Category Archives: Molecular Genetics

Molecular Genetics and Metabolism Reports is a peer reviewed, open access journal that publishes reports describing investigations that use the tools of biochemical genetics and molecular genetics for studies of normal and disease states.

A companion title to Molecular Genetics and Metabolism, it welcomes brief research articles, sequence reports, case reports and letters to the editor.

In addition to brief research articles, sequence reports, case reports and letters to the editor are considered.

Research Areas include:

- Newborn Screening and Diagnosis of Inherited Metabolic Diseases- Clinical Management and Treatment of Inborn Errors of Metabolism- Normal and Pathogenic Functioning Related to Biochemical Genetic Disease- Biochemical Studies of Primary and Secondary Enzyme Defects- Thresholds, Moonlighting Functions of Proteins and Biochemical Network Modules- Intercellular and Intracellular Metabolic Relationships

Molecular Genetics and Metabolism Reports is a peer reviewed, open access journal that publishes reports describing investigations that use the tools of biochemical genetics and molecular genetics for studies of normal and disease states.

A companion title to Molecular Genetics and Metabolism, it

More here:
Molecular Genetics and Metabolism Reports - ScienceDirect

Molecular Genetics and Metabolism contributes to the understanding of the metabolic and molecular basis of disease. This peer reviewed journal publishes articles describing investigations that use the tools of biochemical genetics and molecular genetics for studies of normal and disease states in humans and animal models.

In addition to original research articles, minireviews reporting timely advances and commentaries providing novel insights are considered.

Research Areas include:

- Newborn Screening and Diagnosis of Inherited Metabolic Diseases- Clinical Management and Treatment of Inborn Errors of Metabolism- Normal and Pathogenic Functioning Related to Biochemical Genetic Disease- Biochemical Studies of Primary and Secondary Enzyme Defects- Thresholds, Moonlighting Functions of Proteins and Biochemical Network Modules- Intercellular and Intracellular Metabolic Relationships

Authors are also welcome to submit to the journal?s open access companion title, Molecular Genetics and Metabolism Reports, which welcomes brief research articles, sequence reports, case reports and letters to the editors.

Molecular Genetics and Metabolism contributes to the understanding of the metabolic and molecular basis of disease. This peer reviewed journal publishes articles describing investigations that use the tools of biochemical genetics and molecular genetics for studies of normal and disease states in

Follow this link:
Molecular Genetics and Metabolism | Journal - ScienceDirect

Technique to deduce the time in prehistory when two or more life forms diverged

The molecular clock is a figurative term for a technique that uses the mutation rate of biomolecules to deduce the time in prehistory when two or more life forms diverged. The biomolecular data used for such calculations are usually nucleotide sequences for DNA, RNA, or amino acid sequences for proteins. The benchmarks for determining the mutation rate are often fossil or archaeological dates. The molecular clock was first tested in 1962 on the hemoglobin protein variants of various animals, and is commonly used in molecular evolution to estimate times of speciation or radiation. It is sometimes called a gene clock or an evolutionary clock.

The notion of the existence of a so-called "molecular clock" was first attributed to mile Zuckerkandl and Linus Pauling who, in 1962, noticed that the number of amino acid differences in hemoglobin between different lineages changes roughly linearly with time, as estimated from fossil evidence.[1] They generalized this observation to assert that the rate of evolutionary change of any specified protein was approximately constant over time and over different lineages (known as the molecular clock hypothesis).

The genetic equidistance phenomenon was first noted in 1963 by Emanuel Margoliash, who wrote: "It appears that the number of residue differences between cytochrome c of any two species is mostly conditioned by the time elapsed since the lines of evolution leading to these two species originally diverged. If this is correct, the cytochrome c of all mammals should be equally different from the cytochrome c of all birds. Since fish diverges from the main stem of vertebrate evolution earlier than either birds or mammals, the cytochrome c of both mammals and birds should be equally different from the cytochrome c of fish. Similarly, all vertebrate cytochrome c should be equally different from the yeast protein."[2] For example, the difference between the cytochrome c of a carp and a frog, turtle, chicken, rabbit, and horse is a very constant 13% to 14%. Similarly, the difference between the cytochrome c of a bacterium and yeast, wheat, moth, tuna, pigeon, and horse ranges from 64% to 69%. Together with the work of Emile Zuckerkandl and Linus Pauling, the genetic equidistance result directly led to the formal postulation of the molecular clock hypothesis in the early 1960s.[3]

Similarly, Vincent Sarich and Allan Wilson in 1967 demonstrated that molecular differences among modern Primates in albumin proteins showed that approximately constant rates of change had occurred in all the lineages they assessed.[4] The basic logic of their analysis involved recognizing that if one species lineage had evolved more quickly than a sister species lineage since their common ancestor, then the molecular differences between an outgroup (more distantly related) species and the faster-evolving species should be larger (since more molecular changes would have accumulated on that lineage) than the molecular differences between the outgroup species and the slower-evolving species. This method is known as the relative rate test. Sarich and Wilson's paper reported, for example, that human (Homo sapiens) and chimpanzee (Pan troglodytes) albumin immunological cross-reactions suggested they were about equally different from Ceboidea (New World Monkey) species (within experimental error). This meant that they had both accumulated approximately equal changes in albumin since their shared common ancestor. This pattern was also found for all the primate comparisons they tested. When calibrated with the few well-documented fossil branch points (such as no Primate fossils of modern aspect found before the K-T boundary), this led Sarich and Wilson to argue that the human-chimp divergence probably occurred only ~46 million years ago.[5]

The observation of a clock-like rate of molecular change was originally purely phenomenological. Later, the work of Motoo Kimura[6] developed the neutral theory of molecular evolution, which predicted a molecular clock. Let there be N individuals, and to keep this calculation simple, let the individuals be haploid (i.e. have one copy of each gene). Let the rate of neutral mutations (i.e. mutations with no effect on fitness) in a new individual be {displaystyle mu } . The probability that this new mutation will become fixed in the population is then 1/N, since each copy of the gene is as good as any other. Every generation, each individual can have new mutations, so there are {displaystyle mu } N new neutral mutations in the population as a whole. That means that each generation, {displaystyle mu } new neutral mutations will become fixed. If most changes seen during molecular evolution are neutral, then fixations in a population will accumulate at a clock-rate that is equal to the rate of neutral mutations in an individual.

