Interactome

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Interactome

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules (such as those among proteins, also known as protein–protein interactions, PPIs; or between small molecules and proteins) but can also describe sets of indirect interactions among genes (genetic interactions).

Part of the DISC1 interactome with genes represented by text in boxes and interactions noted by lines between the genes. From Hennah and Porteous, 2009.

The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq. Mathematically, interactomes are generally displayed as graphs. though interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs.

Molecular interaction networks

Molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates, etc.) and also within a given family. Whenever such molecules are connected by physical interactions, they form molecular interaction networks that are generally classified by the nature of the compounds involved. Most commonly, interactome refers to protein–protein interaction (PPI) network (PIN) or subsets thereof. For instance, the Sirt-1 protein interactome and Sirt family second order interactome is the network involving Sirt-1 and its directly interacting proteins where as second order interactome illustrates interactions up to second order of neighbors (Neighbors of neighbors). Another extensively studied type of interactome is the protein–DNA interactome, also called a gene-regulatory network, a network formed by transcription factors, chromatin regulatory proteins, and their target genes. Even metabolic networks can be considered as molecular interaction networks: metabolites, i.e. chemical compounds in a cell, are converted into each other by enzymes, which have to bind their substrates physically.

In fact, all interactome types are interconnected. For instance, protein interactomes contain many enzymes which in turn form biochemical networks. Similarly, gene regulatory networks overlap substantially with protein interaction networks and signaling networks.

Size

Estimates of the yeast protein interactome. From Uetz P. & Grigoriev A, 2005.

It has been suggested that the size of an organism's interactome correlates better than genome size with the biological complexity of the organism. Although protein–protein interaction maps containing several thousand binary interactions are now available for several species, none of them is presently complete and the size of interactomes is still a matter of debate.

Yeast

The yeast interactome, i.e. all protein–protein interactions among proteins of Saccharomyces cerevisiae, has been estimated to contain between 10,000 and 30,000 interactions. A reasonable estimate may be on the order of 20,000 interactions. Larger estimates often include indirect or predicted interactions, often from affinity purification/mass spectrometry (AP/MS) studies.

Genetic interaction networks

Main article: Genetic interaction network

Genes interact in the sense that they affect each other's function. For instance, a mutation may be harmless, but when it is combined with another mutation, the combination may turn out to be lethal. Such genes are said to "interact genetically". Genes that are connected in such a way form genetic interaction networks. Some of the goals of these networks are: develop a functional map of a cell's processes, drug target identification using chemoproteomics, and to predict the function of uncharacterized genes.

In 2010, the most "complete" gene interactome produced to date was compiled from about 5.4 million two-gene comparisons to describe "the interaction profiles for ~75% of all genes in the budding yeast", with ~170,000 gene interactions. The genes were grouped based on similar function so as to build a functional map of the cell's processes. Using this method the study was able to predict known gene functions better than any other genome-scale data set as well as adding functional information for genes that hadn't been previously described. From this model genetic interactions can be observed at multiple scales which will assist in the study of concepts such as gene conservation. Some of the observations made from this study are that there were twice as many negative as positive interactions, negative interactions were more informative than positive interactions, and genes with more connections were more likely to result in lethality when disrupted.

Interactomics

Interactomics is a discipline at the intersection of bioinformatics and biology that deals with studying both the interactions and the consequences of those interactions between and among proteins, and other molecules within a cell. Interactomics thus aims to compare such networks of interactions (i.e., interactomes) between and within species in order to find how the traits of such networks are either preserved or varied.

Interactomics is an example of "top-down" systems biology, which takes an overhead view of a biosystem or organism. Large sets of genome-wide and proteomic data are collected, and correlations between different molecules are inferred. From the data new hypotheses are formulated about feedbacks between these molecules. These hypotheses can then be tested by new experiments.

Experimental methods to map interactomes

The study of interactomes is called interactomics. The basic unit of a protein network is the protein–protein interaction (PPI). While there are numerous methods to study PPIs, there are relatively few that have been used on a large scale to map whole interactomes.

The yeast two hybrid system (Y2H) is suited to explore the binary interactions among two proteins at a time. Affinity purification and subsequent mass spectrometry is suited to identify a protein complex. Both methods can be used in a high-throughput (HTP) fashion. Yeast two hybrid screens allow false positive interactions between proteins that are never expressed in the same time and place; affinity capture mass spectrometry does not have this drawback, and is the current gold standard. Yeast two-hybrid data better indicates non-specific tendencies towards sticky interactions rather while affinity capture mass spectrometry better indicates functional in vivo protein–protein interactions.

Computational methods to study interactomes

Once an interactome has been created, there are numerous ways to analyze its properties. However, there are two important goals of such analyses. First, scientists try to elucidate the systems properties of interactomes, e.g. the topology of its interactions. Second, studies may focus on individual proteins and their role in the network. Such analyses are mainly carried out using bioinformatics methods and include the following, among many others:

Validation

First, the coverage and quality of an interactome has to be evaluated. Interactomes are never complete, given the limitations of experimental methods. For instance, it has been estimated that typical Y2H screens detect only 25% or so of all interactions in an interactome. The coverage of an interactome can be assessed by comparing it to benchmarks of well-known interactions that have been found and validated by independent assays. Other methods filter out false positives calculating the similarity of known annotations of the proteins involved or define a likelihood of interaction using the subcellular localization of these proteins.

Predicting PPIs

Schizophrenia PPI.

