Tuesday, July 25, 2017

Dopamine says "Make antibodies"

Photo 1: Lymphatic system
in brain. Source
For a long number of years nervous system and immunological system have been seen as two separate systems. In the last couple of decades this idea has been strongly questioned. Central nervous system which has been assumed to be devoid of immune activity is now known to harbour immune system of its own (Link). Their location was quite close to prominent blood vessels. There has been some proof that neural system could in part regulate neural activity also (Link). In 2015, Louveau et al reported that the brain has a lymphatic system of its own. The anatomical discovery was surprising since the vessels’ were hidden in a location deep within the brain. Lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), is a marker of lymphatic system. LYVE1 immunostaining of whole-mount meninges is shown in Photo 1 showing distribution of brain lymphatics.

There are several papers that have established that neuroimmune interactions are 2 way. Classical nervous system molecules such as dopmaine can act on immune cells. For example, T cells express several dopamine receptors (DARs). It has been established that stimulation of specific DARs on Dendritic cells and T cells, influence CD4+ T cell differentiation into Th1 or Th17 inflammatory cells. Dopamine receptors are universally expressed in T cells, dendritic cells (DCs), B cells, NK cells, neutrophils, eosinophils, and monocytes. Fig 1, shows an example of established pathways on how neurotransmitters affect T cell response. Ofcourse, the reverse is also true, though the phenomenon is less well studied. A really good example is cytokines. IL-6 is now highly implicated as a Neuropoetin (Stimulate Neuronal growth), though mechanism is not clearly defined.

Fig 1: Neurotransmitter-mediated regulation of T cell response. Source
So far, so interesting. There is convincing evidence that dopamine receptor is important. But having a receptor is one thing. There has been some research earlier suggesting that the immune cells can themselves also make dopamine and it has not been clear as to what is the role. And thats the new story.

Making an antibody is a very tightly regulated process. When the immune system encounters a foriegn molecules the T cells delivers signal to B cells in a complicated molecular process. In germinal centres, highly mobile T cells and B cells specific for the same pathogen can directly interact with each other through the formation of dynamic specialized surface structures called T–B immunological synapses. In a new study by Papa etal, showed that the Follicular helper T cells or TFH (They are also known as Follicular B helper T cells) use dopamine as cargo loader for immune molecules which mediates a T-B synapse which triggers B cell maturation.

Fig 2: Graphic model of the proposed positive feedback
between human TFH and germinal centre B cells.
Source
The first experiment goes on to show that Chromogranin B (CGB) is present in human tonsils, spleens, and lymph nodes and determined that the T cells isolated from these samples icontained granules filled with dopamine. Using a method called live-cell RNA detection CGB was shown to be present in high quantities of human germinal centre TFH cells but not so much in other T cells. Subsequent experiments showed that dopamine was not really there in detectable amounts in cells other than TFH cells. Next set of experiments showed that human TFH cells released dopamine on stimulation by germinal-centre B cells and it upregulated the ICOSL on the cell surface of germinal-centre B cells. This process was also shown to enhance accumulation of CD40L and chromogranin B granules at the human TFH cell synapse and increases the synapse area. The tests also showed that the process could be blocked by haloperidol and a DRD1 specific antagonist SKF83566, thus narrowing down the receptor to DRD1. Based on the experimental findings, the authors proposed an interaction model, shown in Figure 2. According to an explanatory accompanying paper, it has been hypothesised that TFH cells have a very stringent requirement for efficiency and specificity at the immunological synapse, which explains the finding that dopamine is used by human TFH cells, but not by human T cells of other subclasses.

So now I have some questions. Is there a possible mechanism where dopamine from a neuron stimulates B cells? Does this finding explain why in certain dopamine related disorders such as schizophrenia (where there is presumably an increased dopamine actvity) have an increased autoimmune phenomenon. As Hai Qi speculates, "When disease characteristics or treatment options are associated with changes in dopamine, the possible involvement of, and implications for, antibody- mediated immunity should be considered".

As Papa the lead author comments, “These particles were previously thought to only exist in neurons in the brain and we think they are, potentially, an excellent target for therapies to speed up or dampen the body’s immune response, depending on the disease you’re dealing with. Like neurons, specialised T cells transfer dopamine to B cells that provides additional ‘motivation’ for B cells to produce the best antibodies they can to help to clear up an infection. The human body has developed an advanced form of protection against bacteria, viruses and other foreign bodies that relies on the immune system".

References:

Papa I, Saliba D, Ponzoni M, Bustamante S, Canete P, Gonzalez-Figueroa P et al. TFH-derived dopamine accelerates productive synapses in germinal centres. Nature. 2017;547(7663):318-323.

Qi H. Immunology: Nervous crosstalk to make antibodies. Nature. 2017;547(7663):288-290.

Friday, July 21, 2017

nCD64 as a marker of Sepsis

Several times in my blogs, I have talked about how important it is to make a diagnosis at the fastest turn around time possible. In an attempt to miniaturise the testing platform and obtaining faster results, several technologies have been tested. In context with infections, genome detection and sequencing based technologies are increasingly becoming better and more accessible. Another example is pathogen specific molecular marker detection method on which a good lot of R&D is invested. MALDI-TOF is an excellent example.

Fig 1: Hospitalisation rates for sepsis or septicemia.
Sepsis is a serious issue. Any clinical microbiologist who works in association with the hospital knows the seriousness of sepsis. The terms "Sepsis" and "Septicemia" both refer to a bloodstream infection. Though in a strictly technical sense they mean two different things, they have been interchangeably used in literature and was widely accepted as similar. The earlier definition of sepsis was based on the idea that it is a systemic response and was thus assessed using a systemic inflammatory response syndrome (SIRS) criteria. To date, there is no clear definition of what sepsis is though it is generally agreed that it means circulating pathogen in blood. The diagnosis is based on evidence of fever, respiratory rate and abnormal total WBC count followed by bacterial identification from blood culture. There are no global estimates of sepsis prevalence. Available estimates suggest a range of <1% in a population. However,  there is a significant trend observed everywhere as shown in Fig 1.

In most parts of the globe, a prediction of sepsis is made based on markers such as C reactive protein and procalcitonin levels. Many studies have attempted to come up with a marker. Some of the well-researched markers of sepsis include triggering receptor expressed on myeloid cells-1 (TREM-1), azurocidin, CD64, CD11b etc.

Fig 2: Process schematic of the differential expression-based
cell-counting technology. