared with models lacking evidence of genetic dysregulation of key cell-cycle genes (Supplementary Fig. S15F and S15G).

Discussion

The identification of MRTX849 as a highly selective KRASG12C inhibitor capable of near-complete inhibition of KRAS in vivo provides a renewed opportunity to better understand the role of this mutation as an oncogenic driver in various cancers and to guide rational clinical trial design. The lack of a significant correlation between sensitivity to MRTX849 antitumor activity in in vitro versus in vivo model systems made it necessary to further study KRAS oncogene dependence in tumor models in vivo, a more clinically relevant setting. The demonstration that MRTX849 exhibited significant antitumor efficacy in all evaluated KRASG12C-mutated cancer models and demonstrated marked regression in the majority (65%) confirms that this mutation is a broadly operative oncogenic driver and that MRTX849 represents a compelling therapeutic opportunity. This evidence of activity extended to patients, as demonstrated by RECIST partial responses in 2 patients enrolled in a phase I clinical trial of MRTX849. Collectively however, these data also illustrate that the degree of dependence of cancer cells on the presence of a KRASG12C mutation for growth and survival can vary across tumors and that co-occurring genetic alterations observed in KRAS-mutated cancers may influence response to direct targeted therapy. The further observation that KRAS mutations occur across different cancers and that no single co-occurring genetic alteration predicted response to treatment illustrates the genetic heterogeneity of KRAS-driven cancers. Findings in the present studies are consistent with other functional genomics or therapeutic strategies to block KRAS function across panels of cell lines or models which demonstrated a highly significant response of KRAS-mutant cells to target knockdown, a heterogeneous magnitude of response, and no clear co-occurring aberrations that predict resistance to target blockade (5, 32, 33). Interestingly, despite the implication that certain mutations that co-occur with KRAS including TP53, STK11, and KEAP1 may limit therapeutic response in KRASG12C-positive lung cancers, none of these mutations correlated with response or resistance in the cell-line panel. In addition, the partial response we reported in the patient with lung adenocarcinoma was observed in a patient harboring deleterious comutations in both STK11 and KEAP1. Together, these data further illustrate the heterogeneity and complexity of KRAS-mutated cancers and suggest that no binary co-occurring genetic event may be predictive of therapeutic response.

Temporal and dose–response analysis indicated maximal modification of KRASG12C and durable inhibition of KRAS-dependent signaling was important in maximizing therapeutic response. The recovery of ERK signaling and the inability to inhibit mTOR–S6 signaling despite continued treatment were each associated with transient or submaximal response to MRTX849. ERK1/2 is implicated in direct phosphorylation and negative feedback regulation of EGFR (T669), FGFR1 (S777), and SOS1, and each of these targets may facilitate KRASG12C-independent resetting of ERK signaling flux (34–36). The rapid and remarkable suppression of ERK pathway–regulated transcripts such as DUSP and SPRY/SPRED family members by MRTX849 in all models evaluated is consistent with that observed for RAF inhibitors and is implicated in reactivation of ERK and RTK signaling (18, 19). The dual-specificity phosphatases DUSP4 and 6 were strongly suppressed by MRTX849 and are implicated in dephosphorylating and inactivating ERK1/2 (14, 18, 37), whereas SPRY family members are implicated in the negative regulation of RTKs and adaptor proteins (e.g., GRB2), and may participate in modifying RAS family nucleotide exchange and effector binding (e.g., RAF1; ref. 38). Although suppression of DUSP and SPRY/SPRED was broadly observed in all models, the magnitude of signaling reactivation and response to MRTX849 varied across models. This suggests some tumor models harbor additional factors that bypass KRAS dependence or affect RAS pathway signaling flux, such as expression or activation of selected RTKs (e.g., ERBB2 amplification in the KYSE-410 model) or STK11 loss-of-function mutations, and may be primed for feedback reactivation of RAS-dependent signaling and/or limit the degree of signaling inhibition by MRTX849. This phenomenon was observed for BRAFV600E-mutant colon cancer (but not melanoma) which exhibits high baseline EGFR expression, is primed for rapid feedback activation of this RTK, and is resistant to single-agent inhibition but highly responsive to cotargeting BRAF (and/or MEK) and EGFR (20). In addition, blockade of BRAF or MEK1/2 resulted in feedback-mediated activation of the PI3K–mTOR signaling pathway in concert with the coactivation of upstream RTKs (e.g., EGFR), resulting in bypass of ERK pathway dependence and therapeutic resistance (17, 20, 39). The observations that baseline expression of HER family RTKs trended with MRTX849 antitumor activity and that CRISPR-based drug-anchored screens implicated EGFR, SHP2, and mTOR–S6 pathways as cotargetable vulnerabilities both support the hypothesis that these targets act as conditional response modifiers.

Activation of RTK signaling in the context of KRASG12C-mutant cancer was predicted to limit MRTX849 therapeutic response both by enhancing extrinsic regulation of GTPase activity and initiating KRAS-independent ERK and mTOR–S6 pathway activation. Therefore, HER family and SHP2 inhibition were employed as strategies to either block the critical RTK family in KRAS-mutant cells or block collective RTK signaling downstream, respectively. As MRTX849 binds only GDP-KRASG12C, both HER family and SHP2 inhibition each enhanced KRASG12C modification by MRTX849 and significantly improved antitumor activity. This observation is consistent with the putative role of activated RTKs in the engagement of SHP2 to mediate SOS1-dependent RAS GTP loading and to diminish RAS GAP activity, each of which converge on enhanced RAS activation state (40). The afatinib combination demonstrated a clear and marked inhibition of both the ERK–RSK and AKT–mTOR–S6 signaling pathways, whereas the SHP2 inhibitor combination demonstrated a clear impact on ERK–RSK signaling and a relatively less prominent impact on mTOR–S6 signaling. Although afatinib may more effectively address mTOR–S6 bypass signaling, SHP2 inhibition should be an effective combinatorial strategy to combat other RTKs outside of the HER family, such as FGFRs or MET, that could affect KRAS dependence. To further address bypass signaling mediated by RTK activation or STK11 mutations, each of which activate the mTOR–S6 signaling pathway independently of KRAS, mTOR inhibition in combination with MRTX849 was also evaluated. MRTX849 in combination with vistusertib, in fact, demonstrated significantly improved antitumor activity in vivo compared with either single agent in all six tumor models evaluated, regardless of STK11 mutational status. Consistent with the mechanism of action of vistusertib, comprehensive inhibition of AKT–mTOR–S6 signaling was observed for vistusertib alone and near-complete inhibition of pS6S235–36 and pS6240–44 was observed in combination. In addition, the marked feedback reactivation of ERK by vistusertib was relieved by the combination. The induction of ERK activity has been observed in tumor cells following mTORC1 inhibition by rapalogs or ATP-competitive inhibitors and has been implicated in limiting antitumor activity of this class of agents (30, 41, 42), supporting the suppression of ERK signaling by MRTX849 as a key mechanism of response to the combination. Notably, all three combination strategies converge on more comprehensive inhibition of KRAS-dependent signaling, converging on ERK and S6 activity. In addition, although the inhibition of the AKT–mTOR–S6 pathway did not correlate with model response to MRTX849 (potentially due to tumor heterogeneity), the observations that both MTOR and RPS6 drop out in drug-anchored CRISPR screens and that effective combination strategies more comprehensively block this pathway illustrate its likely importance in maximizing therapeutic response in KRAS-mutated cancers.

Cell-cycle dysregulation due to genetic alterations in cell-cycle regulators identified additional factors that could modify the therapeutic response to MRTX849. In addition, CDKN2A, RB1, CDK4, and CDK6 were all identified as gene targets that affected cell fitness in CRISPR screens. Genetic alterations including homozygous deletion of CDKN2A or amplification of CDK4 or CCND1 comprise up to 20% of KRAS-mutated NSCLC (43). Combination studies with MRTX849 and palbociclib in vivo demonstrated more comprehensive inhibition of RB and E2F family target genes and increased antitumor activity compared with either single agent in NSCLC models. In addition, these studies indicated that the combination resulted in more effective inhibition of S6 (S235/236) phosphorylation, establishing a previously unappreciated connection between cell-cycle blockade and protein translation pathways. Notably, this combination was especially effective in CDKN2A-deleted models, suggesting that this combination strategy may be primarily beneficial in a molecularly defined subset of patients characterized by decoupling of cell-cycle regulation from KRAS.

Collectively, models exhibiting a cytoreductive response to single-agent MRTX849 demonstrated a more comprehensive and durable inhibition of KRAS-dependent signaling and induction of an apoptotic response. These data suggest that maintaining durable inhibition of KRAS-dependent signaling below a defined threshold is required to elicit tumor regression. The elucidation of mechanisms that limit the therapeutic response to single-agent KRAS inhibition has provided insight toward strategies to enhance therapeutic activity in KRAS-mutant tumors. Of the 35% of models (9/26) that did not exhibit durable regression with single-agent MRTX849 treatment, five models (KYSE410, SW1573, H2122, H2030, and LU6405) were selected for rational combination studies, and at least one combination demonstrated significant improvement in antitumor efficacy and elicited a >50% tumor regression in all five models evaluated. These results suggest that essentially all KRASG12C-mutated cancers can derive clinical benefit from direct KRAS inhibitor–directed therapy either alone or in combination. Furthermore, rational pathway-centric combination regimens directed at hallmark signaling nodes may be directed to genetically defined patient subsets. For example, KRAS-mutated NSCLC exhibits mutually exclusive, co-occurring genetic alterations in STK11 and CDKN2A (43). The present data suggest that KRASG12C/STK11-mutated NSCLC could be readily addressed by combining a KRASG12C inhibitor with an RTK or mTOR inhibitor, whereas KRASG12C/CDKN2A-mutated NSCLC could be more effectively addressed by combination with a CDK4/6 inhibitor. Collectively, the present studies support the broad utility of covalent KRASG12C inhibitors in treating KRASG12C-mutated cancers and provide defining strategies to identify patients likely to benefit from single-agent therapy or rationally directed combinations.

  

UPDATED 02/07/2021

The November 1st issue of Science highlights a series of findings which give cancer researchers some hope in finally winning a thirty year war with the discovery of drugs that target KRAS, one of the most commonly mutated oncogenes  (25% of cancers), and thought to be a major driver of tumorigenesis. Once considered an undruggable target, mainly because of the smooth surface with no obvious pockets to fit a drug in, as well as the plethora of failed attempts to develop such an inhibitor, new findings with recently developed candidates, highlighted in this article and other curated within, are finally giving hope to researchers and oncologists who have been hoping for a clinically successful inhibitor of this once considered elusive target.

For a great review on development of G12C KRas inhibitors please see Dr. Hobb’s and Channing Der’s review in Cell Selective Targeting of the KRAS G12C Mutant: Kicking KRAS When It’s Down

Figure 1Mechanism of Action of ARS853 showing that the inhibitors may not need bind to the active conformation of KRAS for efficacy

Abstract: Two recent studies evaluated a small molecule that specifically binds to and inactivates the KRAS G12C mutant. The new findings argue that the perception that mutant KRAS is persistently frozen in its active GTP-bound form may not be accurate.

Although the development of the KRASG12C-specific inhibitor, compound 12 (Ostrem et al., 2013), was groundbreaking, subsequent studies found that the potency of compound 12 in cellular assays was limited (Lito et al., 2016, Patricelli et al., 2016). A search for more-effective analogs led to the development of ARS853 (Patricelli et al., 2016), which exhibited a 600-fold increase of its reaction rate in vitro over compound 12 and cellular activities in the low micromolar range.

A Summary and more in-depth curation of the Science article is given below:

After decades, progress against an ‘undruggable’ cancer target

Summary

Cancer researchers are making progress toward a goal that has eluded them for more than 30 years: shrinking tumors by shutting off a protein called KRAS that drives growth in many cancer types. A new type of drug aimed at KRAS made tumors disappear in mice and shrank tumors in lung cancer patients, two companies report in papers published this week. It’s not yet clear whether the drugs will extend patients’ lives, but the results are generating a wave of excitement. And one company, Amgen, reports an unexpected bonus: Its drug also appears to stimulate the immune system to attack tumors, suggesting it could be even more powerful if paired with widely available immunotherapy treatments.

Jocelyn Kaiser. After decades, progress against an ‘undruggable’ cancer target. Science  01 Nov 2019: Vol. 366, Issue 6465, pp. 561 DOI: 10.1126/science.366.6465.561

The article highlights the development of three inhibitors: by Wellspring Biosciences, Amgen, and Mirati Therapeutics.

Wellspring BioSciences

In 2013, Dr. Kevan Shokat’s lab at UCSF discovered a small molecule that could fit in the groove of the KRAS mutant G12C.  The G12C as well as the G12D is a common mutation found in KRAS in cancers. KRAS p.G12C mutations predominate in NSCLC comprising 11%–16% of lung adenocarcinomas (45%–50% of mutant KRAS is p.G12C) (Campbell et al., 2016; Jordan et al., 2017), as well as 1%–4% of pancreatic and colorectal adenocarcinomas, respectively (Bailey et al., 2016; Giannakis et al., 2016).  This inhibitor was effective in shrinking, in mouse studies conducted by Wellspring Biosciences,  implanted tumors containing this mutant KRAS.

See Wellspring’s news releases below:

March, 2016 – Publication – Selective Inhibition of Oncogenic KRAS Output with Small Molecules Targeting the Inactive State

 

February, 2016 – Publication – Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism

 

Amgen

Amgen press release on AMG510 Clinical Trial at ASCO 2019

THOUSAND OAKS, Calif., June 3, 2019 /PRNewswire/ — Amgen (NASDAQ: AMGN) today announced the first clinical results from a Phase 1 study evaluating investigational AMG 510, the first KRASG12C inhibitor to reach the clinical stage. In the trial, there were no dose-limiting toxicities at tested dose levels. AMG 510 showed anti-tumor activity when administered as a monotherapy in patients with locally-advanced or metastatic KRASG12C mutant solid tumors. These data are being presented during an oral session at the 55th Annual Meeting of the American Society of Clinical Oncology (ASCO) in Chicago.

“KRAS has been a target of active exploration in cancer research since it was identified as one of the first oncogenes more than 30 years ago, but it remained undruggable due to a lack of traditional small molecule binding pockets on the protein. AMG 510 seeks to crack the KRAS code by exploiting a previously hidden groove on the protein surface,” said David M. Reese, M.D., executive vice president of Research and Development at Amgen. “By irreversibly binding to cysteine 12 on the mutated KRAS protein, AMG 510 is designed to lock it into an inactive state. With high selectivity for KRASG12C, we believe investigational AMG 510 has high potential as both a monotherapy and in combination with other targeted and immune therapies.”

The Phase 1, first-in-human, open-label multicenter study enrolled 35 patients with various tumor types (14 non-small cell lung cancer [NSCLC], 19 colorectal cancer [CRC] and two other). Eligible patients were heavily pretreated with at least two or more prior lines of treatment, consistent with their tumor type and stage of disease. 

Canon, J., Rex, K., Saiki, A.Y. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019) doi:10.1038/s41586-019-1694-1

Besides blocking tumor growth, AMG510 appears to stimulate T cells to attack the tumor, thus potentially supplying a two pronged attack to the tumor, inhibiting oncogenic RAS and stimulating anti-tumor immunity.

Mirati Therapeutics

Mirati’s G12C KRAS inhibitor (MRTX849) is being investigated in a variety of solid malignancies containing the KRAS mutation.

For recent publication on results in lung cancer see Patricelli M.P., et al. Cancer Discov. 2016; (Published online January 6, 2016)

For more information on Mirati’s KRAS G12C inhibitor see https://www.mirati.com/pipeline/kras-g12c/

KRAS G12C Inhibitor (MRTX849)

Study 849-001 – Phase 1b/2 of single agent MRTX849 for solid tumors with KRAS G12C mutation

Phase 1b/2 clinical trial of single agent MRTX849 in patients with advanced solid tumors that have a KRAS G12C mutation.

See details for this study at clinicaltrials.gov

UPDATED 02/07/2021

Amgen scientists’ rapid work to challenge the undruggable KRAS G12C mutation in cancer

Inside a 40-year quest to challenge the KRAS G12C mutation in cancer
By Amgen Oncology
Amgen’s sotorasib, an investigational lung cancer treatment, has been submitted to the FDA for review

 

 

Nearly four decades have passed since researchers first identified the RAS gene family, which includes HRAS, NRAS and KRAS. RAS is the most frequently mutated family of oncogenes – or potentially cancerous genes – in human cancers.1,2 While research  efforts have been able to identify and develop treatments for other driver gene mutations that contribute to cancer growth, success with treating KRAS, the most frequently mutated variant of the RAS family, has remained elusive.2 But now there is hope.

Amgen, a leading biotechnology company, has taken on one of the toughest challenges of the last 40 years in cancer research.3 Chemical biologist Kevan Shokat’s lab at the University of California, San Francisco, identified a small molecule that could slip into a groove on a KRAS mutation called G12C in 2013.4 Building on their own research strategies and this new insight, scientists at Amgen used structural biology and medicinal chemistry to identify an adjacent groove, and by November 2017, made the initial decision to advance the molecule that would become investigational sotorasib.5

KRAS G12C is the most common KRAS mutation in NSCLC.6,7 In the U.S., about 13% of patients with NSCLC harbor the KRAS G12C mutation.8 There is a high unmet need and poor outcomes in the second-line treatment of KRAS G12C-driven non-small cell lung cancer (NSCLC) and, currently, there are no KRASG12C targeted therapies approved.2 

According to Amgen’s head of research and development David Reese, “the company’s scientists had an idea some time ago that the future of oncology would be led by the marriage of immuno-oncology and precision therapy. We wanted to go after high value targets, and RAS proteins are one of them.”