The molecular clock alone can only say that one time period is twice as long as another: it cannot assign concrete dates. For viral phylogenetics and ancient DNA studiestwo areas of evolutionary biology where it is possible to sample sequences over an evolutionary timescalethe dates of the intermediate samples can be used to more precisely calibrate the molecular clock. However, most phylogenies require that the molecular clock be calibrated against independent evidence about dates, such as the fossil record.[7] There are two general methods for calibrating the molecular clock using fossil data: node calibration and tip calibration.[8]

Sometimes referred to as node dating, node calibration is a method for phylogeny calibration that is done by placing fossil constraints at nodes. A node calibration fossil is the oldest discovered representative of that clade, which is used to constrain its minimum age. Due to the fragmentary nature of the fossil record, the true most recent common ancestor of a clade will likely never be found.[8] In order to account for this in node calibration analyses, a maximum clade age must be estimated. Determining the maximum clade age is challenging because it relies on negative evidencethe absence of older fossils in that clade. There are a number of methods for deriving the maximum clade age using birth-death models, fossil stratigraphic distribution analyses, or taphonomic controls.[9] Alternatively, instead of a maximum and a minimum, a prior probability of the divergence time can be established and used to calibrate the clock. There are several prior probability distributions including normal, lognormal, exponential, gamma, uniform, etc.) that can be used to express the probability of the true age of divergence relative to the age of the fossil;[10] however, there are very few methods for estimating the shape and parameters of the probability distribution empirically.[11] The placement of calibration nodes on the tree informs the placement of the unconstrained nodes, giving divergence date estimates across the phylogeny. Historical methods of clock calibration could only make use of a single fossil constraint (non-parametric rate smoothing),[12] while modern analyses (BEAST[10] and r8s[13]) allow for the use of multiple fossils to calibrate the molecular clock. Simulation studies have shown that increasing the number of fossil constraints increases the accuracy of divergence time estimation.[14]

Sometimes referred to as tip dating, tip calibration is a method of molecular clock calibration in which fossils are treated as taxa and placed on the tips of the tree. This is achieved by creating a matrix that includes a molecular dataset for the extant taxa along with a morphological dataset for both the extinct and the extant taxa.[9] Unlike node calibration, this method reconstructs the tree topology and places the fossils simultaneously. Molecular and morphological models work together simultaneously, allowing morphology to inform the placement of fossils.[8] Tip calibration makes use of all relevant fossil taxa during clock calibration, rather than relying on only the oldest fossil of each clade. This method does not rely on the interpretation of negative evidence to infer maximum clade ages.[9]

Demographic changes in populations can be detected as fluctuations in historical coalescent effective population size from a sample of extant genetic variation in the population using coalescent theory.[15][16][17] Ancient population expansions that are well documented and dated in the geological record can be used to calibrate a rate of molecular evolution in a manner similar to node calibration. However, instead of calibrating from the known age of a node, expansion calibration uses a two-epoch model of constant population size followed by population growth, with the time of transition between epochs being the parameter of interest for calibration.[18][19] Expansion calibration works at shorter, intraspecific timescales in comparison to node calibration, because expansions can only be detected after the most recent common ancestor of the species in question. Expansion dating has been used to show that molecular clock rates can be inflated at short timescales[18] (< 1 MY) due to incomplete fixation of alleles, as discussed below[20][21]

This approach to tip calibration goes a step further by simultaneously estimating fossil placement, topology, and the evolutionary timescale. In this method, the age of a fossil can inform its phylogenetic position in addition to morphology. By allowing all aspects of tree reconstruction to occur simultaneously, the risk of biased results is decreased.[8] This approach has been improved upon by pairing it with different models. One current method of molecular clock calibration is total evidence dating paired with the fossilized birth-death (FBD) model and a model of morphological evolution.[22] The FBD model is novel in that it allows for "sampled ancestors", which are fossil taxa that are the direct ancestor of a living taxon or lineage. This allows fossils to be placed on a branch above an extant organism, rather than being confined to the tips.[23]

Bayesian methods can provide more appropriate estimates of divergence times, especially if large datasetssuch as those yielded by phylogenomicsare employed.[24]

Sometimes only a single divergence date can be estimated from fossils, with all other dates inferred from that. Other sets of species have abundant fossils available, allowing the hypothesis of constant divergence rates to be tested. DNA sequences experiencing low levels of negative selection showed divergence rates of 0.70.8% perMyr in bacteria, mammals, invertebrates, and plants.[25] In the same study, genomic regions experiencing very high negative or purifying selection (encoding rRNA) were considerably slower (1% per 50Myr).

In addition to such variation in rate with genomic position, since the early 1990s variation among taxa has proven fertile ground for research too,[26] even over comparatively short periods of evolutionary time (for example mockingbirds[27]). Tube-nosed seabirds have molecular clocks that on average run at half speed of many other birds,[28] possibly due to long generation times, and many turtles have a molecular clock running at one-eighth the speed it does in small mammals, or even slower.[29] Effects of small population size are also likely to confound molecular clock analyses. Researchers such as Francisco J. Ayala have more fundamentally challenged the molecular clock hypothesis.[30][31][32] According to Ayala's 1999 study, five factors combine to limit the application of molecular clock models:

Molecular clock users have developed workaround solutions using a number of statistical approaches including maximum likelihood techniques and later Bayesian modeling. In particular, models that take into account rate variation across lineages have been proposed in order to obtain better estimates of divergence times. These models are called relaxed molecular clocks[33] because they represent an intermediate position between the 'strict' molecular clock hypothesis and Joseph Felsenstein's many-rates model[34] and are made possible through MCMC techniques that explore a weighted range of tree topologies and simultaneously estimate parameters of the chosen substitution model. It must be remembered that divergence dates inferred using a molecular clock are based on statistical inference and not on direct evidence.

The molecular clock runs into particular challenges at very short and very long timescales. At long timescales, the problem is saturation. When enough time has passed, many sites have undergone more than one change, but it is impossible to detect more than one. This means that the observed number of changes is no longer linear with time, but instead flattens out. Even at intermediate genetic distances, with phylogenetic data still sufficient to estimate topology, signal for the overall scale of the tree can be weak under complex likelihood models, leading to highly uncertain molecular clock estimates.[35]

At very short time scales, many differences between samples do not represent fixation of different sequences in the different populations. Instead, they represent alternative alleles that were both present as part of a polymorphism in the common ancestor. The inclusion of differences that have not yet become fixedleads to a potentially dramatic inflation of the apparent rate of the molecular clock at very short timescales.[21][36]

The molecular clock technique is an important tool in molecular systematics, the use of molecular genetics information to determine the correct scientific classification of organisms or to study variation in selective forces. Knowledge of approximately constant rate of molecular evolution in particular sets of lineages also facilitates estimation of the dates of phylogenetic events, including those not documented by fossils, such as the divergences between living taxa. In these casesespecially over long stretches of timethe limitations of the molecular clock hypothesis (above) must be considered; such estimates may be off by 50% or more.