Using experimental data as a starting point, homology transfer is one way to predict interactomes. Here, PPIs from one organism are used to predict interactions among homologous proteins in another organism ("interologs"). However, this approach has certain limitations, primarily because the source data may not be reliable (e.g. contain false positives and false negatives). In addition, proteins and their interactions change during evolution and thus may have been lost or gained. Nevertheless, numerous interactomes have been predicted, e.g. that of Bacillus licheniformis.

Some algorithms use experimental evidence on structural complexes, the atomic details of binding interfaces and produce detailed atomic models of protein–protein complexes as well as other protein–molecule interactions. Other algorithms use only sequence information, thereby creating unbiased complete networks of interaction with many mistakes.

Some methods use machine learning to distinguish how interacting protein pairs differ from non-interacting protein pairs in terms of pairwise features such as cellular colocalization, gene co-expression, how closely located on a DNA are the genes that encode the two proteins, and so on. Random Forest has been found to be most-effective machine learning method for protein interaction prediction. Such methods have been applied for discovering protein interactions on human interactome, specifically the interactome of Membrane proteins and the interactome of Schizophrenia-associated proteins.

Text mining of PPIs

Some efforts have been made to extract systematically interaction networks directly from the scientific literature. Such approaches range in terms of complexity from simple co-occurrence statistics of entities that are mentioned together in the same context (e.g. sentence) to sophisticated natural language processing and machine learning methods for detecting interaction relationships.

Protein function prediction

Protein interaction networks have been used to predict the function of proteins of unknown functions. This is usually based on the assumption that uncharacterized proteins have similar functions as their interacting proteins (guilt by association). For example, YbeB, a protein of unknown function was found to interact with ribosomal proteins and later shown to be involved in bacterial and eukaryotic (but not archaeal) translation. Although such predictions may be based on single interactions, usually several interactions are found. Thus, the whole network of interactions can be used to predict protein functions, given that certain functions are usually enriched among the interactors. The term hypothome has been used to denote an interactome wherein at least one of the genes or proteins is a hypothetical protein.

Perturbations and disease

Main article: Network medicine

The topology of an interactome makes certain predictions how a network reacts to the perturbation (e.g. removal) of nodes (proteins) or edges (interactions). Such perturbations can be caused by mutations of genes, and thus their proteins, and a network reaction can manifest as a disease. A network analysis can identify drug targets and biomarkers of diseases.

Network structure and topology

Interaction networks can be analyzed using the tools of graph theory. Network properties include the degree distribution, clustering coefficients, betweenness centrality, and many others. The distribution of properties among the proteins of an interactome has revealed that the interactome networks often have scale-free topology where functional modules within a network indicate specialized subnetworks. Such modules can be functional, as in a signaling pathway, or structural, as in a protein complex. In fact, it is a formidable task to identify protein complexes in an interactome, given that a network on its own does not directly reveal the presence of a stable complex.

Studied interactomes

Viral interactomes

Viral protein interactomes consist of interactions among viral or phage proteins. They were among the first interactome projects as their genomes are small and all proteins can be analyzed with limited resources. Viral interactomes are connected to their host interactomes, forming virus-host interaction networks. Some published virus interactomes include

Bacteriophage

• Escherichia coli bacteriophage lambda
• Escherichia coli bacteriophage T7
• Streptococcus pneumoniae bacteriophage Dp-1
• Streptococcus pneumoniae bacteriophage Cp-1

The lambda and VZV interactomes are not only relevant for the biology of these viruses but also for technical reasons: they were the first interactomes that were mapped with multiple Y2H vectors, proving an improved strategy to investigate interactomes more completely than previous attempts have shown.

Human (mammalian) viruses

• Human varicella zoster virus (VZV)
• Chandipura virus
• Epstein-Barr virus (EBV)
• Hepatitis C virus (HPC), Human-HCV interactions
• Hepatitis E virus (HEV)
• Herpes simplex virus 1 (HSV-1)
• Kaposi's sarcoma-associated herpesvirus (KSHV)
• Murine cytomegalovirus (mCMV)

Bacterial interactomes

Relatively few bacteria have been comprehensively studied for their protein–protein interactions. However, none of these interactomes are complete in the sense that they captured all interactions. In fact, it has been estimated that none of them covers more than 20% or 30% of all interactions, primarily because most of these studies have only employed a single method, all of which discover only a subset of interactions. Among the published bacterial interactomes (including partial ones) are

Species	proteins total	interactions	type
Helicobacter pylori	1,553	~3,004	Y2H
Campylobacter jejuni	1,623	11,687	Y2H
Treponema pallidum	1,040	3,649	Y2H
Escherichia coli	4,288	(5,993)	AP/MS
Escherichia coli	4,288	2,234	Y2H
Mesorhizobium loti	6,752	3,121	Y2H
Mycobacterium tuberculosis	3,959	>8000	B2H
Mycoplasma genitalium	482		AP/MS
Synechocystis sp. PCC6803	3,264	3,236	Y2H
Staphylococcus aureus (MRSA)	2,656	13,219	AP/MS

The E. coli and Mycoplasma interactomes have been analyzed using large-scale protein complex affinity purification and mass spectrometry (AP/MS), hence it is not easily possible to infer direct interactions. The others have used extensive yeast two-hybrid (Y2H) screens. The Mycobacterium tuberculosis interactome has been analyzed using a bacterial two-hybrid screen (B2H).

Note that numerous additional interactomes have been predicted using computational methods (see section above).

Eukaryotic interactomes

There have been several efforts to map eukaryotic interactomes through HTP methods. While no biological interactomes have been fully characterized, over 90% of proteins in Saccharomyces cerevisiae have been screened and their interactions characterized, making it the best-characterized interactome. Species whose interactomes have been studied in some detail include

• Schizosaccharomyces pombe
• Caenorhabditis elegans
• Drosophila melanogaster
• Homo sapiens

Recently, the pathogen-host interactomes of Hepatitis C Virus/Human (2008), Epstein Barr virus/Human (2008), Influenza virus/Human (2009) were delineated through HTP to identify essential molecular components for pathogens and for their host's immune system.

Predicted interactomes

As described above, PPIs and thus whole interactomes can be predicted. While the reliability of these predictions is debatable, they are providing hypotheses that can be tested experimentally. Interactomes have been predicted for a number of species, e.g.

• Human (Homo sapiens)
• Rice (Oryza sativa)
• Xanthomonas oryzae
• Arabidopsis thaliana
• Tomato (Solanum lycopersicum)
• Field mustard (Brassica rapa)
• Maize, corn (Zea mays)
• Poplar (Populus trichocarpa)
• SARS-CoV-2

Representation of the predicted SARS-CoV-2/Human interactome

Network properties

Protein interaction networks can be analyzed with the same tool as other networks. In fact, they share many properties with biological or social networks. Some of the main characteristics are as follows.

The Treponema pallidum protein interactome.

Degree distribution

The degree distribution describes the number of proteins that have a certain number of connections. Most protein interaction networks show a scale-free (power law) degree distribution where the connectivity distribution P(k) ~ k^−γ with k being the degree. This relationship can also be seen as a straight line on a log-log plot since, the above equation is equal to log(P(k)) ~ —y•log(k). One characteristic of such distributions is that there are many proteins with few interactions and few proteins that have many interactions, the latter being called "hubs".

Hubs

Highly connected nodes (proteins) are called hubs. Han et al.^[73] have coined the term "party hub" for hubs whose expression is correlated with its interaction partners. Party hubs also connect proteins within functional modules such as protein complexes. In contrast, "date hubs" do not exhibit such a correlation and appear to connect different functional modules. Party hubs are found predominantly in AP/MS data sets, whereas date hubs are found predominantly in binary interactome network maps. Note that the validity of the date hub/party hub distinction was disputed. Party hubs generally consist of multi-interface proteins whereas date hubs are more frequently single-interaction interface proteins. Consistent with a role for date-hubs in connecting different processes, in yeast the number of binary interactions of a given protein is correlated to the number of phenotypes observed for the corresponding mutant gene in different physiological conditions.

Modules

Nodes involved in the same biochemical process are highly interconnected.

Evolution

The evolution of interactome complexity is delineated in a study published in Nature. In this study it is first noted that the boundaries between prokaryotes, unicellular eukaryotes and multicellular eukaryotes are accompanied by orders-of-magnitude reductions in effective population size, with concurrent amplifications of the effects of random genetic drift. The resultant decline in the efficiency of selection seems to be sufficient to influence a wide range of attributes at the genomic level in a nonadaptive manner. The Nature study shows that the variation in the power of random genetic drift is also capable of influencing phylogenetic diversity at the subcellular and cellular levels. Thus, population size would have to be considered as a potential determinant of the mechanistic pathways underlying long-term phenotypic evolution. In the study it is further shown that a phylogenetically broad inverse relation exists between the power of drift and the structural integrity of protein subunits. Thus, the accumulation of mildly deleterious mutations in populations of small size induces secondary selection for protein–protein interactions that stabilize key gene functions, mitigating the structural degradation promoted by inefficient selection. By this means, the complex protein architectures and interactions essential to the genesis of phenotypic diversity may initially emerge by non-adaptive mechanisms.

Criticisms, challenges, and responses

Kiemer and Cesareni raise the following concerns with the state (circa 2007) of the field especially with the comparative interactomic: The experimental procedures associated with the field are error prone leading to "noisy results". This leads to 30% of all reported interactions being artifacts. In fact, two groups using the same techniques on the same organism found less than 30% interactions in common. However, some authors have argued that such non-reproducibility results from the extraordinary sensitivity of various methods to small experimental variation. For instance, identical conditions in Y2H assays result in very different interactions when different Y2H vectors are used.

Techniques may be biased, i.e. the technique determines which interactions are found. In fact, any method has built in biases, especially protein methods. Because every protein is different no method can capture the properties of each protein. For instance, most analytical methods that work fine with soluble proteins deal poorly with membrane proteins. This is also true for Y2H and AP/MS technologies.

Interactomes are not nearly complete with perhaps the exception of S. cerevisiae. This is not really a criticism as any scientific area is "incomplete" initially until the methodologies have been improved. Interactomics in 2015 is where genome sequencing was in the late 1990s, given that only a few interactome datasets are available (see table above).

While genomes are stable, interactomes may vary between tissues, cell types, and developmental stages. Again, this is not a criticism, but rather a description of the challenges in the field.

It is difficult to match evolutionarily related proteins in distantly related species. While homologous DNA sequences can be found relatively easily, it is much more difficult to predict homologous interactions ("interologs") because the homologs of two interacting proteins do not need to interact. For instance, even within a proteome two proteins may interact but their paralogs may not.