Because of this effort to rapidly accelerate the speed of innovation, investigational sotorasib entered clinical trials in humans less than 12 months.

 

 

At the same time that scientists discovered investigational sotorasib, the team was undertaking a project to map out every step it takes to progress a potential new treatment from an idea in a lab to being made available for patients. The goal was to shrink timelines and eliminate gaps to develop drugs more rapidly in order to reach patients with serious illnesses like NSCLC as quickly as possible.

Because of this effort to rapidly accelerate the speed of innovation, sotorasib entered clinical trials in humans less than 12 months.5 Sotorasib was the first investigational KRASG12C inhibitor to enter the clinic, and is now being studied in the broadest clinical program exploring 10 combinations with global investigational sites spanning five continents.9 In a little more than two years, the sotorasib clinical program has established a clinical data set of more than 700 patients studied across 13 tumor types.9

The investigational treatment was recently submitted to the FDA for review and was granted Breakthrough Therapy designation, a distinction designed to expedite the development and review of drugs.5 It was also accepted into the FDA’s Real-Time Oncology Review pilot program, which aims to explore a more efficient review process.5

To learn more about Amgen and how the speed of innovation is bringing new oncology treatments to patients with high unmet needs, visit Amgen.com/KnowKRAS.  

______________________

1 Ryan MB, et al. Nat Rev Clin Oncol. 2018;15:709-720.

2 Cox AD, et al. Nat Rev Drug Discov. 2014;13:828-851.

3 Kim D, et al. Cell. 2020. doi:10.1016/j.cell.2020.09.044.

4 Ostrem JM, et al. Nature. 2013 ; 503 :548-551.

5 AMGEN, 2020. Retrieved January 8, 2021, from https://www.amgen.com/stories/2020/12/rapidly-advancing-development-of-amgens-investigational-kras-g12c-inhibitor

6 Pakkala S, et al. JCI Insight. 2018;3:e120858.

7 Arbour KC, et al. Clin Cancer Res. 2018;24:334-340.

8 Amgen, Data on File. 2020.

9 ClinicalTrials.gov. NCT04185883, NCT04380753, NCT03600883, NCT04303780. https://clinicaltrials.gov/ct2/. Accessed January 20, 2020.

 
 
Members of the editorial and news staff of the USA TODAY Network were not involved in the creation of this content.
 
 
 

Additional References:

Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism.

Lito P et al. Science. (2016)

Targeting KRAS Mutant Cancers with a Covalent G12C-Specific Inhibitor.

Janes MR et al. Cell. (2018)

Potent and Selective Covalent Quinazoline Inhibitors of KRAS G12C.

Zeng M et al. Cell Chem Biol. (2017)

Campbell, J.D., Alexandrov, A., Kim, J., Wala, J., Berger, A.H., Pedamallu, C.S., Shukla, S.A., Guo, G., Brooks, A.N., Murray, B.A., et al.; Cancer Genome Atlas Research Network (2016). Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat. Genet.48, 607–616

Jordan, E.J., Kim, H.R., Arcila, M.E., Barron, D., Chakravarty, D., Gao, J., Chang, M.T., Ni, A., Kundra, R., Jonsson, P., et al. (2017). Prospective comprehensive molecular characterization of lung adenocarcinomas for efficient patient matching to approved and emerging therapies. Cancer Discov. 7, 596–609.

Bailey, P., Chang, D.K., Nones, K., Johns, A.L., Patch, A.M., Gingras, M.C., Miller, D.K., Christ, A.N., Bruxner, T.J., Quinn, M.C., et al.; Australian Pancreatic Cancer Genome Initiative (2016). Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52.

Giannakis, M., Mu, X.J., Shukla, S.A., Qian, Z.R., Cohen, O., Nishihara, R., Bahl, S., Cao, Y., Amin-Mansour, A., Yamauchi, M., et al. (2016). Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865.

Read Full Post »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

One of the most contagious diseases known to humankind, measles killed an average of 2.6 million people each year before a vaccine was developed, according to the World Health Organization. Widespread vaccination has slashed the death toll. However, lack of access to vaccination and refusal to get vaccinated means measles still infects more than 7 million people and kills more than 100,000 each year worldwide as reported by WHO. The cases are on the rise, tripling in early 2019 and some experience well-known long-term consequences, including brain damage and vision and hearing loss. Previous epidemiological research into immune amnesia suggests that death rates attributed to measles could be even higher, accounting for as much as 50 percent of all childhood mortality.

 

Over the last decade, evidence has mounted that the measles vaccine protects in two ways. It prevents the well-known acute illness with spots and fever and also appears to protect from other infections over the long term by giving general boost to the immune system. The measles virus can impair the body’s immune memory, causing so-called immune amnesia. By protecting against measles infection, the vaccine prevents the body from losing or “forgetting” its immune memory and preserves its resistance to other infections. Researchers showed that the measles virus wipes out 11% to 73% of the different antibodies that protect against viral and bacterial strains a person was previously immune to like from influenza to herpes virus to bacteria that cause pneumonia and skin infections.

 

This study at Harvard Medical School and their collaborators is the first to measure the immune damage caused by the virus and underscores the value of preventing measles infection through vaccination. The discovery that measles depletes people’s antibody repertoires, partially obliterating immune memory to most previously encountered pathogens, supports the immune amnesia hypothesis. It was found that those who survive measles gradually regain their previous immunity to other viruses and bacteria as they get re-exposed to them. But because this process may take months to years, people remain vulnerable in the meantime to serious complications of those infections and thus booster shots of routine vaccines may be required.

 

VirScan detects antiviral and antibacterial antibodies in the blood that result from current or past encounters with viruses and bacteria, giving an overall snapshot of the immune system. Researchers gathered blood samples from unvaccinated children during a 2013 measles outbreak in the Netherlands and used VirScan to measure antibodies before and two months after infection in 77 children who’d contracted the disease. The researchers also compared the measurements to those of 115 uninfected children and adults. Researchers found a striking drop in antibodies from other pathogens in the measles-infected children that clearly suggested a direct effect on the immune system resembling measles-induced immune amnesia.

 

Further tests revealed that severe measles infection reduced people’s overall immunity more than mild infection. This could be particularly problematic for certain categories of children and adults, the researchers said. The present study observed the effects in previously healthy children only. But, measles is known to hit malnourished children much harder, the degree of immune amnesia and its effects could be even more severe in less healthy populations. Inoculation with the MMR (measles, mumps, rubella) vaccine did not impair children’s overall immunity. The results align with decades of research. Ensuring widespread vaccination against measles would not only help prevent the expected 120,000 deaths that will be directly attributed to measles this year alone, but could also avert potentially hundreds of thousands of additional deaths attributable to the lasting damage to the immune system.

 

References:

 

https://hms.harvard.edu/news/inside-immune-amnesia?utm_source=Silverpop

 

https://science.sciencemag.org/content/366/6465/599

 

www.who.int/immunization/newsroom/measles-data-2019/en/

 

https://www.ncbi.nlm.nih.gov/pubmed/20636817

 

https://www.ncbi.nlm.nih.gov/pubmed/27157064

 

https://www.ncbi.nlm.nih.gov/pubmed/30797735

 

Read Full Post »

Nano-guided cell networks as conveyors of molecular communication

Nature Communications
6,
Article number:
8500
doi:10.1038/ncomms9500
Received
07 March 2015
Accepted
28 August 2015
Published
12 October 2015

Abstract

Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and ‘binned’ responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a ‘bio-litmus’ in a manner read by simple optical means.

At a glance

Figures

View all figures

left

  1. Nano-guided cell networks for processing molecular information.
    Figure 1
  2. Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.
    Figure 2
  3. Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.
    Figure 3
  4. Affinity-based probing for functional analysis of AI-2-induced protein expression.
    Figure 4
  5. Single and multi-population cell responses to autoinducer-2.
    Figure 5
  6. Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.
    Figure 6
  7. Extension of nano-guided cell networks for hypothetical regulatory structures.
    Figure 7

right

Introduction

It has become increasingly apparent that a wealth of molecular information exists, which, when appropriately accessed, can provide feedback on biological systems, their componentry and their function. Thus, there is a developing niche that transcends length scales to concurrently recognize molecular detail and at the same time provide understanding of the overall system1, 2. An emerging scheme is to develop nano- to micro-scaled tools that intimately engage with biological systems through monitoring and interacting at the molecular level, with synthetic biology being one such tool3, 4, 5, 6, 7.

While synthetic biology is often viewed as an innovative means for ‘green’ product synthesis through the genetic rearrangement of cells, their biosynthetic capabilities and their regulatory networks can instead be tuned for executive function8, 9, 10. That is, cells can be rewired to survey molecular space3, 11, 12 as they have sophisticated capabilities to recognize, amplify and transduce chemical information13. Further, they provide a means to connect biological systems with traditional microelectronic devices and in doing so present a potential interface between chemically based biomolecular processing and conventional vectors of information flow, such as electrons and photons14, 15, 16. Specifically, through engineered design, cell-based molecular processing can be further coupled to enable external abiotic responses. Cells, then, represent a versatile means for mediating the molecular ‘signatures’ common in complex environments, or in other words, they are conveyors of molecular communication17, 18, 19.

Further, beyond clonal cell-based sensors, there is an emerging concept of population engineering to establish microorganisms in deliberate networks that enable enriched system identification through a combination of distinctive yet coexistent behaviours, including, perhaps, competitive or cooperative features8, 20, 21, 22, 23, 24, 25. We posit the use of cell populations assembled in parallel¸ where multiple microbes with distinct molecular recognition capabilities work congruently. An advantage is that populations, as opposed to few cells, can facilitate thorough sampling since the presence of many cells increases their spatial breadth and per-cell data contributions (Fig. 1a). Each cellular unit undergoes independent decision-making and contributes a datum to its entire constituency. The prevalence of data provided within the population, then, substantiates a collective output by the system based on the molecular landscape. As follows in a multi-population system, molecular input thus influences the outcomes of each population, and elicits plural responses when the molecular input ranges overlap the ranges of the sensing populations21, which can define classification boundaries (Fig. 1a). Cell-mediated classification was posited in silico by Didovyk et al.21, where reporter libraries with randomized sensitivities to a molecular cue elicit concentration-dependent fluorescent patterns and these are elucidated by population screening . In the present construct, multiple populations enable multiplexed analysis, resulting, here, in a response gradation that is designed to index the molecular input ‘signature’. Consequently, the fed-back information becomes transfigured beyond a dose-dependent cell-by-cell analysis. That is, the output is predicated by the comparison between the populations rather than accumulation of response within a total population.

Figure 1: Nano-guided cell networks for processing molecular information.

Nano-guided cell networks for processing molecular information.

(a) Biotic (multicellular) processing is facilitated by cell recognition, signal transduction and genetic response. The genetically encoded response reflects the identity and prevalence of the target molecule(s). Biotic processing includes both increased cell number of responders and their genetically tuned response patterns. (b) Abiotic processing, used in conjunction with biotic processing, adds dimensionality to cell-based output by modifying through a physical stimulus (in our example, magnetic focusing). (c) Schematic of a cell population and nanomaterial-based network comprising both biotic (green/red axis) and abiotic (black axis) processing mechanisms. This conceptual system interprets molecular information by intercepting diverse molecular inputs, processes them autonomously through independent cell units within the system and refines output to include positive responders that are viewed via orthogonal means (visual classification). The system’s hierarchical structure allows molecular information to be refined into categorized collective outputs.

With population engineering as a premise for enriched molecular information processing, we engineered cell species, each to achieve an appropriate output through genetic means. There is conceptual basis for incorporation into networks, such as through mobile surveillance and position-based information relay26, 27. Hence, it is conceivable that, in addition to autonomous molecular recognition and processing afforded by synthetic biology, the use of physical stimuli to enable cell response could confer similar networking properties28, 29. For example, the complete information-processing ‘repertoire’ can be expanded beyond specific cell responses by the integration of external stimuli that serve to collate cell populations30. Specifically, we envision integration of nanomaterials that enable co-responses to molecular inputs, such that cell populations employ traditional reporting functions, that is, fluorescence marker expression, as well as responses that enable additional processing via the integration of stimuli-responsive abiotic materials (Fig. 1b).

In our example, cells are engineered to respond by permitting the attachment of magnetic nanoparticles (mNPs), such that each fluorescent cell becomes receptive to a magnetic field. Thus, the combination of cell-nanoparticle structures provides further dimensionality for the conveyance of molecular information (via magnetic stimulation). That is, without magnetic collation the fully distributed system would harbour diffuse responses; a magnetically stimulated system results in acute output due to a filtering and focusing effect (Fig. 1b)31, 32, allowing binned information to be readily, and fluorescently, conveyed.

The detection and interpretation of signalling molecules in our example is based on a microbial communication process known as quorum sensing (QS). The molecules, autoinducers (AIs), are secreted and perceived within a microbial community; once accumulated, the AI level indicates that the population size has reached a ‘quorum’33, 34. By surpassing a threshold concentration, the AI signalling coordinates population-wide phenotypic changes35. We have designed a QS information processor that utilizes two cell populations to independently interrogate natural microbial communities and generate information about QS activity by accessing AI-2 (ref. 36). Each cell population becomes ‘activated’ in response to a characteristic AI-2 level by expressing a fluorescent marker and a streptavidin-binding peptide (SBP) on the outer membrane38. SBP provides a means for collating data by binding mNPs that are introduced into the community. Using a post-processing magnetic sweep, the system as a whole interprets a molecular landscape and refines output into colour-categorized, or ‘binned,’ states (no fluorescence, red, or red and green) through (1) parallel population processing and (2) acute focusing (Fig. 1c).

The use of engineered cells as data-acquiring units and selectively equipping each with functional nanomaterials to form a redistributable processing system merges two paradigms: decentralized, active probing at a molecular scale and self-organization of units through structured dependencies on stimuli42. The population-based system overall contributes categorized feedback about a biological environment.

Results

Surface expression of SBP and fluorescent protein fusions

First, we established expression of a fusion protein consisting of a fluorescent marker (enhanced green fluorescent protein (eGFP) and variants) and SBP. Importantly, for SBP to function as a coupling agent between cells and mNPs, we used AIDAc (kindly shared by J. Larssen)40 to export the chimeric protein to Escherichia coli’s outer surface. Translocation to a cell’s surface utilizes a signal peptide (for inner membrane translocation) and AIDAc as an outer membrane autotransporter pore38, 39, 40, 41, with the passenger protein linked to each. In Fig. 2a, we depict expression of three different constructs using Venus, eGFP and mCherry for optical transmission, and the AIDAc translocator domain for surface localization. These constructs are mapped inSupplementary Fig. 1. After induction with isopropyl B-D-1-thiogalactopyranoside (IPTG), cultures were probed for surface expression of the SBP portion of the tagged fluorescent protein. Cells were incubated with fluorescently labelled streptavidin; the fluorophore of the streptavidin probe was orthogonal to the expressed fluorescent protein. The multiple fluorescence emissions were analysed by confocal microscopy without spectral overlap. The fraction of cells (fc) that exhibit colocalized fluorescent protein and the fluorescently-labeled streptavidin is reported in Fig. 2b, showing that SBP–Venus cells bound streptavidin at a slightly lower frequency than SBP–mCherry and SBP–eGFP, which exhibited statistically similar fractions (fc=0.7).

Figure 2: Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

Cells express functional, interchangeable protein components indicating both fluorescence and ability for streptavidin-linked surface coupling.

(a) A T7 cassette was used to express chimeric proteins consisting of a membrane autotransporter domain (AIDAc), one of several fluorescent proteins and a streptavidin-binding peptide (SBP). Fluorophore-tagged streptavidin (SA) was used to bind SBP. (b) Of cells expressing fluorescent proteins (FP), those also marked by SBP coupling are represented as a ‘colocalized fraction (fc),’ plotted with image analysis-based s.d. of at least five replicates. The asterisk ‘*’ denotes fc that +SBP–eGFP and+SBP–mCherry are statistically equivalent (fc~0.7) by t-test and greater than +SBP–Venus cells. (c) Composite images show cell fluorescence (Column I) from the fluorescent protein (FP); labelled streptavidin using orthogonal filter sets (Column II); and an overlay of both (Column III). Arrows indicate representative cells with strong colocalization. Plotted in Column IV are the fluorescence mean grey values (y-axis) from a representative horizontal slice of the composite image (x-axis). Vertical bars displayed between Columns III and IV identify the position of each analysed slice. Arrows indicate peaks that match the highlighted cells in Column III. fc values are noted. Fluorophores with non-overlapping spectra were paired. Row 1, Venus expression (yellow-green) was paired with Dylight405-labelled SA (blue). Row 2, eGFP expression (green) was paired with Alexafluor594-labeled SA (red). Row 3, mCherry expression (red) was paired with Alexafluor488-labeled SA (green). Scale bar in lower left, 50 μm.