Originally posted here:
Molecular clock - Wikipedia

1953 scientific paper on the helical structure of DNA by James Watson and Francis Crick

"Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid" was the first article published to describe the discovery of the double helix structure of DNA, using X-ray diffraction and the mathematics of a helix transform. It was published by Francis Crick and James D. Watson in the scientific journal Nature on pages 737738 of its 171st volume (dated 25 April 1953).[1][2]

This article is often termed a "pearl" of science because it is brief and contains the answer to a fundamental mystery about living organisms. This mystery was the question of how it is possible that genetic instructions are held inside organisms and how they are passed from generation to generation. The article presents a simple and elegant solution, which surprised many biologists at the time who believed that DNA transmission was going to be more difficult to deduce and understand. The discovery had a major impact on biology, particularly in the field of genetics, enabling later researchers to understand the genetic code.

The application of physics and chemistry to biological problems led to the development of molecular biology, which is particularly concerned with the flow and consequences of biological information from DNA to proteins. The discovery of the DNA double helix made clear that genes are functionally defined parts of DNA molecules, and that there must be a way for cells to translate the information in DNA to specific amino acids, which are used in order to make proteins.

Linus Pauling was a chemist who was very influential in developing an understanding of the structure of biological molecules. In 1951, Pauling published the structure of the alpha helix, a fundamentally important structural component of proteins. In early 1953, Pauling published a triple helix model of DNA, which subsequently turned out to be incorrect.[3] Both Crick, and particularly Watson, thought that they were racing against Pauling to discover the structure of DNA.

Max Delbrck was a physicist who recognized some of the biological implications of quantum physics. Delbruck's thinking about the physical basis of life stimulated Erwin Schrdinger to write, What Is Life? Schrdinger's book was an important influence on Crick and Watson. Delbruck's efforts to promote the "Phage Group" (exploring genetics by way of the viruses that infect bacteria) was important in the early development of molecular biology in general and the development of Watson's scientific interests in particular.[4]

Crick, Watson, and Maurice Wilkins who won the Nobel Prize for Medicine in recognition of their discovery of the DNA double helix.

It is not always the case that the structure of a molecule is easy to relate to its function. What makes the structure of DNA so obviously related to its function was described modestly at the end of the article: "It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material".

The "specific pairing" is a key feature of the Watson and Crick model of DNA, the pairing of nucleotide subunits.[5] In DNA, the amount of guanine is equal to cytosine and the amount of adenine is equal to thymine. The A:T and C:G pairs are structurally similar. In particular, the length of each base pair is the same and they fit equally between the two sugar-phosphate backbones. The base pairs are held together by hydrogen bonds, a type of chemical attraction that is easy to break and easy to reform. After realizing the structural similarity of the A:T and C:G pairs, Watson and Crick soon produced their double helix model of DNA with the hydrogen bonds at the core of the helix providing a way to unzip the two complementary strands for easy replication: the last key requirement for a likely model of the genetic molecule.

Indeed, the base-pairing did suggest a way to copy a DNA molecule. Just pull apart the two sugar-phosphate backbones, each with its hydrogen bonded A, T, G, and C components. Each strand could then be used as a template for assembly of a new base-pair complementary strand.

When Watson and Crick produced their double helix model of DNA, it was known that most of the specialized features of the many different life forms on Earth are made possible by proteins. Structurally, proteins are long chains of amino acid subunits. In some way, the genetic molecule, DNA, had to contain instructions for how to make the thousands of proteins found in cells. From the DNA double helix model, it was clear that there must be some correspondence between the linear sequences of nucleotides in DNA molecules to the linear sequences of amino acids in proteins. The details of how sequences of DNA instruct cells to make specific proteins was worked out by molecular biologists during the period from 1953 to 1965. Francis Crick played an integral role in both the theory and analysis of the experiments that led to an improved understanding of the genetic code.[6]

Other advances in molecular biology stemming from the discovery of the DNA double helix eventually led to ways to sequence genes. James Watson directed the Human Genome Project at the National Institutes of Health.[7] The ability to sequence and manipulate DNA is now central to the biotechnology industry and modern medicine. The austere beauty of the structure and the practical implications of the DNA double helix combined to make Molecular structure of Nucleic Acids; A Structure for Deoxyribose Nucleic Acid one of the most prominent biology articles of the twentieth century.

Although Watson and Crick were first to put together all the scattered fragments of information that were required to produce a successful molecular model of DNA, their findings had been based on data collected by researchers in several other laboratories. For example, they drew on published research relating to the discovery of Hydrogen bonds in DNA by John Masson Gulland, Denis Jordan and their colleagues at University College Nottingham in 1947.[8][9][10] However the discovery of the DNA double helix also used a considerable amount of material from the unpublished work of Rosalind Franklin, A.R. Stokes, Maurice Wilkins, and H.R. Wilson at King's College London. Key data from Wilkins, Stokes, and Wilson, and, separately, by Franklin and Gosling, were published in two separate additional articles in the same issue of Nature with the article by Watson and Crick.[11][12] The article by Watson and Crick acknowledged that they had been "stimulated" by experimental results from the King's College researchers, and a similar acknowledgment was published by Wilkins, Stokes, and Wilson in the following three-page article.