Each protein–protein interactome may represent only a partial sample of potential interactions, even when a supposedly definitive version is published in a scientific journal. Additional factors may have roles in protein interactions that have yet to be incorporated in interactomes. The binding strength of the various protein interactors, microenvironmental factors, sensitivity to various procedures, and the physiological state of the cell all impact protein–protein interactions, yet are usually not accounted for in interactome studies.

Lewis acids and bases

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Lewis_acids_and_bases

Diagram of some Lewis bases (left) and acids (right)

A Lewis acid (named for the American physical chemist Gilbert N. Lewis) is a chemical species that contains an empty orbital which is capable of accepting an electron pair from a Lewis base to form a Lewis adduct. A Lewis base, then, is any species that has a filled orbital containing an electron pair which is not involved in bonding but may form a dative bond with a Lewis acid to form a Lewis adduct. For example, NH₃ is a Lewis base, because it can donate its lone pair of electrons. Trimethylborane [(CH₃)₃B] is a Lewis acid as it is capable of accepting a lone pair. In a Lewis adduct, the Lewis acid and base share an electron pair furnished by the Lewis base, forming a dative bond. In the context of a specific chemical reaction between NH₃ and Me₃B, a lone pair from NH₃ will form a dative bond with the empty orbital of Me₃B to form an adduct NH₃•BMe₃. The terminology refers to the contributions of Gilbert N. Lewis.

The terms nucleophile and electrophile are sometimes interchangeable with Lewis base and Lewis acid, respectively. These terms, especially their abstract noun forms nucleophilicity and electrophilicity, emphasize the kinetic aspect of reactivity, while the Lewis basicity and Lewis acidity emphasize the thermodynamic aspect of Lewis adduct formation.

Depicting adducts

In many cases, the interaction between the Lewis base and Lewis acid in a complex is indicated by an arrow indicating the Lewis base donating electrons toward the Lewis acid using the notation of a dative bond — for example, Me₃B←NH₃. Some sources indicate the Lewis base with a pair of dots (the explicit electrons being donated), which allows consistent representation of the transition from the base itself to the complex with the acid:

Me₃B + :NH₃ → Me₃B:NH₃

A center dot may also be used to represent a Lewis adduct, such as Me₃B·NH₃. Another example is boron trifluoride diethyl etherate, BF₃·Et₂O. In a slightly different usage, the center dot is also used to represent hydrate coordination in various crystals, as in MgSO₄·7H₂O for hydrated magnesium sulfate, irrespective of whether the water forms a dative bond with the metal.

Although there have been attempts to use computational and experimental energetic criteria to distinguish dative bonding from non-dative covalent bonds, for the most part, the distinction merely makes note of the source of the electron pair, and dative bonds, once formed, behave simply as other covalent bonds do, though they typically have considerable polar character. Moreover, in some cases (e.g., sulfoxides and amine oxides as R₂S → O and R₃N → O), the use of the dative bond arrow is just a notational convenience for avoiding the drawing of formal charges. In general, however, the donor–acceptor bond is viewed as simply somewhere along a continuum between idealized covalent bonding and ionic bonding.

Lewis acids

Major structural changes accompany binding of the Lewis base to the coordinatively unsaturated, planar Lewis acid BF₃

Lewis acids are diverse and the term is used loosely. Simplest are those that react directly with the Lewis base, such as boron trihalides and the pentahalides of phosphorus, arsenic, and antimony.

In the same vein, CH+3 can be considered to be the Lewis acid in methylation reactions. However, the methyl cation never occurs as a free species in the condensed phase, and methylation reactions by reagents like CH₃I take place through the simultaneous formation of a bond from the nucleophile to the carbon and cleavage of the bond between carbon and iodine (S_N2 reaction). Textbooks disagree on this point: some asserting that alkyl halides are electrophiles but not Lewis acids, while others describe alkyl halides (e.g. CH₃Br) as a type of Lewis acid. The IUPAC states that Lewis acids and Lewis bases react to form Lewis adducts, and defines electrophile as Lewis acids.

Simple Lewis acids

Some of the most studied examples of such Lewis acids are the boron trihalides and organoboranes:

BF₃ + F⁻ → BF−4

In this adduct, all four fluoride centres (or more accurately, ligands) are equivalent.

BF₃ + OMe₂ → BF₃OMe₂

Both BF₄⁻ and BF₃OMe₂ are Lewis base adducts of boron trifluoride.

Many adducts violate the octet rule, such as the triiodide anion:

I₂ + I⁻ → I−3

The variability of the colors of iodine solutions reflects the variable abilities of the solvent to form adducts with the Lewis acid I₂.

Some Lewis acids bind with two Lewis bases, a famous example being the formation of hexafluorosilicate:

SiF₄ + 2 F⁻ → SiF2−6

Complex Lewis acids

Most compounds considered to be Lewis acids require an activation step prior to formation of the adduct with the Lewis base. Complex compounds such as Et₃Al₂Cl₃ and AlCl₃ are treated as trigonal planar Lewis acids but exist as aggregates and polymers that must be degraded by the Lewis base. A simpler case is the formation of adducts of borane. Monomeric BH₃ does not exist appreciably, so the adducts of borane are generated by degradation of diborane:

B₂H₆ + 2 H⁻ → 2 BH−4

In this case, an intermediate B₂H−7 can be isolated.

Many metal complexes serve as Lewis acids, but usually only after dissociating a more weakly bound Lewis base, often water.