That is, microscopy results related to the colocalization analysis are depicted for pairings of Venus and blue-streptavidin (SA), eGFP and red-SA, and mCherry and green-SA (Fig. 2c). Strong signals were observed in both filter sets (the fluorescent protein (Column I) and the labelled streptavidin (Column II)). Overlaying each image reveals colocalization, as indicated in Column III, where arrows point to examples of strong colocalization. In addition, Column IV plots fluorescence intensities across horizontal sections of the images, where cells that exhibit colocalized fluorescence are indicated by superimposed peaks. For +pSBP–Venus cells, those with both a blue and yellow signal are observed as pale blue–violet in the overlaid image. Cells with +pSBP–eGFP and +pSBP–mCherry and labelled streptavidin emit both green and red signals; their colocalization appears yellow. Controls shown in Supplementary Fig. 2, verify that fluorescent streptavidin (all colours) has specificity for only SBP-expressing cells over negative controls. Colocalization indicates that not only are both components of the fusion, SBP and the fluorescent protein, expressed, but that SBP is accessible to bind streptavidin on the cell’s surface. This is the first use of AIDAc for cell surface anchoring of fluorescent proteins, each having been functionalized with an affinity peptide.

Cell hybridization via mNPs

Given that expression of a fluorescent protein tagged with SBP enabled external binding of streptavidin, we employed this interaction for fastening streptavidin-functionalized materials directly to the cell surface. We chose streptavidin-conjugated mNPs, 100 nm in diameter (an order of magnitude smaller than a cell), for binding to a cell surface (Fig. 3a) to impart the abiotic magnetic properties. Scanning electron microscopy (SEM) was used to observe surface interaction between cell surface-expressing SBP and streptavidin-functionalized mNPs. Supplementary Fig. 3a,bshows electron micrographs of E. coli cells (dimensions 1.5–2 μm in length) and the mNPs (~100 nm in diameter). The SEM image in Fig. 3b, shows a magnetically isolated SBP-expressing cell with streptavidin-mNPs. The sample was prepared by mixing SBP-expressing cells with streptavidin-mNPs, then collecting or ‘focusing’ into a magnetized pellet via magnetic field, then separating from unbound cells in the supernatant. The cells were then washed and resuspended. In Fig. 3b, clusters of surface-bound mNPs are observed. In addition, the elemental composition was analysed with energy-dispersive X-ray spectroscopy, shown in Fig. 3c by an element map superimposed with carbon (red) and iron (green). While the cell appears to be of a uniform carbon composition, the particles localized at the cell surface (highlighted with arrows) were found having a strong iron composition; thus, elemental analysis confirmed particle identity as iron oxide mNPs. Additional characterization of magnetic functionality, including detailed SEM and fluorescent microscopic analysis prior to and after application of magnetic fields, is described in theSupplementary Information (Supplementary Fig. 3).

Figure 3: Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

Cells equipped with magnetic nanoparticles (mNPs) via streptavidin-mediated interaction with surface-expressed proteins.

(a) Cell surface binding of streptavidin-conjugated magnetic nanoparticles occurs via surface-anchored streptavidin-binding peptide (SBP). The fusion of T7-expressed SBP-fluorescent protein (FP)-AIDAc enables the cell surface accessibility. (b) Scanning electron micrograph of an E. coli cell with surface-bound particles. (c) Element map of carbon (red) and iron (green) through energy-dispersive spectroscopy.

In sum, the well-known affinity interaction between streptavidin and the peptide SBP is harnessed to endow cells with non-natural abiotic properties. Here coupling a functionalized nanomaterial to the surface-displayed peptide physically extends the fusion protein and also adds physical (magnetic) functionality to the cell.

Linking expression to AI-2 recognition

The expression system for pSBP–Venus was then put under AI-2 control so that the protein is expressed in the presence of AI-2 instead of IPTG. That is, we coupled the native QS signal transduction circuitry to the reporter cassette. To ensure ample expression (as the native operon is fairly weak), we placed expression of T7 RNA polymerase under control of the natural QS circuitry43. Phosphorylated AI-2 activates the system through derepression of the regulator LsrR, naturally upregulating AI-2 import and phosphorylation44, and, by design, the T7 RNA polymerase on a sensor plasmid43. When sbp–Venus is included downstream of a T7 promoter region on a second plasmid, expression is then triggered by AI-2 uptake (Supplementary Fig. 4a). Then, we used two host sensor strains engineered to provide varied AI-2 sensitivity (denoted responders ‘A’ and ‘B’). In ‘A’, lsrFG, genes required for internally phosphorylated AI-2 degradation45, 46 are deleted. Also, both strains lack the terminal AI-2 synthase, luxS, so they cannot produce AI-2 and, instead, must ‘receive’ AI-2 from an external source (Supplementary Fig. 4a). The phenotypic difference between A and B is the threshold level of AI-2 that activates the genetic response47, 48. Fully constructed, these cells are designed to take up and process AI-2 to generate fluorescence output (that co-functions with streptavidin binding).

We next evaluated the kinetics of surface-fusion protein expression and effects on cell growth. The AI-2-induced expression for AIDAc-linked and SBP-tagged fluorescent proteins did not alter growth kinetics for either cell type (Supplementary Fig. 4b,c). Expression efficacy was also evaluated via immunoassay of the outer membrane, probing for AI-2-induced surface display. After induction with 20 μM AI-2, extracts from cell types A and B were size-separated and blotted using alkaline phosphatase-conjugated streptavidin to probe for the SBP-tagged protein fusion (Supplementary Fig. 5). The 88 kDa AIDAc–Venus–SBP protein was only found in the membrane-containing pellet fraction (Fig. 4a). Analogously, protein orientation was assessed by immunolabeling the fluorescent protein. Cell type B transformed with pSBP–eGFP was induced with 20 μM AI-2 overnight; cell surfaces were then probed for eGFP using a mouse anti-GFP primary antibody and red-labelled secondary anti-mouse IgG. Simultaneously, cells were observed using phase contrast and fluorescence confocal microscopy. We noted a punctate pattern for eGFP, which was in one-to-one correspondence with red immunostaining of the surface-expressed protein. The positive staining of eGFP-expressing cells for red fluorescence, contrasted by the absence of negative control immunostaining indicated surface exposure of the fusion (Supplementary Fig. 6). Confocal microscopy confirmed precise colocalization of the eGFP and red-labelled antibodies within the confines of individual cells (Fig. 4b). Therefore, efficient transport of this functionality to the membrane under AI-2 induction was demonstrated in each host.

Figure 4: Affinity-based probing for functional analysis of AI-2-induced protein expression.

Affinity-based probing for functional analysis of AI-2-induced protein expression.

(a) 64–82 kDa region of western blot for pelleted (P) and supernatant (S) protein fractions isolated from Type A and B cells. Alkaline phosphatase-conjugated streptavidin was used to target AIDAc–Venus–SBP at expression timepoints. Arrows indicate the expected position of the full fusion protein. (b) Immunostaining for assessment of the fluorescent protein surface accessibility. The external surfaces of cells expressing AIDAc–eGFP–SBP were probed with an anti–eGFP and Alexafluor594-labelled antibody pair. A representative overlaid fluorescence and phase contrast image is shown along with fluorescence images of the green (G) and red (R) filters for the boxed-in region. Scale bar, 2 μM.

Establishing molecular ranges for cell interrogation

Importantly, the engineered cells each provide a characteristic response to the level of AI-2. Recently, we showed that AI-2 level influences the quorum size of responding engineered populations but does not alter the expression level within each quorum47. Here we evaluated our engineered AI-2 responders, again for quorum size (or in other words, percentage of AI-2-responsive cells in the population), this time varying the compositions of molecular input and the configuration of responders (Fig. 5a). First, we added AI-2, synthesized in vitro, to each of the two responder populations (Fig. 5b). We also added conditioned medium (CM), the spent medium from an AI-2 producer culture containing metabolic byproducts, as well as AI-2 (refs 36, 49; Fig. 5c). We also mixed the responder populations and added AI-2 to gauge responses in complex cultures (Fig. 5d).

Figure 5: Single and multi-population cell responses to autoinducer-2.

Single and multi-population cell responses to autoinducer-2.

(a) Fluorescence output is linked to small molecule input, derived from purified or crude sources. Fluorescence from Responders A and B was analysed after exposure to autoinducer-2 (AI-2) in mono and mixed culture environments. (b) Venus expression from in vitro-synthesized AI-2 added to monocultures of A and B. (c) Venus expression from conditioned media (CM) added to monocultures of A and B. CM was isolated from WT W3110 E. coli cultures sampled at indicated OD. Data are averages from triplicate cultures with s.d. indicated. (d) Red and green fluorescence responses to AI-2 during co-incubation of Responders A (pSBP–mCherry+, red) and B (pSBP–eGFP+, green). Representative fluorescence images show colocalization of red and green cells. Scale bar, 10 μm. The average cell count per responder cell is plotted against AI-2 concentration, as determined by image analysis in quadruplicate. All data are plotted as averages of at least triplicate samples with s.d.

Specifically, in Fig. 5b, A and B populations were incubated at mid-exponential phase with in vitro-synthesized AI-2 (refs 50, 51) at concentrations: 0, 2, 10, 28 and 75 μM. After 12 h, samples were observed for fluorescence by confocal microscopy and then quantified by fluorescence-activated cell sorting (FACS; Supplementary Fig. 4c). We found that SBP–Venus expression for responder A cells occurred at the lowest tested level (2 μM AI-2), where 56% of the population expressed SBP–Venus and this fraction increased with AI-2 reaching a maximum of 90% at 28 μM. For type B, a more gradual trend was found; only ~1% was fluorescent from 0-2 μM, and this increased from 9 to 46% as AI-2 was increased to 28 μM. Finally, the highest fraction of fluorescing cells was found at the highest concentration tested, 75 μM.

We next isolated CM, which contains a dynamic composition of unfiltered metabolites and media components, from W3110 E. coli cultures at intervals during their exponential growth, throughout which AI-2 accumulates (AI-2 levels for the samples are indicated in Supplementary Fig. 7). CM aliquots were mixed with either A or B cells and cultured in triplicate for 12 h. Through FACS analysis it was found, again, that a larger subpopulation of A expressed Venus compared with population B at any concentration (Fig. 5c). Statistically relevant expression from B was not apparent until incubated with CM from cultures at an optical density (OD) of 0.23. In all cases, population A recognized AI-2 presence, including from media isolated at a W3110 OD of 0.05, the minimum cell density tested in this study.

The sensitivities of both strains to AI-2-mediated induction corroborate previous literature10, 47. These trends demonstrate that strains engineered for altered sensitivity to molecular cues provide discrimination of concentration level. That is, the identical plasmid expression system was transformed into different hosts, providing robust and distinct levels of expression.

Having developed cell types A and B with differential ability to detect AI-2, we next altered the reporters so that each cell type expressed a unique SBP-fluorescence fusion for colour-coded designation. Cell type A was engineered with pSBP–mCherry and type B with pSBP–eGFP, resulting in red and green fluorescence, respectively. These populations were mixed together in equal proportion at mid-exponential phase, introduced to a range of AI-2 concentrations, and incubated overnight. Populations A and B exhibited equal growth rates when cultured alone and together (Supplementary Fig. 8c); it followed that the cocultures should comprise a 1:1 ratio of each constituent. Fluorescence output is shown by representative images in Fig. 5d. Also in Fig. 5d, the green and red cell count is plotted from a quadruplicate analysis for each input concentration.

Coculturing enables parallel processing as the molecule-rich environment is perceived by each cell, and is processed uniquely per cell type. Yet, since each sensing mechanism is a living and proliferating population, we tested whether the potentially altered dynamics of coculturing would permit the same sensitivities as isolated culturing. We evaluated the Monod-type saturation constant for each population independently and in cocultures. We found, in Fig. 5d, the general trends in response to an increasing AI-2 level were as predicted by modelled response curves (Supplementary Table 4), which were also well-correlated to Fig. 5b data (Supplementary Fig. 8a,b). That is, the saturation constants that describe dependence on AI-2 were unchanged when measured in cocultures. Phenomenologically, as expected, an initial accumulation of red type A responders was found. Then, at higher AI-2 levels, we found an emergence of a green subpopulation (type B). Above 28 μM, there was no longer an apparent differential response that would otherwise enable discrimination of AI-2 concentration; based on the consistency with modelled behaviour, coculturing contributed to dampen the response as the maximum percentage of responding cells in cocultures is 50% instead of 100%. However, the overall fluorescence output is enriched by the combination of multiple populations since the ranges of sensitivity overlap and effectively expand that of the master population (Supplementary Fig. 8d). Specifically, because the fluorescence of B is described by a larger saturation constant, its fluorescence continually increases at higher AI-2 concentrations, while the fluorescence of A remains unchanged. Thus, coculturing between A and B enables resolvable output that is lower than the detection limit of B (due to A) yet surpasses the upper limit at which A saturates by the inclusion of B. The choice to fluorescently differentiate A and B was important because the output would otherwise be biased by extracellular components including the existence of non-sensing cells. Due to colour designation of A and B, a colour ‘pattern’ emerges as a feature of the parallel response, which we recognize is independent of the absolute fluorescence of the population.

Consensus feedback through multidimensional processing

We hypothesized that the value of cell-based sensing would be enhanced if the cell output could be collated in an unbiased manner that in turn were easily ‘read’ using optical means. We engaged magnetic processing, which represents an abiotic processing step that enhances the signal by focusing the collective response. Hence, cells were equipped with streptavidin-conjugated mNPs (Fig. 3). The ability of a magnetic field to refine fluorescence output through filtering and focusing is described in the Supplementary Information (Supplementary Fig. 11). Thus, in our combinatorial approach, fluorescence feedback about molecular information within a microbial community entails biotic processing through constituencies of two independent cell types in conjunction with magnetic post-processing that is enabled by guidance at the nanoscale (Fig. 6c). Moreover, since the fluorescence feedback data is provided through two constituencies, consensus from each independently provides an aggregate output; in our example, the output becomes relayed as a distinctive ‘binned’ category due to finite colour-combinations generated from constituencies A and B (Fig. 6c).

Figure 6: Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

Binning molecular information through cell-based parallel processing and magnetically focusing fluorescence into collective consensus output.

(a) A and B cell types were co-incubated with AI-2 levels ranging from 0 to 55 μM AI-2 (left axis), then imaged after magnetic nanoparticle coupling and magnetic collation. Fluorescence results (centred directly over the magnet) are shown from high to low input (top left to bottom right). (b) Quantification of red and green fluorescence cell densities per AI-2 level. (c) The process of accessing molecular information begins by distributing Responders A and B within the environment of an AI-2 producer, P. A and B independently express fluorophore fusions and are linked with magnetic nanoparticles on processing autoinducer-2. Magnetic focusing translocates fluorescing responders. Image analysis of the magnetically collated cell aggregate reveals classified fluorescence output, representing the AI-2 composition of the interrogated environment. (d) Bright field (left) and fluorescence (right, red and green filters) images positioned over the edge of a magnet, as indicated by the inset. The sample in the bottom image pair was isolated from an environment of low AI-2 accumulation. The sample in the top image pair was isolated from a high AI-2 environment. (e) Quantification of visual space occupied by collated cells (eGFP and mCherry expressers) while distributed (- magnet) and magnetically focused (+). Scale bars, 50 μm.

Again, type A transmits red output (SBP–mCherry+) and type B transmits green (SBP–eGFP+). These were first co-incubated with titred concentrations of AI-2, to obtain results similar to those ofFig. 5d. By coupling mNPs to the responsive parallel populations, we tested for aggregate two-colour output to provide informative feedback within a set of outcomes ranging from no colour, red-only to red+green. After overnight co-incubation and a magnetic sweep with streptavidin-mNPs, fluorescence results are shown in Fig. 6a, where the recovered cells are displayed above a magnet’s center in order from highest to lowest AI-2 level (top left to bottom right). The processing output generated by the range of conditions was quantitatively assessed for contributions from A and B responders. The spatial density of each fluorophore, or the area occupied by fluorescent responders as a percentage of total visible area, was quantified and plotted in Fig. 6b. Here the trend of increasing fluorescence with AI-2 is followed by both A and B cell types; however, red A cells accumulate at a higher rate than green B cells. This relationship between A and B processing is not only consistent with their previous characterizations (Fig. 5) but indicates that the aggregate output is unbiased regardless of assembly with mNPs and magnetic-stimulated redistribution (Supplementary Information, Supplementary Fig. 14a).

Next, A and B cells were added together to probe the QS environment of Listeria innocua, an AI-2-producing cell type that is genetically and ecologically similar to the pathogenic strain L. monocytogenes52. The environment was biased towards low and high cell density conditions by altering nutrient levels to develop contrasting scenarios of AI-2 level. Preliminary characterization in the Supplementary Information indicated that L. innocua proliferation is unperturbed by the presence of E. coli responders (Supplementary Fig. 12) and that type A cells detect AI-2 at lowListeria densities limited by sparse nutrients; then with rich nutrient availability, cell proliferation permits a higher AI-2 level that can be detected by type B (Supplementary Fig. 13). Replicating these conditions, we expected red fluorescence to be observed at low culture density and for green fluorescence to be reported when high (Fig. 6c). Two conditions were tested: L. innocua was proportioned to responder cells at 20:1 in dilute media to establish a low culture density condition or, alternatively, a ratio of 200:1 in rich media for a high culture density condition. After overnight co-incubation and a magnetic sweep (applied directly to the triple strain cultures) with streptavidin-mNPs, the recovered cells are displayed above a magnet’s edge (shown in Fig. 6d). Acute focusing of the fluorescence signals, contributed by each subset population of the processor (A and B), is visually apparent. The magnetic field had a physical effect of repositioning the ‘on’ subsets to be tightly confined within the magnetic field.