In 1968, Watson published a highly controversial autobiographical account of the discovery of the double-helical, molecular structure of DNA called The Double Helix, which was not publicly accepted either by Crick or Wilkins.[13] Furthermore, Erwin Chargaff also printed a rather "unsympathetic review" of Watson's book in the 29 March 1968 issue of Science. In the book, Watson stated among other things that he and Crick had access to some of Franklin's data from a source that she was not aware of, and also that he had seenwithout her permissionthe B-DNA X-ray diffraction pattern obtained by Franklin and Gosling in May 1952 at King's in London. In particular, in late 1952, Franklin had submitted a progress report to the Medical Research Council, which was reviewed by Max Perutz, then at the Cavendish Laboratory of the University of Cambridge. Watson and Crick also worked in the MRC-supported Cavendish Laboratory in Cambridge whereas Wilkins and Franklin were in the MRC-supported laboratory at King's in London. Such MRC reports were not usually widely circulated, but Crick read a copy of Franklin's research summary in early 1953.[13][14]

Perutz's justification for passing Franklin's report about the crystallographic unit of the B-DNA and A-DNA structures to both Crick and Watson was that the report contained information which Watson had heard before, in November 1951, when Franklin talked about her unpublished results with Raymond Gosling during a meeting arranged by M.H.F. Wilkins at King's College, following a request from Crick and Watson;[15] Perutz said he had not acted unethically because the report had been part of an effort to promote wider contact between different MRC research groups and was not confidential.[16] This justification would exclude Crick, who was not present at the November 1951 meeting, yet Perutz also gave him access to Franklin's MRC report data. Crick and Watson then sought permission from Cavendish Laboratory head William Lawrence Bragg, to publish their double-helix molecular model of DNA based on data from Franklin and Wilkins.

By November 1951, Watson had acquired little training in X-ray crystallography, by his own admission, and thus had not fully understood what Franklin was saying about the structural symmetry of the DNA molecule.[14] Crick, however, knowing the Fourier transforms of Bessel functions that represent the X-ray diffraction patterns of helical structures of atoms, correctly interpreted further one of Franklin's experimental findings as indicating that DNA was most likely to be a double helix with the two polynucleotide chains running in opposite directions. Crick was thus in a unique position to make this interpretation because he had formerly worked on the X-ray diffraction data for other large molecules that had helical symmetry similar to that of DNA. Franklin, on the other hand, rejected the first molecular model building approach proposed by Crick and Watson: the first DNA model, which in 1952 Watson presented to her and to Wilkins in London, had an obviously incorrect structure with hydrated charged groups on the inside of the model, rather than on the outside. Watson explicitly admitted this in his book The Double Helix.[14]

See more here:
Molecular Structure of Nucleic Acids: A Structure for Deoxyribose ...

The Department of Molecular Genetics is administered from the Medical Sciences Building and has nearly 100 faculty members whose labs are located within the Medical Science Building, the Best Institute, the Donnelly Centre for Cellular and Biomolecular Research, the FitzGerald Building, the Hospital for Sick Children, Mount Sinai Hospital, the Ontario Institute for Cancer Research, and Princess Margaret Hospital.

The Master of Science and Doctor of Philosophy programs in Molecular Genetics offer research training in a broad range of genetic systems from bacteria and viruses to humans. Research projects include DNA repair, recombination and segregation, transcription, RNA splicing and catalysis, regulation of gene expression, signal transduction, interactions of host cells with bacteria and viruses, developmental genetics of simple organisms (worms and fruit flies) as well as complex organisms (mice), molecular neurobiology, molecular immunology, cancer biology and virology, structural biology, and human genetics and gene therapy.

Students may also be interested in the combined degree program inMedicine, Doctor of / Doctor of Philosophy (MD/PhD).

See video: Explore Graduate Programs at the Faculty of Medicine.

More:
Molecular Genetics School of Graduate Studies - University of Toronto

Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry. 2018;75:33646.

PubMed PubMed Central Article Google Scholar

Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299312.

PubMed Article Google Scholar

Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398:170012.

Article Google Scholar

American Psychiatric Association DS, Association AP, Others. Diagnostic and statistical manual of mental disorders: DSM-5, Vol. 5. Washington, DC: American Psychiatric Association; 2013.

Book Google Scholar

Caldiroli A, Capuzzi E, Tagliabue I, Capellazzi M, Marcatili M, Mucci F, et al. Augmentative pharmacological strategies in treatment-resistant major depression: a comprehensive review. Int J Mol Sci. 2021;22:13070.

CAS PubMed PubMed Central Article Google Scholar

Zhuo C, Li G, Lin X, Jiang D, Xu Y, Tian H, et al. The rise and fall of MRI studies in major depressive disorder. Transl Psychiatry. 2019;9:335.

PubMed PubMed Central Article Google Scholar

Nolan M, Roman E, Nasa A, Levins KJ, OHanlon E, OKeane V, et al. Hippocampal and amygdalar volume changes in major depressive disorder: a targeted review and focus on stress. Chronic Stress. 2020;4:2470547020944553.

PubMed PubMed Central Article Google Scholar

Hellewell SC, Welton T, Maller JJ, Lyon M, Korgaonkar MS, Koslow SH, et al. Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder. Transl Psychiatry. 2019;9:176.

PubMed PubMed Central Article Google Scholar

Schmaal L, Pozzi E, Ho TC, van Velzen LS, Veer IM, Opel N, et al. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing. Transl Psychiatry. 2020;10:172.

PubMed PubMed Central Article Google Scholar

Stockmeier CA, Rajkowska G. Cellular abnormalities in depression: evidence from postmortem brain tissue. Dialogues Clin Neurosci. 2004;6:18597.

PubMed PubMed Central Article Google Scholar

Duric V, Banasr M, Stockmeier CA, Simen AA, Newton SS, Overholser JC, et al. Altered expression of synapse and glutamate related genes in post-mortem hippocampus of depressed subjects. Int J Neuropsychopharmacol. 2013;16:6982.

CAS PubMed Article Google Scholar

Li J, Seidlitz J, Suckling J, Fan F, Ji GJ, Meng Y, et al. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun. 2021;12:1647.

CAS PubMed PubMed Central Article Google Scholar

Prentner R. Consciousness: a molecular perspective. Philosophies. 2017;2:26.

Article Google Scholar

Sohn E. Decoding the neuroscience of consciousness. Nature. 2019;571:S25.

CAS PubMed Article Google Scholar

Shadrina M, Bondarenko EA, Slominsky PA. Genetics factors in major depression disease. Front Psychiatry. 2018;9:334.

PubMed PubMed Central Article Google Scholar

Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke S, Wray NR, Lewis CM, Hamilton SP, Weissman MM, et al. A mega-analysis of genome-wide association studies for major depressive disorder. Mol Psychiatry. 2013;18:497511.

Article Google Scholar

Viktorin A, Meltzer-Brody S, Kuja-Halkola R, Sullivan PF, Landn M, Lichtenstein P, et al. Heritability of perinatal depression and genetic overlap with nonperinatal depression. Am J Psychiatry. 2016;173:15865.