[Mg(H₂O)₆]²⁺ + 6 NH₃ → [Mg(NH₃)₆]²⁺ + 6 H₂O

H⁺ as Lewis acid

The proton (H⁺)  is one of the strongest but is also one of the most complicated Lewis acids. It is convention to ignore the fact that a proton is heavily solvated (bound to solvent). With this simplification in mind, acid-base reactions can be viewed as the formation of adducts:

• H⁺ + NH₃ → NH+4
• H⁺ + OH⁻ → H₂O

Applications of Lewis acids

A typical example of a Lewis acid in action is in the Friedel–Crafts alkylation reaction. The key step is the acceptance by AlCl₃ of a chloride ion lone-pair, forming AlCl−4 and creating the strongly acidic, that is, electrophilic, carbonium ion.

RCl +AlCl₃ → R⁺ + AlCl−4

Lewis bases

A Lewis base is an atomic or molecular species where the highest occupied molecular orbital (HOMO) is highly localized. Typical Lewis bases are conventional amines such as ammonia and alkyl amines. Other common Lewis bases include pyridine and its derivatives. Some of the main classes of Lewis bases are

• amines of the formula NH_3−xR_x where R = alkyl or aryl. Related to these are pyridine and its derivatives.
• phosphines of the formula PR_3−xAr_x.
• compounds of O, S, Se and Te in oxidation state −2, including water, ethers, ketones

The most common Lewis bases are anions. The strength of Lewis basicity correlates with the pK_a of the parent acid: acids with high pK_a's give good Lewis bases. As usual, a weaker acid has a stronger conjugate base.

• Examples of Lewis bases based on the general definition of electron pair donor include:
  • simple anions, such as H⁻ and F⁻
  • other lone-pair-containing species, such as H₂O, NH₃, HO⁻, and CH₃⁻
  • complex anions, such as sulfate
  • electron-rich π-system Lewis bases, such as ethyne, ethene, and benzene

The strength of Lewis bases have been evaluated for various Lewis acids, such as I₂, SbCl₅, and BF₃.

Lewis base	Donor atom	Enthalpy of complexation (kJ/mol)
Quinuclidine	N	150
Et3N	N	135
Pyridine	N	128
Acetonitrile	N	60
DMA	O	112
DMSO	O	105
THF	O	90.4
Et2O	O	78.8
Acetone	O	76.0
EtOAc	O	75.5
Trimethylphosphine	P	97.3
Tetrahydrothiophene	S	51.6

Applications of Lewis bases

Main article: Homogeneous catalysis

Nearly all electron pair donors that form compounds by binding transition elements can be viewed ligands. Thus, a large application of Lewis bases is to modify the activity and selectivity of metal catalysts. Chiral Lewis bases, generally multidentate, confer chirality on a catalyst, enabling asymmetric catalysis, which is useful for the production of pharmaceuticals. The industrial synthesis of the anti-hypertension drug mibefradil uses a chiral Lewis base (R-MeOBIPHEP), for example.

Hard and soft classification

Main article: HSAB theory

Lewis acids and bases are commonly classified according to their hardness or softness. In this context hard implies small and nonpolarizable and soft indicates larger atoms that are more polarizable.

• typical hard acids: H⁺, alkali/alkaline earth metal cations, boranes, Zn²⁺
• typical soft acids: Ag⁺, Mo(0), Ni(0), Pt²⁺
• typical hard bases: ammonia and amines, water, carboxylates, fluoride and chloride
• typical soft bases: organophosphines, thioethers, carbon monoxide, iodide

For example, an amine will displace phosphine from the adduct with the acid BF₃. In the same way, bases could be classified. For example, bases donating a lone pair from an oxygen atom are harder than bases donating through a nitrogen atom. Although the classification was never quantified it proved to be very useful in predicting the strength of adduct formation, using the key concepts that hard acid—hard base and soft acid—soft base interactions are stronger than hard acid—soft base or soft acid—hard base interactions. Later investigation of the thermodynamics of the interaction suggested that hard—hard interactions are enthalpy favored, whereas soft—soft are entropy favored.

Quantifying Lewis acidity

Many methods have been devised to evaluate and predict Lewis acidity. Many are based on spectroscopic signatures such as shifts NMR signals or IR bands e.g. the Gutmann-Beckett method and the Childs method.

The ECW model is a quantitative model that describes and predicts the strength of Lewis acid base interactions, −ΔH. The model assigned E and C parameters to many Lewis acids and bases. Each acid is characterized by an E_A and a C_A. Each base is likewise characterized by its own E_B and C_B. The E and C parameters refer, respectively, to the electrostatic and covalent contributions to the strength of the bonds that the acid and base will form. The equation is

−ΔH = E_AE_B + C_AC_B + W

The W term represents a constant energy contribution for acid–base reaction such as the cleavage of a dimeric acid or base. The equation predicts reversal of acids and base strengths. The graphical presentations of the equation show that there is no single order of Lewis base strengths or Lewis acid strengths and that single property scales are limited to a smaller range of acids or bases.

History

MO diagram depicting the formation of a dative covalent bond between two atoms

The concept originated with Gilbert N. Lewis who studied chemical bonding. In 1923, Lewis wrote An acid substance is one which can employ an electron lone pair from another molecule in completing the stable group of one of its own atoms. The Brønsted–Lowry acid–base theory was published in the same year. The two theories are distinct but complementary. A Lewis base is also a Brønsted–Lowry base, but a Lewis acid does not need to be a Brønsted–Lowry acid. The classification into hard and soft acids and bases (HSAB theory) followed in 1963. The strength of Lewis acid-base interactions, as measured by the standard enthalpy of formation of an adduct can be predicted by the Drago–Wayland two-parameter equation.