The processing output generated by the contrasting culture conditions was again assessed for the respective contributions of A and B, and for changes in spatial signal density due to the magnetic sweep (Fig. 6e). The analysis was based on images provided in Supplementary Fig. 14b. Data inFig. 6e indicate that red type A cells are prevalent regardless of culture condition (except negative controls). However, compared with the low AI-2 condition, the abundance of green cells is 100-fold higher in the high AI-2 condition. In addition, the ratio of green to red was consistent prior to and after magnetic concentration, substantiating observations in the distributed system. Further, data show that magnetic refining increased per-area fluorescence 100-fold or 10-fold in low and high cell culture studies, respectively.

Based on the thresholds established for responder populations A and B, we found colour-coded binning corresponded to AI-2 level, where ‘red-only’ represented less AI-2 than ‘red+green’ (Fig. 5d). Thus, we found a binned output was established via this multidimensional molecular information-processing system and that this matched the expectations. Red feedback (from responder A) indicated dilute AI-2 accumulation occurred in the low density culture. In the dense cultures, high AI-2 accumulation turned on both A and B for combined red and green feedback.

System response patterns defined by parallel populations

Our example demonstrates the concept of an amorphous processing system that utilizes several biotic and abiotic components for multidimensional information processing. Interestingly, a binning effect was enabled: our system yields an index of colour-categorized feedback that characterizes the sampled environment. In Fig. 7, we present a means to extend our approach to multidimensional systems, those with more than one molecule-of-interest and at different concentrations. That is, by appropriate design of the cell responders, we can further enrich the methodology, its depth and breadth of applicability. We depict 10 hypothetical pairs of responses (with defining equations located in Supplementary Table 5)—those that can be driven by appropriately engineering cells to portend altered genetic responses. For example, rows 1 and 3 provide genetic outcomes as a function of analyte (AI-2) concentration. The hypothetical depictions are feasible as ‘designer’ signal transduction and marker expression processes enabled by synthetic biology21, 53, 54. Rows 2 and 4 demonstrate the corresponding visual planes, where red cell numbers (x-axis) are plotted against green (y-axis), illustrated by the first example. If one divides the two-dimensional space into quadrants (no colour, majority red, majority green, and equivalent ratios of red and green), it becomes apparent that the relationship between cell types influences the ‘visual’ or optical output. Thus, the 10 arbitrary response sets yield a variety of pairings that can provide unique visual patterns for categorizing molecular information. We have simplified the analysis by placing dot marker symbols at the various coincident datapoints, revealing visual patterns. In this way, the ability to incorporate unique responses to a multitude of molecular cues, all within a single pair of cells, or through further multiplexing with additional cell populations becomes apparent.

Figure 7: Extension of nano-guided cell networks for hypothetical regulatory structures.

Extension of nano-guided cell networks for hypothetical regulatory structures.

(a) Rows 1 and 3 depict 10 hypothetical genetic responses to molecular inputs for pairs of fluorescence-reporting cell populations (red, R and green, G). Rows 2 and 4 depict genetic responses as phase-plane plots yielding distinct patterns. This establishes a visual field, showing the extent of any population–population bias (illustrated in example case 1). (b) Left panel: a two-population pairing (shown in case 10) defines visual output that inherently bins into three quadrants: Q1, negligible colour; Q2, red bias due to majority red cell output; and Q4, combined red and green output. Right panel: data from Figs 5d and 6bare plotted analogously, where each data point represents an autoinducer-2 input (labelled, μM). As expected, red and green outputs were binned into Q1, Q2 and Q4 as indicated by coloured outlines.

Our AI-2-conveying cell network is similar to example 7 in Fig. 7a and the AI-2 response curves inFig. 5 (characterized by Supplementary Table 4 equations). Example 7 establishes output into three basic quadrants, including Q1 (negligible colour), Q2 (majority red) and Q4 (roughly equal red and green) (Fig. 7b). We recast the data from Figs 5d and 6b as a phase-plane portrait in Fig. 7c. This reveals the mechanisms by which the output is binned and how the originating cell response curves lead to this pattern, which in turn, was unchanged due to magnetic refinement. InSupplementary Fig. 15, we demonstrate a parameterization of the red and green response curves that suggest the methodology is robust, that when cells are appropriately engineered one could ‘tune’ system characteristics to enhance or diminish a binning effect. We suggest that the utility of subcellular genetic tuning extends well beyond per-cell performance. Rather, we suggest such a strategy may be used to guide the dynamics of population architecture for actuation of by-design response patterns at a systems level.

Discussion

While cell-based sensors work well in well-defined assay conditions, extension to complex environments remains a challenge. They grow, they move, they perturb their environs, they report in a time and concentration-dependent manner, small numbers of sensor cells may require signal amplification and so on. Also, increasingly, bacterial cells are engineered for user specified ‘executive’ functions in complex environments55, 56, 57. Their performance depends on their ability to filter out extraneous noise while surveying the molecular landscape, and providing informed actuation.

Our system interrogates the molecular space by focusing on bacterial QS and a widely distributed signal molecule, AI-2. In addition to genetic attributes of the AI-2-responding sensor cells, AI-2 is a chemoattractant for E. coli, and hence E. coli engineered to sense and respond to AI-2 will naturally move towards its sources, enabling full sampling of the prevailing state10, 37. Each strain evaluates AI-2 with a distinct sensitivity. When ‘activated’ in response to a characteristic level, the cells simultaneously expressed a fluorescent marker and a SBP on the outer membrane via AIDAc translocation. SBP provides a means for cell hybridization through its strong affinity to streptavidin, and here, aids in binding mNPs. This enables the non-genetically coded property of cell translocation within a magnetic field through physically stimulated focusing and binning.

By making use of a diversity of biotic and abiotic features, our multidimensional system of ‘responder’ populations exemplifies several key metrics that promote executive performance in such environments: active molecule capture, post-capture refining of the detection output and finally the utilization of multiple feedback thresholds58, 59, 60. Here cells facilitate AI-2 recognition autonomously and actively because, as a distributed network they reside planktonically, chemotaxing to and continually processing signals over time. When AI-2 is detected, a processor cell’s cognate machinery responds by upregulation of the native QS operon, leading to rapid signal uptake and thereby creating an active-capture signal-processing mechanism. To maximize information acquisition and account for a potentially heterogeneous molecular landscape, cells serve as molecular sampling units among a distributed population, which leads to data fed back as a consensus of fluorescent ‘datapoints’. Then, distributed data collection can be selectively reversed via the incorporated abiotic feature: mNPs, fastened externally on the cell through affinity-guided self-assembly. As such, responding cells obtained this extendable feature, thereby becomes sensitized to repositioning within a magnetic field.

The layered nature of the processor here, from the subcellular to multicellular scale, permits a series of selective steps: it commences with the AI-2-triggered expression cascade which releases a tight repressor, surface localization of both the fluorescent protein and SBP tag, and finally nanoparticle binding for recovery. In addition, multiple layers of amplification result in orthogonal fluorescence feedback. The AI-2 detection event leads to whole-cell fluorescence through expression of many protein copies47. Then their physical collection further amplifies the signal, yielding a macroscopic composite of many individual cell units. When utilized as a network of multiple constituencies, responder cell types A and B contribute individual recognition results (off, red or green) to a single consensus output. Finally, due to their overlapping thresholds for recognition of the same molecule, in this case, AI-2, parallel processing by A and B responders can contribute to visual interpretation of information about the molecule. Outcomes are classified into a finite number of states: here output to no fluorescence, red, or red and green, with each addition of colour as a metric of a higher interval of AI-2. In many respects, the elucidation of layered information networks as demonstrated here is analogous to computer information processing via information theory61, 62, 63.

Here, however, interrogation of biological systems requires a reliable means for accessing molecular information—that which is communicated between biological species and that which can be relayed to the end user. The responder cells need not be present in high concentration, nor must they all be collected in the present format. We suggest that engineered biological mechanisms are well-poised to serve at this critical interface between information acquisition and user interaction. Thus, the functional design of components for autonomous self-assembly, decision-making and networking is requisite in the field of micro- and nano-scaled machines. Our combinatorial approach allows for cells to independently assess, yet collectively report, on molecular information. Its processing is enabled through appropriate integration of synthetic biology and nanomaterials design. We suggest this approach provides a rich opportunity to direct many formats of multi-population response through genetic tuning and systems-level engineering. Further development of cellular networks and incorporation of alternate abiotic attributes can expand the depth and breadth of molecular communication for user specified actuation.

Read Full Post »

Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

4.3.7

4.3.7 Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

” data-icon-position=”” data-hide-link-title=”0″>

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

Read Full Post »

AACR Congratulates Dr. William G. Kaelin Jr., Sir Peter J. Ratcliffe, and Dr. Gregg L. Semenza on 2019 Nobel Prize in Physiology or Medicine

Reporter: Stephen J. Williams, PhD

 

from The American Association for Cancer Research aacr.org:

 

AACR Congratulates Dr. William G. Kaelin Jr., Sir Peter J. Ratcliffe, and Dr. Gregg L. Semenza on 2019 Nobel Prize in Physiology or Medicine

10/7/2019

PHILADELPHIA — The American Association for Cancer Research (AACR) congratulates Fellow of the AACR Academy William G. Kaelin Jr., MD, Sir Peter J. Ratcliffe, MD, FRS, and AACR member Gregg L. Semenza, MD, PhD, on receiving the 2019 Nobel Prize in Physiology or Medicine for their discoveries of how cells sense and adapt to oxygen availability.

Kaelin, professor of medicine at the Dana-Farber Cancer Institute and Harvard Medical School in Boston; Ratcliffe, director of Clinical Research at the Francis Crick Institute in London; and Semenza, director of the Vascular Program at the Institute for Cell Engineering at Johns Hopkins University School of Medicine in Baltimore, are being recognized by the Nobel Assembly at the Karolinska Institute for identifying the molecular machinery that regulates the activity of genes in response to varying levels of oxygen, which is one of life’s most essential adaptive processes. Their work has provided basic understanding of several diseases, including many types of cancer, and has laid the foundation for the development of promising new approaches to treating cancer and other diseases.

Kaelin, Ratcliffe, and Semenza were previously recognized for this work with the 2016 Lasker-DeBakey Clinical Medical Research Award.

Kaelin’s research focuses on understanding how mutations affecting tumor-suppressor genes cause cancer. As part of this work, he discovered that a tumor-suppressor gene called von Hippel–Lindau (VHL) is involved in controlling the cellular response to low levels of oxygen. Kaelin’s studies showed that the VHL protein binds to hypoxia-inducible factor (HIF) when oxygen is present and targets it for destruction. When the VHL protein is mutated, it is unable to bind to HIF, resulting in inappropriate HIF accumulation and the transcription of genes that promote blood vessel formation, such as vascular endothelial growth factor (VEGF). VEGF is directly linked to the development of renal cell carcinoma and therapeutics that target VEGF are used in the clinic to treat this and several other types of cancer.

Kaelin has been previously recognized with numerous other awards and honors, including the 2006 AACR-Richard and Hinda Rosenthal Award.

Ratcliffe independently discovered that the VHL protein binds to HIF. Since then, his research has focused on the molecular interactions underpinning the binding of VHL to HIF and the molecular events that occur in low levels of oxygen, a condition known as hypoxia. Prior to his work on VHL, Ratcliffe’s research contributed to elucidating the mechanisms by which hypoxia increases levels of the hormone erythropoietin (EPO), which leads to increased production of red blood cells.

Semenza’s research, which was independent of Ratcliffe’s, identified in exquisite detail the molecular events by which the EPO gene is regulated by varying levels of oxygen. He discovered HIF and identified this protein complex as the oxygen-dependent regulator of the EPO gene. Semenza followed up this work by identifying additional genes activated by HIF, including showing that the protein complex activates the VEGF gene that is pivotal to the development of renal cell carcinoma.

The recognition of Kaelin and Semenza increases the number of AACR members to have been awarded a Nobel Prize to 70, 44 of whom are still living.

The Nobel Prize in Physiology or Medicine is awarded by the Nobel Assembly at the Karolinska Institute for discoveries of major importance in life science or medicine that have changed the scientific paradigm and are of great benefit for mankind. Each laureate receives a gold medal, a diploma, and a sum of money that is decided by the Nobel Foundation.

The Nobel Prize Award Ceremony will be Dec. 10, 2019, in Stockholm.

Please find following articles on the Nobel Prize and Hypoxia in Cancer on this Open Access Journal:

2018 Nobel Prize in Physiology or Medicine for contributions to Cancer Immunotherapy to James P. Allison, Ph.D., of the University of Texas, M.D. Anderson Cancer Center, Houston, Texas. Dr. Allison shares the prize with Tasuku Honjo, M.D., Ph.D., of Kyoto University Institute, Japan

The History, Uses, and Future of the Nobel Prize, 1:00pm – 6:00pm, Thursday, October 4, 2018, Harvard Medical School

2017 Nobel prize in chemistry given to Jacques Dubochet, Joachim Frank, and Richard Henderson  for developing cryo-electron microscopy

Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Hypoxia Inducible Factor 1 (HIF-1)[7.9]

 

 

Read Full Post »

Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

Reporter: Dror Nir, PhD

Authors’ list: Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger, Sagiv Shifman & Aviv A. Mezer
Published in: Nature Communications volume 10, Article number: 3403 (2019)

3.5.2.2

3.5.2.2   Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine

Abstract

It is an open question whether aging-related changes throughout the brain are driven by a common factor or result from several distinct molecular mechanisms. Quantitative magnetic resonance imaging (qMRI) provides biophysical parametric measurements allowing for non-invasive mapping of the aging human brain. However, qMRI measurements change in response to both molecular composition and water content. Here, we present a tissue relaxivity approach that disentangles these two tissue components and decodes molecular information from the MRI signal. Our approach enables us to reveal the molecular composition of lipid samples and predict lipidomics measurements of the brain. It produces unique molecular signatures across the brain, which are correlated with specific gene-expression profiles. We uncover region-specific molecular changes associated with brain aging. These changes are independent from other MRI aging markers. Our approach opens the door to a quantitative characterization of the biological sources for aging, that until now was possible only post-mortem.

Introduction

The biology of the aging process is complex, and involves various physiological changes throughout cells and tissues1. One of the major changes is atrophy, which can be monitored by measuring macroscale brain volume reduction1,2. In some cases, atrophy can also be detected as localized microscale tissue loss reflected by increased water content3. This process is selective for specific brain regions and is thought to be correlated with cognitive decline in Alzheimer’s disease2,4,5. In addition to atrophy, there are molecular changes associated with the aging of both the normal and pathological brain5,6. Specifically, lipidome changes are observed with age, and are associated with several neurological diseases7,8,9,10,11.

It is an open question as to whether there are general principles that govern the aging process, or whether each system, tissue, or cell deteriorates with age for different reasons12,13. On one hand, the common-cause hypothesis proposes that different biological aging-related changes are the result of a single underlying factor14,15. This implies that various biomarkers of aging will be highly correlated16. On the other hand, the mosaic theory of aging suggests that there are several distinct aging mechanisms that have a heterogenous effect throughout the brain12,13. According to this latter view, combining different measurements of brain tissue is crucial in order to fully describe the state of the aging brain. To test these two competing hypotheses in the context of volumetric and molecular aging-related changes, it is essential to measure different biological aspects of brain tissue. Unfortunately, the molecular correlates of aging are not readily accessible by current in vivo imaging methods.

The main technique used for non-invasive mapping of the aging process in the human brain is magnetic resonance imaging (MRI)2,17,18,19. Advances in the field have led to the development of quantitative MRI (qMRI). This technique provides biophysical parametric measurements that are useful in the investigation and diagnosis of normal and abnormal aging20,21,22,23,24,25,26,27. qMRI parameters have been shown to be sensitive to the microenvironment of brain tissue and are therefore named in vivo histology28,29,30. Nevertheless, an important challenge in applying qMRI measurements is increasing their biological interpretability. It is common to assume that qMRI parameters are sensitive to the myelin fraction20,23,30,31,32,33, yet any brain tissue including myelin is a mixture of multiple lipids and proteins. Moreover, since water protons serve as the source of the MRI signal, the sensitivity of qMRI parameters to different molecular microenvironments may be confounded by their sensitivity to the water content of the tissue34,35. We hypothesized that the changes observed with aging in MRI measurements20,23,30,31,32,33,36 such as R1, R2, mean diffusivity (MD), and magnetization transfer saturation (MTsat)37, could be due to a combination of an increase in water content at the expense of tissue loss, and molecular alterations in the tissue.

Here, we present a qMRI analysis that separately addresses the contribution of changes in molecular composition and water content to brain aging. Disentangling these two factors goes beyond the widely accepted “myelin hypothesis” by increasing the biological specificity of qMRI measurements to the molecular composition of the brain. For this purpose, we generalize the concept of relaxivity, which is defined as the dependency of MR relaxation parameters on the concentration of a contrast agent38. Instead of a contrast agent, our approach exploits the qMRI measurement of the local non-water fraction39 to assess the relaxivity of the brain tissue itself. This approach allows us to decode the molecular composition from the MRI signal. In samples of known composition, our approach provides unique signatures for different brain lipids. In the live human brain, it produces unique molecular signatures for different brain regions. Moreover, these MRI signatures agree with post-mortem measurements of the brain lipid and macromolecular composition, as well as with specific gene-expression profiles. To further validate the sensitivity of the relaxivity signatures to molecular composition, we perform direct comparison of MRI and lipidomics on post-mortem brains. We exploit our approach for multidimensional characterization of aging-related changes that are associated with alterations in the molecular composition of the brain. Finally, we evaluate the spatial pattern of these changes throughout the brain, in order to compare the common-cause and the mosaic theories of aging in vivo.