PubMed Article Google Scholar

Levey DF, Stein MB, Wendt FR, Pathak GA, Zhou H, Aslan M, et al. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nat Neurosci. 2021;24:95463.

CAS PubMed PubMed Central Article Google Scholar

Noh K, Park JC, Han JS, Lee SJ. From bound cells comes a sound mind: the role of neuronal growth regulator 1 in psychiatric disorders. Exp Neurobiol. 2020;29:110.

PubMed PubMed Central Article Google Scholar

Dennis EL, Jahanshad N, Braskie MN, Warstadt NM, Hibar DP, Kohannim O, et al. Obesity gene NEGR1 associated with white matter integrity in healthy young adults. Neuroimage. 2014;102:54857.

CAS PubMed Article Google Scholar

Amare AT, Schubert KO, Tekola-Ayele F, Hsu YH, Sangkuhl K, Jenkins G, et al. The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression. J Neural Transm. 2019;126:3545.

CAS PubMed Article Google Scholar

Zhang YQ, Lin WP, Huang LP, Zhao B, Zhang CC, Yin DM. Dopamine D2 receptor regulates cortical synaptic pruning in rodents. Nat Commun. 2021;12:6444.

CAS PubMed PubMed Central Article Google Scholar

Wagnon JL, Briese M, Sun W, Mahaffey CL, Curk T, Rot G, et al. CELF4 regulates translation and local abundance of a vast set of mRNAs, including genes associated with regulation of synaptic function. PLoS Genet. 2012;8:e1003067.

CAS PubMed PubMed Central Article Google Scholar

Shen Y, Zhang C, Xiao K, Liu D, Xie G. CELF4 regulates spine formation and depression-like behaviors of mice. Biochem Biophys Res Commun. 2022;605:3944.

CAS PubMed Article Google Scholar

Starnawska A, Tan Q, Soerensen M, McGue M, Mors O, Brglum AD, et al. Epigenome-wide association study of depression symptomatology in elderly monozygotic twins. Transl Psychiatry. 2019;9:214.

CAS PubMed PubMed Central Article Google Scholar

Chan RF, Turecki G, Shabalin AA, Guintivano J, Zhao M, Xie LY, et al. Cell typespecific methylome-wide association studies implicate neurotrophin and innate immune signaling in major depressive disorder. Biol Psychiatry. 2020;87:43142.

CAS PubMed Article Google Scholar

Roberson-Nay R, Lapato DM, Wolen AR, Lancaster EE, Webb BT, Verhulst B, et al. An epigenome-wide association study of early-onset major depression in monozygotic twins. Transl Psychiatry. 2020;10:301.

CAS PubMed PubMed Central Article Google Scholar

Litke C, Bading H, Mauceri D. Histone deacetylase 4 shapes neuronal morphology via a mechanism involving regulation of expression of vascular endothelial growth factor D. J Biol Chem. 2018;293:8196207.

CAS PubMed PubMed Central Article Google Scholar

Kim MS, Akhtar MW, Adachi M, Mahgoub M, Bassel-Duby R, Kavalali ET, et al. An essential role for histone deacetylase 4 in synaptic plasticity and memory formation. J Neurosci. 2012;32:1087986.

CAS PubMed PubMed Central Article Google Scholar

Shi Y, Wang Q, Song R, Kong Y, Zhang Z. Non-coding RNAs in depression: promising diagnostic and therapeutic biomarkers. EBioMedicine. 2021;71:103569.

CAS PubMed PubMed Central Article Google Scholar

Dwivedi Y. MicroRNAs in depression and suicide: recent insights and future perspectives. J Affect Disord. 2018;240:14654.

CAS PubMed PubMed Central Article Google Scholar

Deussing JM, Jakovcevski M. Histone modifications in major depressive disorder and related rodent models. Adv Exp Med Biol. 2017;978:16983.

CAS PubMed Article Google Scholar

Homorogan C, Nitusca D, Seclaman E, Enatescu V, Marian C. Uncovering the roles of microRNAs in major depressive disorder: from candidate diagnostic biomarkers to treatment response indicators. Life. 2021;11:1073.

CAS PubMed PubMed Central Article Google Scholar

Xu N, Li AD, Ji LL, Ye Y, Wang ZY, Tong L. miR-132 regulates the expression of synaptic proteins in APP/PS1 transgenic mice through C1q. Eur J Histochem. 2019;63:3008.

PubMed Central Article Google Scholar

Scott HL, Tamagnini F, Narduzzo KE, Howarth JL, Lee YB, Wong LF, et al. MicroRNA-132 regulates recognition memory and synaptic plasticity in the perirhinal cortex. Eur J Neurosci. 2012;36:29418.

PubMed PubMed Central Article Google Scholar

Hamon M, Blier P. Monoamine neurocircuitry in depression and strategies for new treatments. Prog Neuropsychopharmacol Biol Psychiatry. 2013;45:5463.

CAS PubMed Article Google Scholar

Kaltenboeck A, Harmer C. The neuroscience of depressive disorders: a brief review of the past and some considerations about the future. Brain Neurosci Adv. 2018;2:2398212818799269.

PubMed PubMed Central Article Google Scholar

Pan JX, Xia JJ, Deng FL, Liang WW, Wu J, Yin BM, et al. Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Transl Psychiatry. 2018;8:130.

PubMed PubMed Central Article Google Scholar

Fogaa MV, Duman RS. Cortical GABAergic dysfunction in stress and depression: new insights for therapeutic interventions. Front Cell Neurosci. 2019;13:87.

PubMed PubMed Central Article Google Scholar

Luscher B, Shen Q, Sahir N. The GABAergic deficit hypothesis of major depressive disorder. Mol Psychiatry. 2011;16:383406.

CAS PubMed Article Google Scholar

Duman RS, Sanacora G, Krystal JH. Altered connectivity in depression: GABA and glutamate neurotransmitter deficits and reversal by novel treatments. Neuron. 2019;102:7590.

CAS PubMed PubMed Central Article Google Scholar

Shin C, Kim YK. Ketamine in major depressive disorder: mechanisms and future perspectives. Psychiatry Investig. 2020;17:18192.

CAS PubMed PubMed Central Article Google Scholar

Zanos P, Moaddel R, Morris PJ, Georgiou P, Fischell J, Elmer GI, et al. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature. 2016;533:4816.

CAS PubMed PubMed Central Article Google Scholar

Edinoff AN, Odisho AS, Lewis K, Kaskas A, Hunt G, Cornett EM, et al. Brexanolone, a GABAA modulator, in the treatment of postpartum depression in adults: a comprehensive review. Front Psychiatry. 2021;12:699740.