Reformulation of Lewis theory

Lewis had suggested in 1916 that two atoms are held together in a chemical bond by sharing a pair of electrons. When each atom contributed one electron to the bond, it was called a covalent bond. When both electrons come from one of the atoms, it was called a dative covalent bond or coordinate bond. The distinction is not very clear-cut. For example, in the formation of an ammonium ion from ammonia and hydrogen the ammonia molecule donates a pair of electrons to the proton; the identity of the electrons is lost in the ammonium ion that is formed. Nevertheless, Lewis suggested that an electron-pair donor be classified as a base and an electron-pair acceptor be classified as acid.

A more modern definition of a Lewis acid is an atomic or molecular species with a localized empty atomic or molecular orbital of low energy. This lowest-energy molecular orbital (LUMO) can accommodate a pair of electrons.

Comparison with Brønsted–Lowry theory

A Lewis base is often a Brønsted–Lowry base as it can donate a pair of electrons to H⁺; the proton is a Lewis acid as it can accept a pair of electrons. The conjugate base of a Brønsted–Lowry acid is also a Lewis base as loss of H⁺ from the acid leaves those electrons which were used for the A—H bond as a lone pair on the conjugate base. However, a Lewis base can be very difficult to protonate, yet still react with a Lewis acid. For example, carbon monoxide is a very weak Brønsted–Lowry base but it forms a strong adduct with BF₃.

In another comparison of Lewis and Brønsted–Lowry acidity by Brown and Kanner, 2,6-di-t-butylpyridine reacts to form the hydrochloride salt with HCl but does not react with BF₃. This example demonstrates that steric factors, in addition to electron configuration factors, play a role in determining the strength of the interaction between the bulky di-t-butylpyridine and tiny proton.

Anarcho-primitivism

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Anarcho-primitivism

Anarcho-primitivism, also known as anti-civilization anarchism, is an anarchist critique of civilization that advocates a return to non-civilized ways of life through deindustrialization, abolition of the division of labor or specialization, abandonment of large-scale organization and all technology other than prehistoric technology, and the dissolution of agriculture. Anarcho-primitivists critique the origins and alleged progress of the Industrial Revolution and industrial society. Most anarcho-primitivists advocate for a tribal-like way of life while some see an even simpler lifestyle as beneficial. According to anarcho-primitivists, the shift from hunter-gatherer to agricultural subsistence during the Neolithic Revolution gave rise to coercion, social alienation, and social stratification.

Anarcho-primitivism argues that civilization is at the root of societal and environmental problems. Primitivists also consider domestication, technology and language to cause social alienation from "authentic reality". As a result, they propose the abolition of civilization and a return to a hunter-gatherer lifestyle.

History

Roots

The roots of primitivism lay in Enlightenment philosophy and the critical theory of the Frankfurt School. The early-modern philosopher Jean-Jacques Rousseau blamed agriculture and cooperation for the development of social inequality and causing habitat destruction. In his Discourse on Inequality, Rousseau depicted the state of nature as a "primitivist utopia"; however, he stopped short of advocating a return to it. Instead, he called for political institutions to be recreated anew, in harmony with nature and without the artificiality of modern civilization. Later, critical theorist Max Horkheimer argued that Environmental degradation stemmed directly from social oppression, which had vested all value in labor and consequently caused widespread alienation.

Development

John Zerzan, the main theoretical proponent of anarcho-primitivism

The modern school of anarcho-primitivism was primarily developed by John Zerzan, whose work was released at a time when green anarchist theories of social and deep ecology were beginning to attract interest. Primitivism, as outlined in Zerzan's work, first gained popularity as enthusiasm in deep ecology began to wane.

Zerzan claimed that pre-civilization societies were inherently superior to modern civilization and that the move towards agriculture and the increasing use of technology had resulted in the alienation and oppression of humankind. Zerzan argued that under civilization, humans and other species have undergone domestication, which stripped them of their agency and subjected them to control by capitalism. He also claimed that language, mathematics and art had caused alienation, as they replaced "authentic reality" with an abstracted representation of reality. In order to counteract such issues, Zerzan proposed that humanity return to a state of nature, which he believed would increase social equality and individual autonomy by abolishing private property, organized violence and the division of labour.

Primitivist thinker Paul Shepard also criticized domestication, which he believed had devalued non-human life and reduced human life to their labor and property. Other primitivist authors have drawn different conclusions to Zerzan on the origins of alienation, with John Fillis blaming technology and Richard Heinberg claiming it to be a result of addiction psychology.

Adoption and practice

Primitivist ideas were taken up by the eco-terrorist Ted Kaczynski, although he has been repeatedly criticised for his violent means by more pacifistic anarcho-primitivists, who instead advocate for non-violent forms of direct action. Primitivist concepts have also taken root within the philosophy of deep ecology, inspiring the direct actions of groups such as Earth First!. Another radical environmentalist group, the Earth Liberation Front (ELF), was directly influenced by anarcho-primitivism and its calls for rewilding.

Primitivists and green anarchists have adopted the concept of ecological rewilding as part of their practice, i.e., using reclaimed skills and methods to work towards a sustainable future while undoing institutions of civilization.