Results

Different brain lipids have unique relaxivity signatures
The aging process in the brain is accompanied by changes in the chemophysical composition, as well as by regional alterations in water content. In order to examine the separate pattern of these changes, we developed a model system. This system was based on lipid samples comprising common brain lipids (phosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylcholine-cholesterol, and phosphatidylinositol-phosphatidylcholine)7. Using the model system, we tested whether accounting for the effect of the water content on qMRI parameters provides sensitivity to fine molecular details such as the head groups that distinguish different membrane phospholipids. The non-water fraction of the lipid samples can be estimated by the qMRI measurement of lipid and macromolecular tissue volume (MTV, for full glossary of terms see Supplementary Table 1)39. By varying the concentration of the lipid samples, we could alter their MTV and then examine the effect of this manipulation on qMRI parameters. The parameters we estimated for the lipid samples were R1, R2, and MTsat. The potential ambiguity in the biological interpretation of qMRI parameters is demonstrated in Fig. 1a. On one hand, samples with similar lipid composition can present different R1 measurements (Fig. 1a, points 1 & 2). On the other hand, scanning samples with different lipid compositions may result in similar R1 measurements (Fig. 1a, points 2 & 3). This ambiguity stems from the confounding effect of the water content on the MR relaxation properties.

Screenshot 2019-08-01 at 14.36.20

We evaluated the dependency of different qMRI parameters on the non-water fraction estimated by MTV. This analysis revealed strong linear dependencies (median R2 = 0.74, Fig. 1a, b and Supplementary Fig. 1a, b). These linear MTV dependencies change as a function of the lipid composition, reflecting the inherent relaxivity of the different lipids. We could therefore use the MTV derivatives of qMRI parameters (dqMRIdMTV, i.e., the slope of the linear relationship between each qMRI parameter and MTV) as a measure that is sensitive to molecular composition. By accounting for the Multidimensional Dependency on MTV (“MDM”) of several qMRI parameters, a unique MRI relaxivity signature was revealed for each lipid (Fig. 1c). This implies that the water-related ambiguity demonstrated in the inset of Fig. 1a can be removed by measuring the MTV dependencies (Fig. 1c). Creating mixtures of several lipids provided supportive evidence for the generality of our framework. Figure 1d and Supplementary Fig. 1c show that the qMRI measurements of a mixture can be predicted by summing the MTV dependencies of pure lipids (for further details see Supplementary Note 1 and Supplementary Fig. 2). Furthermore, we used this biophysical model to predict the lipid composition of a mixture from its MDM measurements (Fig. 1e). This model provided a good estimation of the sphingomyelin (Spg) and phosphatidylserine (PS) content (R2 > 0.64) but failed to predict phosphatidylcholine (PtdCho) content (for further details see Supplementary Note 2). While lipids are considered to be a major source of the MRI signal in the brain 40,41,42,43,44,45, our approach can be applied to other compounds to reveal differences in the MRI signal between different proteins, sugars, and ions (Supplementary Fig. 1d). Hence, the relationships between qMRI parameters and MTV account for the effect of water on MRI measurements and could be of use in quantifying the biological and molecular contributions to the MRI signal of water protons.

The tissue relaxivity of the human brain is region-specific.
In order to target age-related changes in molecular composition, we applied the same approach for the human brain (Fig. 2a).

Screenshot 2019-08-01 at 14.41.35

We found that the linear dependency of qMRI parameters on MTV is not limited to in vitro samples and a similar relationship was also evident in the human brain (Fig. 2b and Supplementary Figs. 3–5). Importantly, different brain regions displayed a distinct dependency on MTV. Therefore, the relaxivity of brain tissue is region-specific. Figure 2b provides an example for the regional linear trends of R1 and MTsat in a single subject. Remarkably, while the thalamus and the pallidum presented relatively similar R1 dependencies on MTV, their MTsat dependencies were different (p < 0.001, two-sample t-test). Compared to these two brain regions, frontal white-matter demonstrated different dependencies on MTV (p < 0.001, two-sample t-test). A better separation between brain regions can therefore be achieved by combining the MTV dependencies of several qMRI parameters (MTsat, MD, R1 and R2). The MTV derivatives of qMRI parameters are consistent across subjects (Fig. 2c and Supplementary Fig. 6), with good agreement between hemispheres (Supplementary Fig. 5). Moreover, they provide a novel pattern of differentiation between brain regions, which is not captured by conventional qMRI methods (Supplementary Fig. 7). In our lipid sample experiments, the MDM approach revealed unique relaxivity signatures of different lipids (Fig. 1c). Therefore, we attribute the observed diversity in the MTV derivatives of qMRI parameters across brain regions to the intrinsic heterogeneity in the chemophysical microenvironment of these regions. The multidimensional dependency of various qMRI parameters on MTV can be represented by the space of MTV derivatives to reveal a unique chemophysical MDM signature for different brain regions (Fig. 2d, see explanatory scheme of the MDM method in Supplementary Fig. 8). Fig. 2 figure2 The MDM method provides region-specific signatures in the in vivo human brain. a Representative MTV, MTsat, and R1 maps. b Calculating the MDM signatures. The dependency of R1 (left) and MTsat (right) on MTV in three brain regions of a single subject. For each region, MTV values were pooled into bins (dots are the median of each bin; shaded area is the median absolute deviation), and a linear fit was calculated (colored lines). The slopes of the linear fit represent the MTV derivatives of R1 and MTsat and vary across brain regions. c The reliability of the MDM method across subjects. Variation in the MTV derivatives of R1 (left) and MTsat (right) in young subjects (N = 23). Different colors represent 14 brain regions (see legend). Edges of each box represent the 25th, and 75th percentiles, median is in black, and whiskers extends to extreme data points. Different brain regions show distinct MTV derivatives. d Unique MDM signatures for different brain regions (in different colors). Each axis is the MTV derivative (“MDM measurements”) of a different qMRI parameter (R1, MTsat, R2, and MD). The range of each axis is in the legend. Colored traces extend between the MDM measurements, shaded areas represent the variation across subjects (N = 23). An overlay of all MDM signatures is marked with dashed lines Full size image The in vivo MDM approach captures ex vivo molecular profiles To validate that the MDM signatures relate to the chemophysical composition of brain tissue, we compared them to a previous study that reported the phospholipid composition of the human brain7. First, we established the comparability between the in vivo MRI measurements and the reported post-mortem data. MTV measures the non-water fraction of the tissue, a quantity that is directly related to the total phospholipid content. Indeed, we found good agreement between the in vivo measurement of MTV and the total phospholipid content across brain regions (R2 = 0.95, Fig. 3a). Söderberg et al.7 identified a unique phospholipid composition for different brain regions along with diverse ratios of phospholipids to proteins and cholesterol. We compared this regional molecular variability to the regional variability in the MDM signatures. To capture the main axes of variation, we performed principal component analysis (PCA) on both the molecular composition of the different brain regions and on their MDM signatures. For each of these two analyses, the first principal component (PC) explained >45% of the variance. The regional projection on the first PC of ex vivo molecular composition was highly correlated (R2 = 0.84, Fig. 3b) with the regional projection on the first PC of in vivo MDM signatures. This confirms that brain regions with a similar molecular composition have similar MDM. Supplementary Fig. 9a provides the correlations of individual lipids with MDM. Importantly, neither MTV nor the first PC of standard qMRI parameters was as strongly correlated with the ex vivo molecular composition as the MDM (Supplementary Fig. 9b, c). We next used the MDM measurements as predictors for molecular properties of different brain regions. Following our content predictions for lipids samples (Fig. 1e), we constructed a weighted linear model for human data (for further details see Supplementary Note 3). To avoid over fitting, we reduced the number of fitted parameters by including only the MDM and the molecular features that accounted for most of the regional variability. The MTV derivatives of R1 and MTsat accounted for most of the variance in MDM. Thus, we used these parameters as inputs to the linear model, while adjusting their weights through cross validation. We tested the performance of this model in predicting the three molecular features that account for most of the variance in the ex vivo molecular composition. Remarkably, MRI-driven MDM measurements provided good predictions for the regional sphingomyelin composition (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c) and the regional ratio of phospholipids to proteins (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c).

Screenshot 2019-08-01 at 14.44.06
Last, we compared the cortical MDM signatures to a gene co-expression network based on a widespread survey of gene expression in the human brain46. Nineteen modules were derived from the gene network, each comprised of a group of genes that co-varies in space. Six out of the nineteen gene modules were significantly correlated with the first PC of MDM. Interestingly, the first PC of MDM across the cortex was correlated most strongly with the two gene modules associated with membranes and synapses (Fig. 4, for further details see Supplementary Note 4 and Supplementary Figs. 10 and 11).

Screenshot 2019-08-01 at 14.47.04

Post-mortem validation for the lipidomic sensitivity of MDM.
The aforementioned analyses demonstrate strong agreement between in vivo MDM measurements and ex vivo molecular composition based on a group-level comparison of two different datasets. Strikingly, we were able to replicate this result at the level of the single brain. To achieve this we performed MRI scans (R1, MTsat, R2, MD, and MTV mapping) followed by histology of two fresh post-mortem porcine brains (Fig. 5a, b). First, we validated the qMRI estimation of MTV using dehydration techniques. MTV values estimated using MRI were in agreement with the non-water fraction found histologically (adjusted R2 = 0.64, p < 0.001 for the F-test, Fig. 5c).

Screenshot 2019-08-01 at 14.50.12
Next, we estimated the lipid composition of different brain regions. Thin-layer chromatography (TLC) was employed to quantify seven neutral and polar lipids (Supplementary Table 2 and Supplementary Fig. 12a). In accordance with the analysis in Fig. 3, we performed PCA to capture the main axes of variation in lipidomics, standard qMRI parameters, and MDM. Figure 5d shows that MTV did not correlate with the molecular variability across the brain, estimated by the 1st PC of lipidomics. Likewise, the molecular variability did not agree with the 1st PC of standard qMRI parameters (Fig. 5e).

Last, we applied the MDM approach to the post-mortem porcine brain. Similar to the human brain, different porcine brain regions have unique MDM signatures (Fig. 5f, g and Supplementary Fig. 12b). Remarkably, we found that agreement between lipid composition and MRI measurements emerges at the level of the MDM signatures. The molecular variability across brain regions significantly correlated with the regional variability in the MDM signatures (adjusted R2 = 0.3, p < 0.01 for the F-test, Fig. 5h). Excluding from the linear regression five outlier brain regions where the histological lipidomics results were 1.5 standard deviations away from the center yielded an even stronger correlation between MDM signatures and lipid composition (adjusted R2 = 0.55, p < 0.001 for the F-test, Supplementary Fig. 12c). This post-mortem analysis validates that the MDM approach allows us to capture molecular information using MRI at the level of the individual brain.

Disentangling water and molecular aging-related changes.
After establishing the sensitivity of the MDM signatures to the molecular composition of the brain, we used them to evaluate the chemophysical changes of the aging process. To assess aging-related changes across the brain, we scanned younger and older subjects (18 older adults aged 67 ± 6 years and 23 younger adults aged 27 ± 2 years). First, we identified significant molecular aging-related changes in the MDM signatures of different brain regions (Figs. 6 and 7, right column; Supplementary Fig. 13). Next, we tested whether the changes in MRI measurements, observed with aging, result from a combination of changes in the molecular composition of the tissue and its water content. We found that although it is common to attribute age-related changes in R1 and MTsat to myelin28,30,36, these qMRI parameters combine several physiological aging aspects. For example, using R1 and MTsat we identified significant aging-related changes in the parietal cortex, the thalamus, the parietal white-matter and the temporal white-matter (Figs. 6 and 7, left column). However, the MDM approach revealed that these changes have different biological sources (Figs. 6 and 7, middle columns; see Supplementary Figs. 14–17 for more brain regions).

Screenshot 2019-08-01 at 14.51.53

Screenshot 2019-08-01 at 14.54.44

Screenshot 2019-08-01 at 14.56.06

In agreement with the mosaic hypothesis, we identified distinct aging patterns for different brain regions. For example, in the hippocampus we found a change in R2* values related to a higher iron concentration with age, along with significant reduction in the total hippocampal volume (Fig. 8a). This age-related shrinkage was not accompanied by lower MTV values, indicating conserved tissue density (Fig. 7b). In addition, there was no significant difference in the hippocampal MDM signature with age (Fig. 7b). Cortical gray-matter areas also exhibited similar trends of volume reduction without major loss in tissue density (Fig. 8a). Unlike the gray matter, in the white matter we did not find volume reduction or large iron accumulation with age (Fig. 8a). However, we did find microscale changes with age in tissue composition, as captured by the MDM signature (Figs. 6a and 7c, and Supplementary Fig. 13), accompanied by a significant density-related decline in MTV (Fig. 8a). These findings are consistent with previous histological studies49,50,51 (see Discussion), and provide the ability to monitor in vivo the different components of the aging mosaic.

Last, to test whether the different biological aging trajectories presented in Fig. 8a share a common cause, we evaluated the correlations between them (Fig. 8b). Importantly, the chemophysical trajectory did not correlate significantly with the iron or volume aging patterns. The spatial distribution of water-related changes was found to correlate with iron content alterations (R2 = 0.27) and chemophysical alterations (R2 = 0.25). However, the strongest correlation between aging-related changes was found in volume and iron content (R2 = 0.77). As shown previously, this correlation may be explained to some extent by a systematic bias in automated tissue classification23. Additional analysis revealed that the different dimensions of the MDM signature capture distinct patterns of aging-related changes (Supplementary Fig. 30). Hence, complementary information regarding the various chemophysical mechanisms underlying brain aging could be gained by combining them.

Discussion

Normal brain aging involves multiple changes, at both the microscale and macroscale level. MRI is the main tool for in vivo evaluation of such age-related changes in the human brain. Here, we propose to improve the interpretation of MRI findings by accounting for the fundamental effect of the water content on the imaging parameters. This approach allows for non-invasive mapping of the molecular composition in the aging human brain.

Our work is part of a major paradigm shift in the field of MRI toward in vivo histology30,36,52. The MDM approach contributes to this important change by providing a hypothesis-driven biophysical framework that was rigorously developed. We demonstrated the power of our framework, starting from simple pure lipid phantoms to more complicated lipid mixtures, and from there, to the full complexity of the brain. In the brain, we show both in vivo and post-mortem validations for the molecular sensitivity of the MDM signatures. Early observations relate different qMRI parameters to changes in the fraction of myelin20,23,30,31,32,33,36. The current approach enriches this view and provides better sensitivity to the molecular composition and fraction of myelin and other cellular tissues.

We developed a unique phantom system of lipid samples to validate our method. While the phantom system is clearly far from the complexity of brain tissue, its simplicity allowed us to verify the specificity of our method to the chemophysical environment. Remarkably, our approach revealed unique signatures for different lipids, and is therefore sensitive even to relatively subtle details that distinguish one lipid from another. We chose to validate our approach using membrane lipids based on previous experiments40,41,42,43,44,45. Nevertheless, we do acknowledge the fact that brain tissue comprises many other compounds beside lipids, such as proteins, sugars, and ions. As we have shown, these other compounds also exhibit unique dependency on MTV. The effect of such compounds, along with other factors such as microstructure, and multi-compartment organization28 is probably captured when we apply the MDM approach to the in vivo human brain. Therefore, the phantoms were made to examine the MRI sensitivity for the chemophysical environment, and the human brain data was used to measure the true biological effects in a complex in vivo environment.

Our relaxivity approach captures the molecular signatures of the tissue, but is limited in its abilities to describe the full complexity of the chemophysical environment of the human brain. For example, R1 and R2, which are used to generate the MDM signatures, are also sensitive to the iron content23,48,52. However, we found that most of our findings cannot be attributed to alterations in iron content as measured with R2* (for more details see Supplementary Note 5). While there is great importance in further isolating different molecular components, we argue that accounting for the major effect of water on qMRI parameters (for R2 distributions see Supplementary Fig. 5) is a crucial step towards more specific qMRI interpretation.

We provide evidence from lipids samples and post-mortem data for the sensitivity of the MDM signatures to the molecular environment (Figs. 1e, 3b, and 5h). The variability of MDM values between human brain regions also correlated with specific gene-expression profiles (Fig. 4). While the comparison of in vivo human brain measurements to previously published ex vivo findings is based on two different datasets, these measurements are highly stable across normal subjects and the intersubject variabilities are much smaller than the regional variability. The agreement between the modalities provides strong evidence for the ability of our method to capture molecular information.