PubMed PubMed Central Article Google Scholar

Prez de la Mora M, Borroto-Escuela DO, Crespo-Ramrez M, Rejn-Orantes JDC, Palacios-Lagunas DA, Martnez-Mata MK, et al. Dysfunctional heteroreceptor complexes as novel targets for the treatment of major depressive and anxiety disorders. Cells. 2022;11:1826.

PubMed PubMed Central Article Google Scholar

Reeves KC, Shah N, Muoz B, Atwood BK. Opioid receptor-mediated regulation of neurotransmission in the brain. Front Mol Neurosci. 2022;15:919773.

CAS PubMed PubMed Central Article Google Scholar

Puryear CB, Brooks J, Tan L, Smith K, Li Y, Cunningham J, et al. Opioid receptor modulation of neural circuits in depression: what can be learned from preclinical data? Neurosci Biobehav Rev. 2020;108:65878.

PubMed Article Google Scholar

Pecia M, Karp JF, Mathew S, Todtenkopf MS, Ehrich EW, Zubieta JK. Endogenous opioid system dysregulation in depression: implications for new therapeutic approaches. Mol Psychiatry. 2019;24:57687.

PubMed Article Google Scholar

Browne CA, Lucki I. Targeting opioid dysregulation in depression for the development of novel therapeutics. Pharm Ther. 2019;201:5176.

CAS Article Google Scholar

Hsu DT, Sanford BJ, Meyers KK, Love TM, Hazlett KE, Walker SJ, et al. It still hurts: altered endogenous opioid activity in the brain during social rejection and acceptance in major depressive disorder. Mol Psychiatry. 2015;20:193200.

CAS PubMed PubMed Central Article Google Scholar

Continued here:
Molecular pathways of major depressive disorder converge on the synapse | Molecular Psychiatry - Nature.com

A team of LSU researchers has developed a way to determine which drug therapies work best against an individual's unique type of cancer, possibly providing a way to find cures more quickly and make treatment more affordable.

The interdisciplinary team includes researchers from the School of Veterinary Medicine, College of Science, College of Engineering and the Center for Computation & Technology. It created CancerOmicsNet, a new drug discovery engine run by artificial intelligence.

Using algorithms originally designed to map complex social networks, like those utilized by Facebook, researchers generated three-dimensional graphs of molecular datasets that include cancer cell lines, drug compounds and interactions among proteins inside the human body.

The graphs are then analyzed and interconnected by AI, forming a much clearer picture of how a specific cancer would respond to a specific drug.

Dr. Michal Brylinski, associate professor of computational biology at LSU, said that the team used established datasets to train the CancerOmicsNet engine into using artificial intelligence.

"Once its trained, then you can ask for something that you dont know and this is the input data," he said. "So you ask what inhibitor you think is going to be effective against this cancer and then AI makes a prediction. Thats the implication to unseen data and then something like that goes to a wet lab and we can validate it.

Wet lab research was conducted by researchers at the LSU School of Veterinary Medicine and led by associate professor of research Brent Stanfield.

They developed the AI algorithm and everything, so our role in the study is just to be the practical applications of the technology," Stanfield said. "They developed the algorithm, identified the drugs and then we tested the drugs in our high-capacity systems to demonstrate their efficacy to kill cancer cells.

Researchers studied notoriously aggressive breast, prostate and pancreatic cell lines to train the AI to recognize connections between specific cancers and cancer drugs that control the production of the enzyme kinase within the body.

Kinase acts as a biological catalyst for cell communication and cell growth. Using drugs that lower kinase activity can suppress the growth of cancerous cells.

Brylinski said the research team used CancerOmicsNet to pick out six combinations of cancer cell lines with the drugs likely to be the most toxic to their gene expression profile and tested them, with encouraging results.

According to acceptable criteria, four out of six worked and this success rate is extremely high because if you just picked up six random drugs and say those drugs are going to work on this cancer, then theyre probably not going to work on that cancer," he said. "Four out of six was very encouraging and this is where we stand right now."

Using CancerOmicsNet like molecular speed dating, the AI can help researchers quickly match cancer cell lines with the drugs likely to be the most toxic to their growth and genetic profile.

Brylinski said knowledge gained through CancerOmicsNet can help overcome the challenge of determining how effective a particular kinase-inhibiting drug could be in the future.

The ultimate goal, he said, is to expand their research to potentially apply it in clinical settings.

"If we have a patient with a certain cancer, they can do a biopsy and then they can profile this cancer with respect to gene expression, genetic mutations and everything," Brylinski said. "Then they can input that data to CancerOmicsNet and it can suggest some therapy for this particular cancer and say this drug could be effective and 'another drug could not be effective.'

The effectiveness of various cancer drugs was initially believed to be tied to molecular consistency, the idea that cancer treatment should be targeted to a specific to a location in the body.

Michelle Collins, dean of the College of Nursing and Health at Loyola University New Orleans and a scientist not involved in the LSU research, said CancerOmicsNet is an example of how our current medical understanding of cancer treatment meets advances in genetic studies and AI.

When cancer drugs first came out, they were one size fits all and werent really tailored to the individual and so you see the medications work better on some people than others," she said. "And with the advent of genetics and genomics, which are the future of medicine, were now going to be able to tailor treatments to the patient and not just in oncology.

Collins said she sees CancerOmicsNet being extremely beneficial to oncological studies and treatment in the future.

I think it has the potential to really revolutionize the field of oncology, because well be able to treat people with medication that is more timely tailored to them," she said. "All of that is good if youre a patient with cancer.

Brylinksi said that the ability to treat cancer with a more direct, focused clinical approach makes him excited to see how CancerOmicsNet develops over time.

"I dont know if were going to make some major breakthrough in oncology any time soon, but were contributing pieces where if enough people are doing this, the whole field is moving forward towards the goal of improving human health," he said. "Were very happy that we can make some contribution, which might not be a huge breakthrough down the road, but definitely something that is useful to improving human health and thats really cool actually.

View post:
Can artificial intelligence help identify best treatments for cancers? LSU researchers say yes - The Advocate

VILMORIN & CIE

*On a like-for-like basis.