Anarcho-primitivist periodicals include Green Anarchy and Species Traitor. The former, self-described as an "anti-civilization journal of theory and action" and printed in Eugene, Oregon, was first published in 2000 and expanded from a 16-page newsprint tabloid to a 76-page magazine covering monkeywrenching topics such as pipeline sabotage and animal liberation. Species Traitor, edited by Kevin Tucker, is self-described as "an insurrectionary anarcho-primitivist journal", with essays against literacy and for hunter gatherer societies. Adjacent periodicals include the radical environmental journal Earth First!

Criticisms

A common criticism is of hypocrisy, i.e. that people rejecting civilization typically maintain a civilized lifestyle themselves, often while still using the very industrial technology that they oppose in order to spread their message. Activist writer Derrick Jensen counters that this criticism merely resorts to an ad hominem argument, attacking individuals but not the actual validity of their beliefs. He further responds that working to entirely avoid such hypocrisy is ineffective, self-serving, and a convenient misdirection of activist energies. Primitivist John Zerzan admits that living with this hypocrisy is a necessary evil for continuing to contribute to the larger intellectual conversation.

Wolfi Landstreicher and Jason McQuinn, post-leftists, have both criticized the romanticized exaggerations of indigenous societies and the pseudoscientific (and even mystical) appeal to nature they perceive in anarcho-primitivist ideology and deep ecology.

Ted Kaczynski also argued that anarcho-primitivists have exaggerated the short working week of primitive society, arguing that they only examine the process of food extraction and not the processing of food, creation of fire and childcare, which adds up to over 40 hours a week.

Parental rights movement

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Parental_rights_movement

The parental rights movement is a socially conservative political movement aimed at restricting schools' ability to teach or practice certain viewpoints on gender, sexuality and race without parental consent.

One of the aims of the movement is to prevent schools from using the preferred pronouns or chosen names of transgender and non-binary youth without disclosing to, or gaining permission from parents. More broadly, it aims to prevent the teaching of LGBT issues in public schools without parents' agreement. Additionally, the parental rights movement has sought to increase parents' control over how children are taught about sexuality and race relations.

The parental rights movement was brought to mainstream attention with the passage of the Parental Rights in Education Act in Florida, colloquially known as the Don't Say Gay law, by Governor Ron DeSantis. Since then, the movement has expanded across the US and Canada. Proponents of the movement have claimed that they aim to prevent the indoctrination of children by LGBT activists, while opponents of the movement argue that parental rights legislation endangers children by possibly outing them to unaccepting guardians.

Definition

Jen Gilbert, a professor at the University of Toronto's Ontario Institute for Studies in Education defined the movement as "a conservative movement to limit the influence of government in people's lives...more generally around the schooling, the parental rights movement has emerged as a movement to limit discussions of sexuality and gender in schools under the auspices of both protecting children and protecting parents' rights to raise children as they see fit".

Media response

The parental rights movement is viewed by some commentators as a form of pushback by conservatism against widespread acceptance of LGTBQ+ individuals and issues more broadly. It has been described as a far-right movement by Dan Lett of the Winnipeg Free Press and by progressive-leaning outlets Salon.com and Michigan Advance. The modern parental rights movement has been characterised by journalist Catherine Caruso as a resurgence of a similar movement in the 1990s. Caruso likened the movement to the stigmatization of HIV during the AIDS epidemic. He identified similar themes with the 1994 bill Contract with America and the Contract with the American Family.

United States

See also: 2020s anti-LGBT movement in the United States

Starting in 2020, parental rights activists in the United States have sought to regulate how race is taught in schools. Prompted by protests after the killings of George Floyd and Breonna Taylor, schools increasingly added antiracist texts to their curricula and diversity, equity, and inclusion measures to their policies and practices. Advocacy from the parental rights movement led to a backlash against those trends, and a wave of laws and regulations—often codified as anti-critical race theory rules—were passed in 2021. Legal scholar LaToya Baldwin Clark connects the 2020s activism to historical backlash from White parents to "contestations over race" like desegregation.

Groups have suggested that similar ideas by parental rights advocates, which have worked to restrict education on sex or sexuality, date back to the 1990s. According to research by the Public Religion Research Institute, the movement's failure to substantially shift norms in public education led many conservative Christian parents to withdraw their children from public schools and move to homeschooling or private schools.

Florida Governor Ron DeSantis brought the parental rights movement to mainstream attention after he signed the Parental Rights in Education Act.

In 2022, the US state of Florida passed the Florida Parental Rights in Education Act, regulating all public schools in the state. The law prohibits public schools from having "classroom discussion" or giving "classroom instruction about sexual orientation or gender identity from kindergarten through third grade or in any manner deemed to be against state standards in all grades; prohibits public schools from adopting procedures or student support forms that maintain the confidentiality of a disclosure by a student, including of the gender identity or sexual orientation of a student, from parents; and requires public schools to bear all the costs of all lawsuits filed by aggrieved parents."

Following its passage, Republicans in the House of Representatives introduced the Stop the Sexualization of Children Act, a bill largely based on the act in Florida.

During the nomination of Justice Ketanji Brown Jackson in 2022, Senator Marsha Blackburn accused Jackson of having a "hidden agenda" to restrict parental rights and expand government reach into schools.