Remarkably, we were able to demonstrate the sensitivity of MDM signatures to lipid composition using direct comparison on post-mortem porcine brains. Even though there are many challenges in scanning post-mortem tissue, segmenting it, and comparing it to anatomically relevant histological results, we were able to replicate our in vivo findings. We provide histological validation for the MRI estimation of MTV. Moreover, we find that while standard qMRI parameters and MTV do not explain the lipidomic variability across the brain, the MDM signatures are in agreement with histological results. Lipids constitute the majority of the brain’s dry weight and are known to be important for maintaining neural conduction and chemical balance53,54. The brain lipidome was shown to have a great deal of structural and functional diversity and was found to vary according to age, gender, brain region, and cell type55. Disruptions of the brain lipid metabolism have been linked to different disorders, including Alzheimer’s disease, Parkinson’s disease, depression, and anxiety7,8,11,54,55,56,57. Our results indicate that the MDM approach enhances the consistency between MRI-driven measurements and lipidomics, compared with standard qMRI parameters.

The simplicity of our model, which is based on a first-order approximation of qMRI dependencies, has great advantages in the modeling of complex environments. Importantly, we used lipids samples to show that the contributions of different mixture-components can be summed linearly (Fig. 1d). For contrast agents, the relaxivity is used to characterize the efficiency of different agents. Here, we treated the tissue itself, rather than a contrast material, as an agent to compute the relaxivity of the tissue. While relaxivity is usually calculated for R1 and R2, we extended this concept to other qMRI parameters. Our results showed that the tissue relaxivity changes as a function of the molecular composition. This suggests that the relaxivity of the tissue relates to the surface interaction between the water and the chemophysical environment. A theoretical formulation for the effect of the surface interaction on proton relaxation has been proposed before58,59. Specifically, a biophysical model for the linear relationship between R1 and R2 to the inverse of the water content (1/WC = 1/(1 – MTV)) was suggested by Fullerton et al.43. Interestingly, 1/WC varies almost linearly with MTV in the physiological range of MTV values. Applying our approach with 1/WC instead of MTV produces relatively similar results (Supplementary Fig. 28). However, using MTV as a measure of tissue relaxivity allowed us to generalize the linear model to multiple qMRI parameters, thus producing multidimensional MDM signatures.

We show that the MDM signatures allow for better understanding of the biological sources for the aging-related changes observe with MRI. Normal brain aging involves multiple changes, at both the microscale and macroscale levels. Measurements of macroscale brain volume have been widely used to characterize aging-associated atrophy. Our method of analysis can complement such findings and provide a deeper understanding of microscale processes co-occurring with atrophy. Moreover, it allows us to test whether these various microscale and macroscale processes are caused by a common factor or represent the aging mosaic. Notably, we discovered that different brain regions undergo different biological aging processes. Therefore, combining several measurements of brain tissue is crucial in order to fully describe the state of the aged brain. For example, the macroscale aging-related volume reduction in cortical gray areas was accompanied by conserved tissue density, as estimated by MTV, and region-specific chemophysical changes, as estimated by the MDM. In contrast, in white-matter areas both MDM and MTV changed with age. These microscale alterations were not accompanied by macroscale volume reduction. Our in vivo results were validated by previous histological studies, which reported that the cortex shrinks with age, while the neural density remains relatively constant49,50. In contrast, white matter was found to undergo significant loss of myelinated nerve fibers during aging51. In addition, we found that the shrinkage of the hippocampus with age is accompanied with conserved tissue density and chemophysical composition. This is in agreement with histological findings, which predict drastic changes in hippocampal tissue composition in neurological diseases such as Alzheimer, but not in normal aging49,50,60,61. In contrast, hippocampal macroscale volume reduction was observed in both normal and pathological aging2.

It should be noted that most of the human subjects recruited for this study were from the academic community. However, the different age groups were not matched for variables such as IQ and socioeconomic status. In addition, the sample size in our study was quite small. Therefore, the comparison we made between the two age groups may be affected by variables other than age. Our approach may benefit from validation based on larger quantitative MRI datasets27,62. Yet, we believe we have demonstrated the potential of our method to reveal molecular alterations in the brain. Moreover, the agreement of our findings with previous histological aging studies supports the association between the group differences we measured and brain aging. Our results suggest that the MDM approach may be very useful in differentiating the effects of normal aging from those of neurodegenerative diseases. There is also great potential for applications in other brain research fields besides aging. For example, our approach may be used to advance the study and diagnosis of brain cancer, in which the lipidomic environment undergoes considerable changes63,64,65.

To conclude, we have presented here a quantitative MRI approach that decodes the molecular composition of the aging brain. While common MRI measurements are primarily affected by the water content of the tissue, our method employed the tissue relaxivity to expose the sensitivity of MRI to the molecular microenvironment. We presented evidence from lipid samples, post-mortem porcine brains and in vivo human brains for the sensitivity of the tissue relaxivity to molecular composition. Results obtained by this method in vivo disentangled different biological processes occurring in the human brain during aging. We identified region-specific patterns of microscale aging-related changes that are associated with the molecular composition of the human brain. Moreover, we showed that, in agreement with the mosaic theory of aging, different biological age-related processes measured in vivo have unique spatial patterns throughout the brain. The ability to identify and localize different age-derived processes in vivo may further advance human brain research.

Methods

Phantom construction
The full protocol of lipids phantom preparation is described in Shtangel et al.66.

In short, we prepared liposomes from one of the following lipids: phosphatidylserine (PS), phosphatidylcholine (PtdCho), phosphatidylcholine-cholesterol (PtdCho-Chol), Phosphatidylinositol-phosphatidylcholine (PI-PtdCho), or sphingomyelin (Spg). These phantoms were designed to model biological membranes and were prepared from lipids by the hydration–dehydration dry film technique67. The lipids were dissolved over a hot plate and vortexed. Next, the solvent was removed to create a dry film by vacuum-rotational evaporation. The samples were then stirred on a hot plate at 65 °C for 2.5 h to allow the lipids to achieve their final conformation as liposomes. Liposomes were diluted with Dulbecco’s phosphate buffered saline (PBS), without calcium and magnesium (Biological Industries), to maintain physiological conditions in terms of osmolarity, ion concentrations and pH. To change the MTV of the liposome samples we varied the PBS to lipid volume ratios66. Samples were then transferred to the phantom box for scanning in a 4 mL squared polystyrene cuvettes glued to a polystyrene box, which was then filled with ~1% SeaKem Agarose (Ornat Biochemical) and ~0.0005 M Gd (Gadotetrate Melumine, (Dotarem, Guerbet)) dissolved in double distilled water (ddw). The purpose of the agar with Gd (Agar-Gd) was to stabilize the cuvettes, and to create a smooth area in the space surrounding the cuvettes that minimalized air–cuvette interfaces. In some of our experiments we used lipid mixtures composed of several lipids. We prepared nine mixtures containing different combinations of two out of three lipids (PtdChol, Spg and PS) in varying volume ratios (1:1,1:2,2:1). For each mixture, we prepared samples in which the ratio between the different lipid components remained constant while the water-to-lipid volume fraction varied.

For the bovine serum albumin (BSA) phantoms, samples were prepared by dissolving lyophilized BSA powder (Sigma Aldrich) in PBS. To change the MTV of these phantoms, we changed the BSA concentration. For the BSA + Iron phantoms, BSA was additionally mixed with a fixed concentration of 50 µg/mL ferrous sulfate heptahydrate (FeSO4*7H2O). Samples were prepared in their designated concentrations at room temperature. Prepared samples were allowed to sit overnight at 4 ℃ to ensure BSA had fully dissolved, without the need for significant agitation, which is known to cause protein cross-linking. Samples were then transferred to the phantom box for scanning.

For Glucose and Sucrose phantoms, different concentrations of D-( + )-Sucrose (Bio-Lab) and D-( + )-Glucose (Sigma) were dissolved in PBS at 40 ℃. Samples were allowed to reach room temperature before the scan.

MRI acquisition for phantoms

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1 & MTV mapping, three-dimensional (3D) Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 8°, 16°, and 30°). The TE/TR was 3.91/18 ms. The scan resolution was 1.1 × 1.1 × 0.9 mm. The same sequence was repeated with a higher resolution of 0.6 × 0.6 × 0.5 mm. The TE/TR was 4.45/18 ms. For calibration, we acquired an additional spin-echo inversion recovery (SEIR) scan. This scan was done on a single slice, with adiabatic inversion pulse and inversion times of TI = 2000, 1200, 800, 400, and 50. The TE/TR was 73/2540 ms. The scan resolution was 1.2 mm isotropic.

For quantitative T2 mapping, images were acquired with a multi spin-echo sequence with 15 equally spaced spin echoes between 10.5 ms and 157.5 ms. The TR was 4.94 s. The scan resolution was 1.2 mm isotropic. For quantitative MTsat mapping, images were acquired with the FLASH Siemens WIP 805 sequence. The TR was 23 ms for all samples except PI:PtdCho for which the TR was 72 ms. Six echoes were equally spaced between 1.93 ms to 14.58 ms. The on-resonance flip angle was 6°, the MT flip angle was 220°, and the RF offset was 700. We used 1.1-mm in-plane resolution with a slice thickness of 0.9 mm. For samples of sucrose and glucose, MTsat mapping was done similar to the human subjects, based on 3D Spoiled gradient (SPGR) echo image with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.91/28 ms. The scan resolution was 1 mm isotropic.

Estimation of qMRI parameters for phantoms

MTV and R1 estimations for the lipids samples were computed based on a the mrQ39 (https://github.com/mezera/mrQ) and Vista Lab (https://github.com/vistalab/vistasoft/wiki) software. The mrQ software was modified to suit the phantom system66. The modification utilizes the fact that the Agar-Gd filling the box around the samples is homogeneous and can, therefore, be assumed to have a constant T1 value. We used this gold standard T1 value generated from the SEIR scan to correct for the excite bias in the spoiled gradient echo scans. While the data was acquired in two different resolutions (see “MRI acquisition”), in our analysis we use the median R1 and MTV of each lipid sample and these are invariant to the resolution of acquisition (Supplementary Fig. 1e). Thus, we were able to use scans with different resolutions without damaging our results. T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68.

For quantitative MTsat mapping see the “MTsat estimation” section for human subjects.

MDM computation for phantoms

We computed the dependency of each qMRI parameter (R1, MTsat, and R2) on MTV in different lipids samples. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). To manipulate the MTV values, we scanned samples of the same lipid in varying concentrations. We computed the median MTV of each sample, along with the median of qMRI parameters. We used these data points to fit a linear model across all samples of the same lipid. The slope of this linear model represents the MTV derivative of the linear equation. We used this derivative estimate of three qMRI parameters (R1, R2, and MTsat) to compute the MDM signatures. The same procedure was used for the MDM computation of lipid mixtures.

MDM modeling of lipid mixtures

We tested the ability of MDM to predict the composition of lipid mixtures. For this analysis we used nine mixture phantoms (see “Phantom construction”), along with the three phantoms of the pure lipid constituents of the mixtures (PS, Spg, and Ptd-Cho).

In order to predict the qMRI parameters of a lipid mixture (Fig. 1d) we used Supplementary Eq. 1 (Supplementary Note 1). To further predict the composition of the mixtures (Fig. 1e) we used Supplementary Eq. 5 (Supplementary Note 2). We solved this equation using the QR factorization algorithm.

Ethics

Human experiments complied with all relevant ethical regations. The Helsinki Ethics Committee of Hadassah Hospital, Jerusalem, Israel approved the experimental procedure. Written informed consent was obtained from each participant prior to the procedure.

Human subjects

Human measurements were performed on 23 young adults (aged 27 ± 2 years, 11 females), and 18 older adults (aged 67 ± 6 years, five females). Healthy volunteers were recruited from the community surrounding the Hebrew University of Jerusalem.

MRI acquisition for human subjects

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 3.34–14.02 ms) and the TR was 19 ms (except for six young subjects for which the scan included only one TE = 3.34 ms). The scan resolution was 1 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 200, 400, 1,200, and 2400 ms. We used 2-mm in-plane resolution with a slice thickness of 3 mm. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 ms and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic. T2 scans of four subjects (one young, three old) were excluded from the analysis due to motion.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.34/27 ms. The scan resolution was 1 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0). In addition, we collected non-diffusion-weighted images with reversed phase-encode blips. For five subjects (four young, one old) we failed to acquire this correction data and they were excluded from the diffusion analysis.

Anatomical images were acquired with 3D magnetization prepared rapid gradient echo (MP-RAGE) scans for 24 of the subjects (14 from the younger subjects, 10 from the older subjects). The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms. Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) scans were acquired for the rest of the subjects. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/5000 ms.

Estimation of qMRI parameters for human subjects

Whole-brain MTV and R1 maps, together with bias correction maps of B1 + and B1-, were computed using the mrQ software39,69 (https://github.com/mezera/mrQ). Voxels in which the B1 + inhomogeneities were extrapolated and not interpolated were removed from the MTV and R1 maps. While we did not correct our MTV estimates for R2*, we showed that employing such a correction does not significantly change our results (see Supplementary Note 6, Supplementary Figs. 20–27). MTV maps of four subjects had bias in the lower part of the brain and they were therefore excluded from the analysis presented in Fig. 3, which includes ROIs in the brainstem.

Whole-brain T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68. To combine the MTV and T2 we co-registered the quantitative MTV map to the T2 map. We used the ANTS software package70 to calculate the transformation and to warp the MTV map and the segmentation. The registration was computed to match the T1 map to the T2 map. Next, we applied the calculated transformation to MTV map (since MTV and T1 are in the same imaging space) and resampled the MTV map to match the resolution of the T2 map. The same transformation was also applied to the segmentation. R2 maps were calculated as 1/T2.

Whole-brain MTsat maps were computed as described in Helms et al.37. The MTsat measurement was extracted from Eq. (1):

MTsat=𝑀0𝐵1𝛼𝑅1TR𝑆MT−(𝐵1𝛼)22−𝑅1TR
(1)
Where SMT is the signal of the SPGR scan with additional MT pulse, α is the flip angle and TR is the repetition time. Mo (the equilibrium magnetization parameter), B1 (the transmit inhomogeneity), and R1 estimations were computed from the non-MT weighted SPGR scans, during the pipeline described under “MTV & R1 estimation”. Registration of the SMT image to the imaging space of the MTV map was done using a rigid-body alignment (R1, B1, and MO are all in the same space as MTV).

Diffusion analysis was done using the FDT toolbox in FSL71,72. Susceptibility and eddy current induced distortions were corrected using the reverse phase-encode data, with the eddy and topup commands73,74. MD maps were calculated using vistasoft (https://github.com/vistalab/vistasoft/wiki). We used a rigid-body alignment to register the corrected dMRI data to the imaging space of the MTV map (Flirt, FSL). In order to calculate the MD-MTV derivatives, we resampled the MTV map and the segmentation to match the dMRI resolution.

We used the SPGR scans with multiple echoes to estimate R2*. Fitting was done through the MPM toolbox75. As we had four SPGR scans with variable flip angles, we averaged the R2* maps acquired from each of these scans for increased SNR.

Human brain segmentation

Whole-brain segmentation was computed automatically using the FreeSurfer segmentation algorithm76. For subjects who had an MP-RAGE scan, we used it as a reference. For the other subjects the MP2RAGE scan was used as a reference. These anatomical images were registered to the MTV space prior to the segmentation process, using a rigid-body alignment. Sub-cortical gray-matter structures were segmented with FSL’s FIRST tool77. To avoid partial volume effects, we removed the outer shell of each ROI and left only the core.

MDM computation in the human brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). For each ROI, we extracted the MTV values from all voxels and pooled them into 36 bins spaced equally between 0.05 and 0.40. This was done so that the linear fit would not be heavily affected by the density of the voxels in different MTV values. We removed any bins in which the number of voxels was smaller than 4% of the total voxel count in the ROI. The median MTV of each bin was computed, along with the median of the qMRI parameter. We used these data points to fit the linear model across bins using Eq. (2):

qMRIparameters=𝑎∗MTV+𝑏
(2)
The slope of this linear model (“a”) represents the MTV derivative of the linear equation. We used this derivative estimate to compute the MDM signatures.

For each subject, ROIs in which the total voxel count was smaller than a set threshold of 500 voxels for the MTsat and R1 maps, 150 voxels for the MD map, and 50 voxels for the R2 map were excluded.

Principal component analysis (PCA) in the human brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. For each MDM dimension (MTV derivatives of R1, MTsat, MD, and R2), we evaluated the median of the different brain areas across the young subjects. As each MDM dimension has different units, we then computed the z-score of each dimension across the different brain area. Finally, we performed PCA. The variables in this analysis were the different MDM dimensions, and the observations were the different brain areas. From this analysis, we derived the first PC that accounts for most of the variability in MDM signatures across the brain. To estimate the median absolute deviations (MAD) across subjects of each MDM measurement in the PC basis, we applied the z-score transformation to the original MAD and then projected them onto the PC basis.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

For the first PC of molecular composition, we followed the same procedure, but used the phospholipid composition and the ratio between phospholipids to proteins and cholesterol as variables. The data was taken from eight post-mortem human brains7. Brains were obtained from individuals between 54 and 57 years of age, which were autopsied within 24 h after death.

Linear model for prediction of human molecular composition

We used MDM measurements in order to predict the molecular composition of different brain areas (Fig. 3c). For this analysis we used Supplementary Eq. 5 in the Supplementary Note 2. We solved this equation using QR factorization algorithm (for more details see Supplementary Note 3).