Financial statements for fiscal year 2021-2022:Vilmorin & Cie once again demonstrates the resilience of its model by achieving solid commercial and financial performances

The consolidated financial statements for 2021-2022, closing on June 30, 2022, were approved by the Vilmorin&Cie Board at its meeting of October 12, 2022. The Statutory Auditors have examined this annual financial information with no particular comments or reservations to make in their conclusions.

In millions of euros

2021-2022

2020-2021

Variationwith current datavs. 2020-2021

Sales for the year

1,587.2

1,476.6

+7.5%

EBITDA

392.1

367.2

+24.9 M

Operating income

136.3

127.4

+8.9 M

Income from associated companies

17.9

26.3

-8.4 M

Financial result

-33.4

-46.9

+13.5 M

Income taxesOf which:

Current taxes

Deferred taxes

-25.4

-22.9-2.5

-13.4

-21.58.1

-12.0 M

-1.4 M-10.6 M

Consolidated net income

95.4

93.4

+2.0 M

Group share of net income

92.2

92.3

-0.1 M

Vilmorin & Cie's consolidated financial information has been established, at the close of fiscal year 2021-2022, in compliance with the IFRS reference (International Financial Reporting Standards) as applied by the European Union on June 30,2022. Consolidated sales, corresponding to revenue from ordinary activities for fiscal year 2021-2022 came to 1,587.2 million euros, an increase of 7.5% with current data, and 6.2% on a like-for-like basis compared with 2020-2021. In spite of a general context that was destabilized by the Russian-Ukrainian conflict, Vilmorin & Cie managed to exceed its objective for growth in consolidated sales as revised at the end of the third quarter of the fiscal year (i.e. growth of around 5% on a comparable basis)1.

After taking into account the cost of destruction and depreciation of inventory, the margin on the cost of goods stood at 48.6%, a decrease of 0.7 percentage points compared with 2020-2021.

Net operating charges came to 635.2 million euros, as opposed to 600.6million euros on June30, 2021.

In compliance with its strategic orientations, Vilmorin & Cie pursued its research programs, both in terms of conventional plant breeding and biotechnologies. Total research investment came to 275.1 million euros, as opposed to 257.0 million euros in 2020-2021, and represents 16.2% of seed activity sales2, which is in line with the average of the previous three fiscal years.

The consolidated operating income stood at 136.3 million euros, an increase compared to the previous fiscal year (127.4 million euros) for all the seeds activities, including Garden Products, resulting in a current operating margin rate of 8.6%, slightly lower by 0.3 percentage points compared with fiscal year 2020-2021.

The share of income from associated companies came to 17.9 million euros, a marked decrease of 8.4 million euros compared with the previous fiscal year, due in particular to a deterioration in the operating performance of AgReliant (North America. Field Seeds) and the impact of hyperinflation, which severely limited the performance of Seed Co in Zimbabwe (Africa. Field Seeds).

The financial result showed a net charge of 33.4 million euros as opposed to 46.9 million euros in 2020-2021, an improvement of 13.5 million euros, mainly due to lower currency exchange losses.

The net charge of income taxes deteriorated by 12.0 million euros, and stood at 25.4 million euros as opposed to 13.4 million euros in 2020-2021, mainly the result of a deterioration in the deferred tax position of 10.6 million euros, partly attributable to hyperinflation in Argentina (under local standards) and in Turkey.

Vilmorin & Cie's total net income came to 95.4 million euros, an increase of 2.0 million euros compared to the previous fiscal year. This is the highest net income since fiscal year 2012-2013. The group share ("attributable to the controlling Company") stood at92.2 million euros.

Net of cash and cash equivalents (321.3 million euros), total financial indebtedness came to 901.1 million euros on June 30, 2022 compared with 867.4 million euros on June 30, 2021. The share of non-current financial indebtedness stood at 1,088.3 million euros, compared with 994.8 million euros the previous year. The group's share of equity stood at1,434.6million euros and minority interests at 48.8 million euros, with a significant increase over the fiscal year, due to the high level of net income and an increase in translation reserves linked to the appreciation of the US dollar.

Thus, compared with the previous fiscal year, the balance sheet structure on June 30, 2022 was marked by a decrease in the ratio of net indebtedness to equity (a gearing of 61%, compared to 65% on June 30, 2021). The leverage ratio as of June 30, 2022 was 2.3x compared with 2.4x as of June 30, 2021, reflecting an improvement in the group's debt reduction capacity.

2022 dividend:Proposal of a dividend of 1.60 euros per share at the upcoming Annual General Meeting of Shareholders, in Auvergne

A dividend of 1.60 euros per share, stable compared to the dividend of the previous fiscal year, will be proposed this year. This corresponds to a distribution rate of 39.8%.

This dividend will be submitted to the vote of the Shareholders by the Board of Directors of Vilmorin & Cie, on the occasion of the Annual General Meeting of Shareholders of December 9, 2022, which will be held in the Auvergne-Rhne-Alpes region, where Limagrain, the parent company and reference shareholder of the Company, is located.

Dividends will be detached on December 13, 2021, with payment on December 15, 2021.

News:A new research partnership concerning pulses in Canada

As explained when sales for the fiscal year 2021-2022 were disclosed, Vilmorin & Cie, through its Field Seeds division, announced the signing of a new partnership with Saskatchewan Pulse Growers3, in July 2022, to set up a joint research and innovation program for pulses, more particularly dry peas and lentils.

This new program, co-financed equally by the two parties, will be based in Saskatoon (Province of Saskatchewan, Canada), where the Limagrain Cereals Research Canada4 joint venture is already established, and will respond directly to the needs of farmers and to major production challenges. Among the targeted goals are resistance to root diseases, increased protein content and, above all, improved yields under variable growing conditions. With the help of a dedicated team, the objective is to bring new expertise to the region in terms of breeding, molecular genetics, treatment of pathologies and field trials.

Canada is the world's largest producer of pulse crops, with more than 2.3 million hectares planted each year5.

Objectives for 2022-2023:

Business growth of between 6% and 8%6and a current operating margin rate of at least 8%

Market conditions for fiscal year 2022-2023 are likely to remain uncertain and fluctuating, due to inflationary pressures resulting, in particular, from the geopolitical context. In this environment, Vilmorin & Cie will continue to play its role as a leading seed company by continuing to strengthen its competitive positions; the Company will pursue its measured investment in research & development, in particular in upstream technologies, while remaining attentive to any opportunity for external growth, in line with its strategic challenges and foundations.

For fiscal year 2022-2023, Vilmorin & Cie has set itself the objective of achieving an increase in consolidated sales of between 6% and 8%6, excluding the positive impact of the EGalim law on sales (which will however be neutral concerning the operating income) 7.

The Company is aiming for a current operating margin rate of at least 8%, impacted by the evolution of its business mix in favor of Field Seeds. This rate will take into account investment into research at a similar level, as a percentage of sales, to that of the two previous fiscal years, and spread evenly between Vegetable Seeds and Field Seeds.

Finally, Vilmorin & Cie is aiming for a contribution from associated companies - mainly AgReliant (North America. Field Seeds), Seed Co (Africa. Field Seeds) and AGT (Australia. Field Seeds) - at least equal to that of fiscal year 2021-2022.

Vilmorin & Cie's commercial prospects in Ukraine-RussiaIn Ukraine, the outlook for the coming fiscal year remains moderate, due to the difficulty of local farmers and distributors to obtain funding and, more generally, the lack of visibility in the region due to the continuing conflict with Russia. Accordingly, the Company expects a level of activity similar to that of fiscal year 2021-2022.

In Russia, thanks to the performance of its sunflower and corn seed varieties in particular, Vilmorin & Cie intends to confirm the momentum observed during the previous fiscal year. Prospects to date are favorable, but their realization remains subject to growing logistical and geopolitical problems, to which the Company is striving to find solutions.

Anthony CARVALHO, Vilmorin & Cie's new Chief Financial Officer

As announced in the press release of its sales for fiscal year 2021-2022, Vilmorin & Cie appointed Anthony CARVALHO to its Executive Committee, on September 14, 2022, as Chief Financial Officer.

Anthony CARVALHO, 33, holds a Master's degree in Information Systems from the Institut Mines-Tlcom SudParis, and also a Master's degree in Audit & Financial Advisory from the University of Paris Dauphine and a Master's degree in Finance from ESSEC. He has significant experience in financial functions, acquired in investment funds and then as Head of the integrated Family Office of the Roullier Group. He also has experience in Audit and Advisory, acquired within Deloitte. Previously, Anthony CARVALHO was, as of September 2021, Chief Financial Officer of the Roullier Group.

Coming disclosures and events

For any further information

Tuesday November 8, 2022*Disclosure of salesfor the first quarter of 2022-2023

Friday December 9, 2022Annual General Meeting of Shareholders

Tuesday December 13, 2022Detachment of the dividends

Thursday December 15, 2022Payment of the dividends

* Disclosure after trading on the Paris stock market.Dates provided as an indication only, and liable to be changed.

Anthony CARVALHOChief Financial Officer

douard ROCHEHead of Financial Communication and Investor Relations

See more here:
Results for the fiscal year on June 30, 2022 - Yahoo Finance

A new way to view colorectal cancer cell differentiation. Secretomics shed light on cells immune response. Targeting the signaling pathway convergence point. Read about papers on these topics recently published in the journal Molecular & Cellular Proteomics.

Colorectal cancer is among the most diagnosed cancers. Its risk factors include genetics, aging and lifestyle choices, such as smoking. Treatment has proved difficult, as this type of cancer is heterogenous and often has an asymptomatic clinical course, resulting in late diagnosis. Identifying reliable biomarkers for diagnosis and disease progression is essential.

Histopathology of colorectal adenocarcinoma with lymphatic invasion.

Research groups previously have determined that altered glycosylation states, in glycosphingolipid particularly, can be associated with malignant transformation in colon cancer. While protein glycosylation in this cancer is studied widely, glycosphingolipid expression of patterns and their contributions to colorectal cancer have yet to be explored.

In a recent study published in Molecular & Cellular Proteomics, Di Wang and collaborators at the Center for Proteomics and Metabolomics at Leiden University Medical Center in the Netherlands performed an in-depth analysis of glycosphingolipid glycans of 22 colorectal cancer cell lines using porous graphitized carbon nanoliquid chromatography coupled with electrospray ionizationmass spectrometry. The authors found that glycosphingolipid expression varies among different cell lines.

They also found that glycosphingolipid expression correlates with relevant glycosyltransferases involved in their biosynthesis as well as with transcription factors implicated in colon differentiation. The authors conclude that this glycomic study provides novel insights into glycosphingolipid glycan regulation for future functional studies in colorectal cancer research.

Hepatocytes cells in the liver that play essential roles in metabolism and detoxification have important secretory and immunological functions. Inflammatory processes produce signals (such as cytokines interleukin-1 and -6) that induce the acute-phase response, which in turn provokes secretion of proteins with immunomodulatory functions to restore homeostasis.

The secretome of a cell or an organism consists of proteins secreted by the endoplasmic reticulumGolgi secretory pathway and other direct or vesicle-based mechanisms. The systematic investigation of secreted proteins by mass spectrometrybased proteomics, however, has faced several challenges, as most approaches use serum-free culture conditions to avoid serum-induced interferences, thus negatively affecting the observable time window, cellular functions and viability.

In a recent article in Molecular & Cellular Proteomics, Sascha Knetch and colleagues at GlaxoSmithKline in Germany developed an interval-based secretomics workflow that determines protein-secretion rates in short serum-free time windows. The authors also implemented a labeling strategy in which they were able to pull up to 11 protein samples into a single mass spectrometry run. This approach allowed for the first comprehensive analysis of time-dependent secretion of liver cell models in response to these pro-inflammatory cytokines.

PI3KmTOR and MEK/MAPK signaling pathways are essential for the regulation of cell survival and other cellular functions. In addition, they are the most frequently dysregulated pathways in cancer.

Some cancer therapies based on kinase inhibitors are effective in tumors that are addicted to prosurvival signals and target individual members within these pathways. This leads to drug resistance and transient responses. To overcome this limitation, researchers are now investigating cotreatments with PI3K/AKT and MEK/MAPK inhibitors; however, the mechanisms leading to sensitivity remain unclear.

In a recent Molecular & Cellular Proteomics article, Maruan Hijazi and collaborators at the Centre for Genomics and Computational Biology at Queen Mary University of London report developing a phosphoproteomics approach based on liquid chromatography tandem mass spectrometry to study the effects of co-treatment on the kinase eEF2K, a convergence point for both pathways.

The authors found that inhibition of eEF2K by siRNA or with a small-molecule inhibitor reversed the anti-proliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell modelspecific manner. The authors conclude that eEF2K is a key mediator of these pathways and a target for synergistic cotreatment in cancer cells.

See the article here:
From the journals: MCP - ASBMB Today