Int J Mol Sci

Blood platelets have important roles in haemostasis, where they quickly stop bleeding in response to vascular damage. They have also recognised functions in thrombosis, immunity, antimicrobal defense, cancer growth and metastasis, tumour angiogenesis, lymphangiogenesis, inflammatory diseases, wound healing, liver regeneration and neurodegeneration. Their brief life span in circulation is strictly controlled by intrinsic apoptosis, where the prosurvival Bcl-2 family protein, Bcl-xL, has a major role. Blood platelets are produced by large polyploid precursor cells, megakaryocytes, residing mainly in the bone marrow. Together with Mcl-1, Bcl-xL regulates megakaryocyte survival. This review describes megakaryocyte maturation and survival, platelet production, platelet life span and diseases of abnormal platelet number with a focus on the role of Bcl-xL during these processes

The RNA-binding protein SRSF3 has an essential role in megakaryocyte maturation and platelet production

RNA processing is increasingly recognized as a critical control point in the regulation of different hematopoietic lineages including megakaryocytes responsible for the production of platelets. Platelets are anucleate cytoplasts that contain a rich repertoire of RNAs encoding proteins with essential platelet functions derived from the parent megakaryocyte. It is largely unknown how RNA binding proteins contribute to the development and functions of megakaryocytes and platelets. We show that serine-arginine–rich splicing factor 3 (SRSF3) is essential for megakaryocyte maturation and generation of functional platelets. Megakaryocyte-specific deletion of Srsf3 in mice led to macrothrombocytopenia characterized by megakaryocyte maturation arrest, dramatically reduced platelet counts, and abnormally large functionally compromised platelets. SRSF3 deficient megakaryocytes failed to reprogram their transcriptome during maturation and to load platelets with RNAs required for normal platelet function. SRSF3 depletion led to nuclear accumulation of megakaryocyte mRNAs, demonstrating that SRSF3 deploys similar RNA regulatory mechanisms in megakaryocytes as in other cell types. Our study further suggests that SRSF3 plays a role in sorting cytoplasmic megakaryocyte RNAs into platelets and demonstrates how SRSF3-mediated RNA processing forms a central part of megakaryocyte gene regulation. Understanding SRSF3 functions in megakaryocytes and platelets provides key insights into normal thrombopoiesis and platelet pathologies as SRSF3 RNA targets in megakaryocytes are associated with platelet diseases.publishedVersionPeer reviewe

Megakaryocytes possess a functional intrinsic apoptosis pathway that must be restrained to survive and produce platelets

Deletion of Bak and Bax, the effectors of mitochondrial apoptosis, does not affect platelet production, however, loss of prosurvival Bcl-xL results in megakaryocyte apoptosis and failure of platelet shedding

Uncovering the complex genetics of human temperament

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic-phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37-53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.Peer reviewe

Regulation of platelet membrane protein shedding in health and disease

Extracellular proteolysis of platelet plasma membrane proteins is an event that ensues platelet activation. Shedding of surface receptors such as glycoprotein (GP) Ibα, GPV and GPVI as well as externalized proteins P-selectin and CD40L releases soluble ectodomain fragments that are subsequently detectable in plasma. This results in the irreversible functional downregulation of platelet receptor-mediated adhesive interactions and the generation of biologically active fragments. In this review, we describe molecular insights into the regulation of platelet receptor and ligand shedding in health and disease. The scope of this review is specially focused on GPIbα, GPV, GPVI, P-selectin and CD40L where we: (1) describe the basic physiological regulation of expression and shedding of these proteins in hemostasis illustrate alterations in receptor expression during (2) apoptosis and (3) ex vivo storage relevant for blood banking purposes; (4) discuss considerations to be made when analyzing and interpreting shedding of platelet membrane proteins and finally; (5) collate clinical evidence that quantify these platelet proteins during disease

Platelet counts (±standard error of the mean, s.e.m.) and best-fit LS model parameters from fits to population survival data for each genotype, with 95% C.I.'s from the Monte Carlo technique in brackets.

<p>Platelet counts (±standard error of the mean, s.e.m.) and best-fit LS model parameters from fits to population survival data for each genotype, with 95% C.I.'s from the Monte Carlo technique in brackets.</p

Lognormal-Senescent model fits of platelet survival data.

<p>(<b>A</b>) Population survival data and LS model best fits for <i>Bcl-x^+/Plt20</i> (blue), wild-type (green) and <i>Bak^−/−</i> (red) mice. A Monte Carlo technique was used to generate estimates of confidence intervals for the model parameters – (<b>B</b>) mean natural life span, <i>μ</i>, (<b>C</b>) standard deviation of natural life span, <i>σ</i>, (<b>D</b>) random loss rate constant, <i>r</i> (always 0 hr⁻¹ for this model), and (<b>E</b>) random loss fraction, <i>f</i> (always 0 for this model). 1000 Monte Carlo simulations were performed and fit to obtain parameters – box-and-whisker plots indicate median, interquartile range, 2.5 and 97.5 percentiles, and outliers are plotted as individual dots.</p

Platelet counts (±s.e.m.) and best-fit Dornhorst model parameters from fits to population survival data for each genotype, with 95% C.I.'s from the Monte Carlo technique in brackets.

<p>Platelet counts (±s.e.m.) and best-fit Dornhorst model parameters from fits to population survival data for each genotype, with 95% C.I.'s from the Monte Carlo technique in brackets.</p

The effect of experimental uncertainty on the ability to constrain the value of the random loss fraction.

<p>Fits to simulated data with Gaussian random noise of varying standard error of the mean (s.e.m.) added to simulate experimental uncertainty. (A)–(E) Simulated model parameters: <i>μ</i> = 100 hr, <i>σ</i> = 25 hr, with (A) <i>f</i> = 0.2, (B) <i>f</i> = 0.8. For each value of <i>f</i> and each value of the s.e.m. of the noise, the fit was repeated 1000 times with independently-generated noise – the box-and-whisker plots indicate median, interquartile range, 2.5 and 97.5 percentiles, and outliers are plotted as individual dots. (C) Interquartile range as a function of random loss fraction for the various levels of noise added to the simulated data. For a given value of the s.e.m. of the noise, higher values of the random loss fraction are generally better constrained than lower (non-zero) values.</p