Gene-expression dataset

For the gene-expression analysis we followed the work of Ben-David and Shifman46. Microarray data was acquired from the Allen Brain Atlas (http://human.brain-map.org/well_data_files) and included a total of 1340 microarray profiles from donors H0351.2001 and H0351.2002, encompassing the different regions of the human brain. The donors were 24 and 39 years old, respectively, at the time of their death, with no known psychopathologies. We used the statistical analysis described by Ben-David and Shifman46. They constructed a gene network using a weighted gene co-expression network analysis. The gene network included 19 modules of varying sizes, from 38 to 7385 genes. The module eigengenes were derived by taking the first PC of the expression values in each module. In addition, we used the gene ontology enrichment analysis described by Ben-David and Shifman to define the name of each module. The colors of the different modules in the Fig. 4 and Supplementary Fig. 10 are the same as in the original paper.

Next, we matched between the gene-expression data and the MRI measurements. This analysis was done on 35 cortical regions extracted from FreeSurfer cortical parcellation. We downloaded the T1-weighted images of the two donors provided by the Allen Brain Atlas (http://human.brain-map.org/mri_viewers/data) and used them as a reference for FreeSurfer segmentation. We then found the FreeSurfer label of each gene-expression sample using the sample’s coordinates in brain space. We removed samples for which the FreeSurfer label and the label provided in the microarray dataset did not agree (there were 72 such samples out of 697 cortical samples). For each gene module, we averaged over the eigengenes of all samples from the same cortical area across the two donors.

Last, we compared the cortical eigengene of each module to the projection of cortical areas on the first PC of MDM. In addition, we compared the modules’ eigengenes to the MTV values of the cortical areas and to the projection of cortical areas on the first PC of standard qMRI parameters (Supplementary Fig. 10). These 57 correlations were corrected for multiple comparisons using the FDR method.

Brain region’s volume computation

To estimate the volume of different brain regions, we calculated the number of voxels in the FreeSurfer segmentation of each region (see “Brain segmentation”).

R2* correction for MTV
To correct the MTV estimates for R2* we used Eq. (3):

MTV𝐶=1−(1−MTV)⋅exp(TE⋅R2∗)
(3)
Where MTVC is the corrected MTV.

Statistical analysis

The statistical significance of the differences between the age groups was computed using an independent-sample t-test (alpha = 0.05, both right and left tail) and was corrected for multiple comparisons using the false-discovery rate (FDR) method. For this analysis, MRI measurements of both hemispheres of bilateral brain regions were joined together. R2 measurements were adjusted for the number of data points. All statistical tests were two-sided.

Post-mortem tissue acquisition

Two post-mortem porcine brains were purchased from BIOTECH FARM.

Post-mortem MRI acquisition

Brains were scanned fresh (without fixation) in water within 6 h after death. Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 4.01 – 16.51 ms) and the TR was 22 ms. The scan resolution was 0.8 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 50, 200, 400, 1200 ms. The scan resolution was 2 mm isotropic. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 4.01/40 ms. The scan resolution was 0.8 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0).

For anatomical images, 3D magnetization prepared rapid gradient echo (MP-RAGE) scans were acquired. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms.

Histological analysis

Following the MRI scans the brains were dissected. Total of 42 brain regions were identified. Four samples were excluded as we were not able to properly separate the WM from the GM. One sample was excluded as we could not properly identify its anatomical origin. Additional two samples were too small for TLC analysis.

The non-water fraction (MTV) was determined by desiccation, also known as the dry-wet method. A small fraction of each brain sample (~0.25 g) was weighed. In order to completely dehydrate the fresh tissues, they were left for several days in a vacuum dessicator over silica gel at 4 °C. The experiment ended when no further weight loss occurred. The MTV of each brain sample was calculated based on the difference between the wet (Wwet) and dry (Wdry) weights of the tissue (Eq. 4):

MTV=𝑊wet−𝑊dry𝑊wet
(4)
For lipid extraction and lipidomics analysis78, Brain samples were weighted and homogenized with saline in plastic tubes on ice at concentration of 1 mg/12.5 µL. Two-hundred fifty microliters from each homogenate were utilized for lipid extraction and analysis with thin-layer chromatography (TLC). The lipid species distribution was analyzed by TLC applying 150 µg aliquots. Samples were reconstituted in 10 µL of Folch mixture and spotted on Silica-G TLC plates. Standards for each fraction were purchased from Sigma Aldrich (Rehovot, Israel) and were spotted in separate TLC lanes, i.e., 50 µg of triacylglycerides (TG), cholesterol (Chol), cholesteryl esters (CE), free fatty acids (FFA), lysophospholipids (Lyso), sphingomyelin (Spg), phosphatidylcholine (PtdCho), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylethanolamine (PE). Plates were then placed in a 20 × 20 cm TLC chamber containing petroleum ether, ethyl ether, and acetic acid (80:20:1, v/v/v) for quantification of neutral lipids or chloroform, methanol, acetic acid, and water (65:25:4:2, v:v:v:v) for quantification of polar lipids and run for 45 min. TG, Chol, CE, FFA, phospholipids (PL), Lyso, Spg, PtdCho, PI, PS, and PE bands were visualized with Iodine, scanned and quantified by Optiquant after scanning (Epson V700). Lyso, CE, TG, and PI were excluded from further analysis as their quantification was noisy and demonstrated high variability across TLC plates. This analysis was conducted under the guidance of Prof. Alicia Leikin-Frenkel in the Bert Strassburger Lipid Center, Sheba, Tel Hashomer.

Estimation of qMRI parameters in the post-mortem brain

Similar to human subjects.

Brain segmentation of post-mortem brain

Brain segmentation was done manually. Five tissue samples were excluded as we could not identify their origin location in the MRI scans.

MDM computation in the post-mortem brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas similarly to the analysis of the human subjects.

Principal component analysis (PCA) in the post-mortem brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. PCA analysis was performed with four variables corresponding to the MDM dimensions (MTV derivatives of R1, MTsat, MD, and R2), and 30 observations corresponding to the different brain regions. As each MDM dimension has different units, we first computed the z-score of each dimension across the different brain areas prior to the PCA. From this analysis we derived the first PC that accounts for most of the variability in MDM signatures across the brain.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

To estimate the variability in the lipid composition across the brain, we computed the first principal component (PC) of lipidomics. PCA analysis was performed with seven variables corresponding to the different polar and neutral lipids (Chol, FFA, PL, Spg, PtdCho, PS, PE), and 30 observations corresponding to the different brain regions. From this analysis, we derived the first PC that accounts for most of the variability in lipid composition across the brain.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

A toolbox for computing MDM signatures is available at [https://github.com/MezerLab/MDM_toolbox].

The code generating the figures of in the paper is available at [https://github.com/MezerLab/MDM_Gen_Figs].

References
1.
Peters, R. Ageing and the brain. Postgrad. Med. J. 82, 84–88 (2006).

2.
Lockhart, S. N. & DeCarli, C. Structural imaging measures of brain aging. Neuropsychol. Rev. 24, 271–289 (2014).

3.
Wozniak, J. R. & Lim, K. O. Advances in white matter imaging: a review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neurosci. Biobehav. Rev. 30, 762–774 (2006).

4.
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6, 67–77 (2010).

5.
Mrak, R. E., Griffin, S. T. & Graham, D. I. Aging-associated changes in human brain. J. Neuropathol. Exp. Neurol. 56, 1269–1275 (1997).

6.
Yankner, B. A., Lu, T. & Loerch, P. The aging brain. Annu. Rev. Pathol. 3, 41–66 (2008).

7.
Söderberg, M., Edlund, C., Kristensson, K. & Dallner, G. Lipid compositions of different regions of the human brain during aging. J. Neurochem. 54, 415–423 (1990).

8.
Lauwers, E. et al. Membrane lipids in presynaptic function and disease. Neuron 90, 11–25 (2016).

9.
Li, Q. et al. Changes in lipidome composition during brain development in humans, chimpanzees, and Macaque monkeys. Mol. Biol. Evol. 34, 1155–1166 (2017).

10.
Müller, C. P. et al. Brain membrane lipids in major depression and anxiety disorders. Biochim. Biophys. Acta-Mol. Cell Biol. Lipids 1851, 1052–1065 (2015).

11.
Naudí, A. et al. Lipidomics of human brain aging and Alzheimer’s disease pathology. Int. Rev. Neurobiol. 122, 133–189 (2015).

12.
Walker, L. C. & Herndon, J. G. Mosaic aging. Med. Hypotheses 74, 1048–1051 (2010).

13.
Cole, J. H., Marioni, R. E., Harris, S. E. & Deary, I. J. Brain age and other bodily ‘ages’: implications for neuropsychiatry. Mol. Psychiatry 1 (2018). https://doi.org/10.1038/s41380-018-0098-1.

14.
Hayflick, L. Biological aging is no longer an unsolved problem. Ann. N. Y. Acad. Sci. 1100, 1–13 (2007).

15.
Christensen, H., Mackinnon, A. J., Korten, A. & Jorm, A. F. The ‘common cause hypothesis’; of cognitive aging: evidence for not only a common factor but also specific associations of age with vision and grip strength in a cross-sectional analysis. Psychol. Aging 16, 588–599 (2001).

16.
Cole, J. H. et al. Brain age predicts mortality. Mol. Psychiatry 23, 1385–1392 (2018).

17.
Sowell, E. R., Thompson, P. M. & Toga, A. W. Mapping changes in the human cortex throughout the span of life. Neuroscience 10, 372–392 (2004).

18.
Fjell, A. M. & Walhovd, K. B. Structural brain changes in aging: courses, causes and cognitive consequences. Rev. Neurosci. 21, 187–221 (2010).

19.
Gunning-Dixon, F. M., Brickman, A. M., Cheng, J. C. & Alexopoulos, G. S. Aging of cerebral white matter: a review of MRI findings. Int. J. Geriatr. Psychiatry 24, 109–117 (2009).

20.
Callaghan, M. F. et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol. Aging 35, 1862–1872 (2014).

21.
Yeatman, J. D., Wandell, B. A. & Mezer, A. A. Lifespan maturation and degeneration of human brain white matter. Nat. Commun. 5, 4932 (2014).

22.
Cox, S. R. et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat. Commun. 7, 13629 (2016).

23.
Lorio, S. et al. Disentangling in vivo the effects of iron content and atrophy on the ageing human brain. Neuroimage 103, 280–289 (2014).

24.
Gracien, R.-M. et al. Evaluation of brain ageing: a quantitative longitudinal MRI study over 7 years. Eur. Radiol. 27, 1568–1576 (2017).

25.
Draganski, B. et al. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55, 1423–1434 (2011).

26.
Tardif, C. L. et al. Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies. Neuroimage 149, 233–243 (2017).

27.
Carey, D. et al. Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. Neuroimage 182, 429–440 (2017).

28.
Cercignani, M., Dowell, N. G. & Tofts, P. S. Quantitative MRI of the Brain: Principles of Physical Measurement. (CRC Press, United States, 2018).

29.
Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. Ser. B 111, 209–219 (1996).

30.
Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy. Curr. Opin. Neurol. 28, 313–322 (2015).

31.
Winklewski, P. J. et al. Understanding the physiopathology behind axial and radial diffusivity changes—what do we know? Front. Neurol. 9, 92 (2018).

32.
Heath, F., Hurley, S. A., Johansen-Berg, H. & Sampaio-Baptista, C. Advances in noninvasive myelin imaging. Dev. Neurobiol. 78, 136–151 (2018).

33.
Lutti, A., Dick, F., Sereno, M. I. & Weiskopf, N. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage 93, 176–188 (2014).

34.
Filo, S. & Mezer, A. A. in Quantitative MRI of the Brain: Principles of Physical Measurement (eds Cercignani, M., Dowell, N. G. & Tofts, P. S.) 55–72 (CRC Press, United States, 2018).

35.
Fullerton, G. D., Cameron, I. L. & Ord, V. A. Frequency dependence of magnetic resonance spin-lattice relaxation of protons in biological materials. Radiology 151, 135–138 (1984).

36.
Does, M. D. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 182, 136–148 (2018).

37.
Helms, G., Dathe, H., Kallenberg, K. & Dechent, P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T 1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 60, 1396–1407 (2008).

38.
Rohrer, M., Bauer, H., Mintorovitch, J., Requardt, M. & Weinmann, H. -J. Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Investig. Radiol. 40, 715–724 (2005).

39.
Mezer, A. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19, 1667–1672 (2013).

40.
Koenig, S. H. Cholesterol of myelin is the determinant of gray‐white contrast in MRI of brain. Magn. Reson. Med. 20, 285–291 (1991).

41.
Koenig, S. H., Brown, R. D., Spiller, M. & Lundbom, N. Relaxometry of brain: why white matter appears bright in MRI. Magn. Reson. Med. 14, 482–495 (1990).

42.
Kucharczyk, W., Macdonald, P. M., Stanisz, G. J. & Henkelman, R. M. Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH. Radiology 192, 521–529 (1994).

43.
Fullerton, G. D., Potter, J. L. & Dornbluth, N. C. NMR relaxation of protons in tissues and other macromolecular water solutions. Magn. Reson. Imaging 1, 209–226 (1982).

44.
Morawski, M. et al. Developing 3D microscopy with CLARITY on human brain tissue: towards a tool for informing and validating MRI-based histology. Neuroimage 182, 417–428 (2018).

45.
Leuze, C. et al. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. Neuroimage 156, 412–422 (2017).

46.
Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012).

47.
Zecca, L., Youdim, M. B. H., Riederer, P., Connor, J. R. & Crichton, R. R. Iron, brain ageing and neurodegenerative disorders. Nat. Rev. Neurosci. 5, 863–873 (2004).

48.
Langkammer, C. et al. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology 257, 455–462 (2010).

49.
Freeman, S. H. et al. Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease. J. Neuropathol. Exp. Neurol. 67, 1205–1212 (2008).

50.
Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40 (2006).

51.
Bowley, M. P., Cabral, H., Rosene, D. L. & Peters, A. Age changes in myelinated nerve fibers of the cingulate bundle and corpus callosum in the rhesus monkey. J. Comp. Neurol. 518, 3046–3064 (2010).

52.
Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S. & Weiskopf, N. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. 73, 1309–1314 (2015).

53.
Piomelli, D., Astarita, G. & Rapaka, R. A neuroscientist’s guide to lipidomics. Nat. Rev. Neurosci. 8, 743–754 (2007).

54.
Sethi, S., Hayashi, M. A., Sussulini, A., Tasic, L. & Brietzke, E. Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders. World J. Biol. Psychiatry 18, 506–520 (2017).

55.
Fantini, J. & Yahi, N. Brain Lipids in Synaptic Function and Neurological Disease: Clues to Innovative Therapeutic Strategies for Brain Disorders. (Academic Press, United States, 2015).

56.
Shinitzky, M. Patterns of lipid changes in membranes of the aged brain. Gerontology 33, 149–154 (1987).

57.
Martin, M., Dotti, C. G. & Ledesma, M. D. Brain cholesterol in normal and pathological aging. Biochim. Biophys. Acta-Mol. Cell Biol. Lipids 1801, 934–944 (2010).

58.
Calucci, L. & Forte, C. Proton longitudinal relaxation coupling in dynamically heterogeneous soft systems. Prog. Nucl. Magn. Reson. Spectrosc. 55, 296–323 (2009).

59.
Halle, B. Molecular theory of field-dependent proton spin-lattice relaxation in tissue. Magn. Reson. Med. 56, 60–72 (2006).

60.
West, M. J., Coleman, P. D., Flood, D. G. & Troncoso, J. C. Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet (Lond., Engl.) 344, 769–772 (1994).

61.
West, M. J., Kawas, C. H., Stewart, W. F., Rudow, G. L. & Troncoso, J. C. Hippocampal neurons in pre-clinical Alzheimer’s disease. Neurobiol. Aging 25, 1205–1212 (2004).

62.
Slater, D. A. et al. Evolution of white matter tract microstructure across the life span. Hum. Brain Mapp. 40, 2252–2268 (2019).

63.
Jarmusch, A. K. et al. Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS. Proc. Natl Acad. Sci. U.S.A. 113, 1486–1491 (2016).

64.
Wenk, M. R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594–610 (2005).

65.
Eberlin, L. S. et al. Classifying human brain tumors by lipid imaging with mass spectrometry. Cancer Res. 72, 645–654 (2012).

66.
Shtangel, O. & Mezer, A. A phantom system designed to assess the effects of membrane lipids on water proton relaxation. bioRxiv 387845 (2018). https://doi.org/10.1101/387845.

67.
Akbarzadeh, A. et al. Liposome: methods of preparation and applications. Liposome Technol. 6, 102 (2013).

68.
Ben-Eliezer, N., Sodickson, D. K. & Block, K. T. Rapid and accurate T 2 mapping from multi-spin-echo data using Bloch-simulation-based reconstruction. Magn. Reson. Med. 73, 809–817 (2015).

69.
Mezer, A., Rokem, A., Berman, S., Hastie, T. & Wandell, B. A. Evaluating quantitative proton-density-mapping methods. Hum. Brain Mapp. 37, 3623–3635 (2016).

70.
Avants, B. B., Tustison, N. & Song, G. Advanced normalization tools (ANTS). Insight J. (2009). http://hdl.handle.net/10380/3113

71.
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage (2004). https://doi.org/10.1016/j.neuroimage.2004.07.051.

72.
Behrens, T. E. J. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. (2003). https://doi.org/10.1002/mrm.10609.

73.
Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage (2003). https://doi.org/10.1016/S1053-8119(03)00336-7.

74.
Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage (2016). https://doi.org/10.1016/j.neuroimage.2015.10.019.