As of 2023, 20 states have had their legislatures introduce derivative bills of the Parental Rights in Education Act, including Arizona, Georgia, Iowa, Kentucky, Louisiana, Michigan, Missouri, Ohio, Oklahoma, Tennessee, and South Carolina. In April 2022, Alabama became the second state to pass a similar bill, with Governor Kay Ivey signing House Bill 322, legislation which additionally requires all students to use either male or female bathrooms in Alabama public schools based on their biological sex. Some states have had similar provisions to Florida's law since the 1980s, though they were never called Don't Say Gay bills by critics until the 2020s.

Many potential candidates for the 2024 Republican Party presidential primaries made parental rights a major theme of their platform. Focusing on school literature with racial or sexual content, parental control over curriculum, and LGBT education, possible candidates like Glenn Youngkin, Ron DeSantis, and Donald Trump have endorsed the goals of the parental rights movement. Coverage in CNN has described this use of "parents' rights" as "an umbrella term for a host of cultural issues".

Canada

In 2009, Alberta passed an act that—while enshrining the rights of sexual minorities—also included a provision that would give parents the option of pulling their children out of lessons when topics related to sex, religion, or sexual orientation were taught. It was referred to as a "parental rights clause" in the media.

Prior to the start of the 2023 school year, the province of New Brunswick altered a policy affecting both formal and informal name changes at school, and the ability of students to choose their preferred pronouns. The revised Policy 713 (Sexual Orientation and Gender Identity Policy) denied students under the age of sixteen the right to make changes to their personal preferences without first receiving parental consent. The province's Minister of Education, Bill Hogan, stated that the policy review which led to the changes had been prompted by complaints from parents. The policy review was controversial, and along with concerns about Premier Blaine Higgs's leadership style, led to calls during the summer for a review of his leadership of the Progressive Conservative Party of New Brunswick. In response, the Christian conservative activist Faytene Grasseschi started a campaign called "Don't Delete Parents", encouraging people to sign a petition in support of Higgs, to pledge support for "pro-parent" political candidates, and to promote the idea that tax dollars should "follow the family" if parents chose to withdraw their children from the public school system in favour of homeschooling or private schools.

In the same year, Saskatchewan also introduced a policy requiring parental consent for children who wished to change their names or pronouns in school and placing restrictions on sexual health education. Following a judicial injunction against the policy, Premier Scott Moe announced that he would invoke the Constitution's Notwithstanding clause to override the decision and bring the policy into law. On October 20, 2023, the government invoked the notwithstanding clause and passed the Parents' Bill of Rights. A national Christian lobbying group called "Action4Canada" has taken credit for influencing the Saskatchewan Party government towards the policy.

In September 2023, Ontario Premier Doug Ford accused school boards in the province of "indoctrinating" students on gender identity, and stated that parents should be involved with decisions around pronoun use at schools.

In the lead-up to the 2023 Manitoba general election in October 2023, the Progressive Conservative Party led by Heather Stefanson promised expanded parental rights in schools. Stefanson's party was defeated by Wab Kinew's New Democratic Party.

Federally, Conservative Party of Canada members adopted a resolution to prohibit "medicinal or surgical interventions" for gender-diverse and transgender kids at the party's 2023 policy convention. Party leader—and leader of the Opposition—Pierre Poilievre has said that schools should leave LGBT issues to parents.

In 2023, the "1 Million March 4 Children" was a series of parental rights protests carried out in various cities throughout Canada. The protesters included adults and students, who claimed that children were being exposed to inappropriate topics regarding sexuality and gender identity and that students in some Canadian schools were being encouraged by teachers to change their pronouns and get "body-altering surgery" without parental knowledge. The protests drew significant counter-protests.

Europe

France

In 2022, following the addition of a gender-neutral pronoun to French dictionaries, French Minister of Education Jean-Michel Blanquer insisted that it was "not the future of the French language" and banned its use in schools.

Ireland

In Ireland, groups such as the Irish Education Alliance and religious bodies such as the Catholic Secondary School Parents Association have opposed the government's introduction of mandatory education about gender identity, pornography, and sexuality, which they perceive as overriding the ethos and rights of parents and schools.

Impact on LGBT youth

Pro-LGBT students in New Brunswick, Canada conducting a walkout protest against parental rights movement induced changes to the province's Sexual Orientation and Gender Identity policy.

Opponents of the parental rights movement argue that the policy would result in forcibly out children to parents or guardians who may not be accepting of their gender or sexual identities. In Canada, opponents such as Marci Ien, the Minister for Women and Gender Equality and Youth, has said that requiring parental consent to use different names or preferred pronouns places trans children in a "life or death situation." In the United States, organised opposition like the Human Rights Campaign oppose the expansion of bills that limit LGBT freedom and expression in schools, suggesting they "stigmatize and marginalize" the LGBT community.

Critics of the parental rights movement include parents, teachers, students, human rights groups, and corporations. They argue that policies which forcibly out LGBT children can be damaging or life-threatening to those with unsupportive families. Such policies have garnered significant concern due to the claimed potential for adverse consequences, including emotional distress, harm to mental well-being, and life-threatening situations for those affected, and can exacerbate issues such as depression, anxiety, and self-esteem problems. It has been suggested that these issues may lead to long-term emotional scars and negatively impacting their overall quality of life.

Additionally, critics highlight that the parental rights movement's insistence on parental control over a child's disclosure of their LGBT identity can perpetuate discrimination and prejudice. By prioritizing parental rights over a child's autonomy, these policies may inadvertently discourage open and honest communication within families, hindering the ability of LGBT youth to seek support or understanding from their loved ones.