75.
Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front. Neurosci. (2013). https://doi.org/10.3389/fnins.2013.00095.

76.
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).

77.
Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage (2011). https://doi.org/10.1016/j.neuroimage.2011.02.046.

78.
Shomonov-Wagner, L., Raz, A. & Leikin-Frenkel, A. Alpha linolenic acid in maternal diet halts the lipid disarray due to saturated fatty acids in the liver of mice offspring at weaning. Lipids Health Dis. (2015). https://doi.org/10.1186/s12944-015-0012-7.

Download references

Acknowledgements

This work was supported by the ISF grant 0399306, awarded to A.A.M. We acknowledge Ady Zelman for the assistance in collecting the human MRI data. We thank Assaf Friedler for assigning research lab space and advising on the lipid sample experiments. We thank Inbal Goshen for assigning research lab space and advising on the protein and ion samples as well as the porcine brain experiments. We thank Magnus Soderberg for advising on histological data interpretation. We are grateful to Brian A. Wandell, Jason Yeatman, Hermona Soreq, Ami Citri, Mark Does, Yaniv Ziv, Ofer Yizhar, Shai Berman, Roey Schurr, Jonathan Bain, Asier Erramuzpe Aliaga, Menachem Gutman, and Esther Nachliel for their critical reading of the manuscript and very useful comments. We thank Prof. Alicia Leikin-Frenkel for her guidance with the TLC analysis. We thank Rona Shaharabani for guidance and support in the post-mortem experiments.

Affiliations

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger & Aviv A. Mezer
Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Sagiv Shifman
Contributions
S.F., O.S., and A.A.M. conceived of the presented idea. S.F. and A.A.M. wrote the manuscript and designed the figures. S.F. collected the human and non-human brain datasets and analyzed them. O.S. performed the phantom experiments and analyzed them. B.W. performed the phantom experiments for non-lipid compounds. N.S. performed the gene-expression analysis. S.S. assisted and instructed with the gene-expression analysis. A.K. performed the porcine brain dissection.

Corresponding author

Correspondence to Aviv A. Mezer.

Ethics declarations & Competing interests

A.A.M, S.F., O.S. and the Hebrew University of Jerusalem have filed a patent application describing the technology used to measure MDM in this work. The other authors declare no competing interests.

Read Full Post »

An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

3.2.9

3.2.9   An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

HER2 is an important prognostic biomarker for 20–30% of breast cancers, which is the most common cancer in women. Overexpression of the HER2 receptor stimulates breast cells to proliferate and differentiate uncontrollably, thereby enhancing the malignancy of breast cancer and resulting in a poor prognosis for affected individuals. Current therapies to suppress the overexpression of HER2 in breast cancer mainly involve treatment with HER2-specific monoclonal antibodies. However, these monoclonal anti-HER2 antibodies have severe side effects in clinical trials, such as diarrhea, abnormal liver function, and drug resistance. Removing HER2 from the plasma membrane or inhibiting the gene expression of HER2 is a promising alternative that could limit the malignancy of HER2-positive cancer cells.

DNA origami is an emerging field of DNA-based nanotechnology and intelligent DNA nanorobots show great promise in working as a drug delivery system in healthcare. Different DNA-based nanorobots have been developed as affordable and facile therapeutic drugs. In particular, many studies reported that a tetrahedral framework nucleic acid (tFNA) could serve as a promising DNA nanocarrier for many antitumor drugs, owing to its high biocompatibility and biosecurity. For example, tFNA was reported to effectively deliver paclitaxel or doxorubicin to cancer cells for reversing drug resistance, small interfering RNAs (siRNAs) have been modified into tFNA for targeted drug delivery. Moreover, the production and storage of tFNA are not complicated, and they can be quickly degraded in lysosomes by cells. Since both free HApt and tFNA can be diverted into lysosomes, so,  combining the HApt and tFNA as a novel DNA nanorobot (namely, HApt-tFNA) can be an effective strategy to improve its delivery and therapeutic efficacy in treating HER2-positive breast cancer.

Researchers reported that a DNA framework-based intelligent DNA nanorobot for selective lysosomal degradation of tumor-specific proteins on cancer cells. An anti-HER2 aptamer (HApt) was site-specifically anchored on a tetrahedral framework nucleic acid (tFNA). This DNA nanorobot (HApt-tFNA) could target HER2-positive breast cancer cells and specifically induce the lysosomal degradation of the membrane protein HER2. An injection of the DNA nanorobot into a mouse model revealed that the presence of tFNA enhanced the stability and prolonged the blood circulation time of HApt, and HApt-tFNA could therefore drive HER2 into lysosomal degradation with a higher efficiency. The formation of the HER2-HApt-tFNA complexes resulted in the HER2-mediated endocytosis and digestion in lysosomes, which effectively reduced the amount of HER2 on the cell surfaces. An increased HER2 digestion through HApt-tFNA further induced cell apoptosis and arrested cell growth. Hence, this novel DNA nanorobot sheds new light on targeted protein degradation for precision breast cancer therapy.

It was previously reported that tFNA was degraded by lysosomes and could enhance cell autophagy. Results indicated that free Cy5-HApt and Cy5-HApt-tFNA could enter the lysosomes; thus, tFNA can be regarded as an efficient nanocarrier to transmit HApt into the target organelle. The DNA nanorobot composed of HApt and tFNA showed a higher stability and a more effective performance than free HApt against HER2-positive breast cancer cells. The PI3K/AKT pathway was inhibited when membrane-bound HER2 decreased in SK-BR-3 cells under the action of HApt-tFNA. The research findings suggest that tFNA can enhance the anticancer effects of HApt on SK-BR-3 cells; while HApt-tFNA can bind to HER2 specifically, the compounded HER2-HApt-tFNA complexes can then be transferred and degraded in lysosomes. After these processes, the accumulation of HER2 in the plasma membrane would decrease, which could also influence the downstream PI3K/AKT signaling pathway that is associated with cell growth and death.

However, some limitations need to be noted when interpreting the findings: (i) the cytotoxicity of the nanorobot on HER2-positive cancer cells was weak, and the anticancer effects between conventional monoclonal antibodies and HApt-tFNA was not compared; (ii) the differences in delivery efficiency between tFNA and other nanocarriers need to be confirmed; and (iii) the confirmation of anticancer effects of HApt-tFNA on tumors within animals remains challenging. Despite these limitations, the present study provided novel evidence of the biological effects of tFNA when combined with HApt. Although the stability and the anticancer effects of HApt-tFNA may require further improvement before clinical application, this study initiates a promising step toward the development of nanomedicines with novel and intelligent DNA nanorobots for tumor treatment.

References:

https://pubs.acs.org/doi/10.1021/acs.nanolett.9b01320

https://www.ncbi.nlm.nih.gov/pubmed/27939064

https://www.ncbi.nlm.nih.gov/pubmed/11694782

https://www.ncbi.nlm.nih.gov/pubmed/27082923

https://www.ncbi.nlm.nih.gov/pubmed/25365825

https://www.ncbi.nlm.nih.gov/pubmed/26840503

https://www.ncbi.nlm.nih.gov/pubmed/29802035

Read Full Post »

Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

4.3.6

4.3.6  Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

Pancreatic cancer is a significant cause of cancer mortality; therefore, the development of early diagnostic strategies and effective treatment is essential. Improvements in imaging technology, as well as use of biomarkers are changing the way that pancreas cancer is diagnosed and staged. Although progress in treatment for pancreas cancer has been incremental, development of combination therapies involving both chemotherapeutic and biologic agents is ongoing.

Cancer is an evolutionary disease, containing the hallmarks of an asexually reproducing unicellular organism subject to evolutionary paradigms. Pancreatic ductal adenocarcinoma (PDAC) is a particularly robust example of this phenomenon. Genomic features indicate that pancreatic cancer cells are selected for fitness advantages when encountering the geographic and resource-depleted constraints of the microenvironment. Phenotypic adaptations to these pressures help disseminated cells to survive in secondary sites, a major clinical problem for patients with this disease.

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology.

PDAC is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. In the present study to comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, single-cell RNA-seq (scRNA-seq) was employed to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases. The diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, were identified in PDAC.

The researchers found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, it was found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, the findings provided a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

References:

https://www.ncbi.nlm.nih.gov/pubmed/31273297

https://www.ncbi.nlm.nih.gov/pubmed/21491194

https://www.ncbi.nlm.nih.gov/pubmed/27444064

https://www.ncbi.nlm.nih.gov/pubmed/28983043

https://www.ncbi.nlm.nih.gov/pubmed/24976721

https://www.ncbi.nlm.nih.gov/pubmed/27693023

Read Full Post »

Celiac Disease Breakthrough: (1) 472 genes regulated differently in organoids reflecting celiac disease than in non-celiac control organoids (2) bio-products derived from gut microorganisms can be employed to modify the epithelial response to gluten, a finding that could lead to future treatment strategies.

 

Reporter: Aviva Lev-Ari, PhD, RN

“These results confirm our hypothesis that genes and exposure to gluten are necessary but not sufficient, since changes in both the composition and function of the gut microbiome are also needed to switch from genetic predisposition to clinical outcome, as shown by our data,” said Alessio Fasano, HMS professor of pediatrics at Mass General, director of MIBRC and co-senior author of the paper.

https://hms.harvard.edu/news/major-shift?utm_source=Silverpop&utm_medium=email&utm_term=field_news_item_3&utm_content=HMNews05132019

 

 

Image Source: iStock/wildpixel

Article | OPEN | Published: 

Human gut derived-organoids provide model to study gluten response and effects of microbiota-derived molecules in celiac disease

Scientific Reports volume 9, Article number: 7029 (2019) | Download Citation

Abstract

Celiac disease (CD) is an immune-mediated disorder triggered by gluten exposure. The contribution of the adaptive immune response to CD pathogenesis has been extensively studied, but the absence of valid experimental models has hampered our understanding of the early steps leading to loss of gluten tolerance. Using intestinal organoids developed from duodenal biopsies from both non-celiac (NC) and celiac (CD) patients, we explored the contribution of gut epithelium to CD pathogenesis and the role of microbiota-derived molecules in modulating the epithelium’s response to gluten. When compared to NC, RNA sequencing of CD organoids revealed significantly altered expression of genes associated with gut barrier, innate immune response, and stem cell functions. Monolayers derived from CD organoids exposed to gliadin showed increased intestinal permeability and enhanced secretion of pro-inflammatory cytokines compared to NC controls. Microbiota-derived bioproducts butyrate, lactate, and polysaccharide A improved barrier function and reduced gliadin-induced cytokine secretion. We concluded that: (1) patient-derived organoids faithfully express established and newly identified molecular signatures characteristic of CD. (2) microbiota-derived bioproducts can be used to modulate the epithelial response to gluten. Finally, we validated the use of patient-derived organoids monolayers as a novel tool for the study of CD.

Mass. General researchers develop 3D “mini-gut” model to study autoimmune response to gluten in celiac and non-celiac patient tissue

Gene expression of intestinal organoids reflects functional differences found in celiac disease

In pursuit of a novel tool for the research and treatment of celiac disease, scientists at the Mucosal Immunology and Biology Research Center (MIBRC) at Massachusetts General Hospital (MGH) have validated the use of intestinal organoids. These three-dimensional tissue cultures are miniature, simplified versions of the intestine produced in vitro. Taking tissue from duodenal biopsies of celiac and non-celiac patients, researchers created the “mini-guts” to explore how the gut epithelium and microbiota-derived molecules respond to gluten, a complex class of proteins found in wheat and other grains.

“We currently have no animal model that can recapitulate the response to gluten that we see in humans,” says Stefania Senger, PhD, co-senior author of the study published in Scientific Reports this week. “Using this human tissue model, we observed that intestinal organoids express the same molecular markers as actual epithelium in the celiac tissue, and the signature gene expression reflects the functional differences that occur when epithelia of celiac disease patients are exposed to gliadin.” Gliadin and glutenin proteins are main components of gluten.

Celiac disease is triggered when genetically predisposed individuals consume gluten. The condition affects approximately 1 percent of the U.S. population. Based on current data, the onset of celiac disease is thought to be preceded by the release of the protein zonulin, which is triggered by the activation of undigested gliadin to induce an autoimmune response. This leads to increased intestinal permeability and a disrupted barrier function. Novel evidence suggests that the microorganisms in the gastrointestinal tract may play a role in the onset of celiac disease.

Earlier studies from the MIBRC group and others have shown that human organoids “retain a gene expression that recapitulates the expression of the tissue of origin, including a diseased state,” the authors write. Through RNA sequencing, the new findings validate the organoid model as a “faithful in vitro model for celiac disease,” Senger says.
Using whole-transcriptome analysis, the researchers identified 472 genes regulated differently in organoids reflecting celiac disease than in non-celiac control organoids. These included novel genes associated with epithelial functions related to the pathogenesis of celiac disease – including gut barrier maintenance, stem cell regeneration and innate immune response. A second finding of the study shows that bioproducts derived from gut microorganisms can be employed to modify the epithelial response to gluten, a finding that could lead to future treatment strategies.

“These results confirm our hypothesis that genes and exposure to gluten are necessary but not sufficient, since changes in both the composition and function of the gut microbiome are also needed to switch from genetic predisposition to clinical outcome, as shown by our data,” says Alessio Fasano, MD, director of the Mucosal Immunology and Biology Research Center and co-senior author.

Senger adds, “We believe our observations represent a major shift in the study of celiac disease. We are confident that with adequate funding we could achieve major goals that include the development and implementation of high-throughput drug screenings to quickly identify new treatments for patients and expand the organoid repository to develop more complex models and pursue personalized treatment.”
Additional co-authors of the paper are first author Rachel Freire, PhD, along with Laura Ingano and Gloria Serena, PhD, of the MGH MIBRC; Murat Cetinbas, PhD, and Ruslan Sadreyev, PhD, MGH Department of Molecular Biology; Anthony Anselmo, PhD, formerly of MGH Molecular Biology and now with PatientsLikeMe, Cambridge, Mass.; and Anna Sapone, MD, PhD, Takeda Pharmaceuticals International. Support for the study includes National Institutes of Health grants RO1 DK104344-01A1 and 1U19 AI082655-02 and the Egan Family Foundation.

SOURCE

https://www.massgeneral.org/about/pressrelease.aspx?id=2403

 

Other related articles and e-Books by LPBI Group’s Authors published on this Open Access Online Scientific Journal include the following:

 

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases

  • Metabolomics 

VOLUME 1: Metabolic Genomics and Pharmaceutics. On Amazon.com since 7/21/2015

http://www.amazon.com/dp/B012BB0ZF0

Gluten-free Diets

Writer and Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/03/01/gluten-free-diets/

 

Breakthrough Digestive Disorders Research: Conditions affecting the Gastrointestinal Tract.

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/12/12/breakthrough-digestive-disorders-research-conditions-affecting-the-gastrointestinal-tract/

 

Collagen-binding Molecular Chaperone HSP47: Role in Intestinal Fibrosis – colonic epithelial cells and subepithelial myofibroblasts

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/25/collagen-binding-molecular-chaperone-hsp47-role-in-intestinal-fibrosis-colonic-epithelial-cells-and-subepithelial-myofibroblasts/

Expanding area of Tolerance-inducing Autoimmune Disease Therapeutics: Key Players

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/01/17/expanding-area-of-tolerance-inducing-autoimmune-disease-therapeutics-key-players/

 

What is the key method to harness Inflammation to close the doors for many complex diseases?

Author and Curator: Larry H Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

Read Full Post »

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Leigh syndrome is one of the hundreds of so-called mitochondrial diseases, which are caused by defects in the mitochondria that produce 90 percent of the body’s energy. These disorders are rare; about 1,000 to 4,000 babies in the United States are born with one every year. But they are devastating and can result in grave impairment of nearly any bodily system. They are largely untreatable, uniformly incurable and very difficult to screen.

 

Leigh syndrome is a terrible disease. It emerges shortly after birth and claims one major organ after another. Movement becomes difficult, and then impossible. A tracheotomy and feeding tube are often necessary by toddlerhood, and as the disease progresses, lungs frequently have to be suctioned manually. Most children with the condition die by the age of 5 or 6.

 

Scientists have devised a procedure called mitochondrial replacement therapy (M.R.T.) that involves transplanting the nucleus of an affected egg (mitochondrial diseases are passed down from the mother’s side) into an unaffected one whose nucleus has been removed. The procedure is sometimes called “three-parent in vitro fertilization”. Mitochondria contain a minuscule amount of DNA, any resulting embryo would have mitochondrial DNA from the donor egg and nuclear DNA from each of its parents.

 

After decades of careful study in cell and animal research M.R.T. is now finally being tested in human clinical trials by doctors in Britain (no births confirmed yet officially). In the United States, however, this procedure is effectively illegal. M.R.T. does not involve altering any genetic code. Defective mitochondria are swapped out for healthy ones.

 

Mitochondrial DNA governs only a handful of basic cellular functions. It is separate from nuclear DNA, which helps determine individual traits like physical appearance, intelligence and personality. That means M.R.T. cannot be used to produce the genetically enhanced “designer babies” and thus should be allowed in humans. But, there is no way to know how safe or effective M.R.T. is until doctors and scientists test it in humans.

 

References:

 

 

The Three Parent Technique to Avoid Mitochondrial Disease in Embryo

 

 

 

 

Read Full Post »

« Newer Posts - Older Posts »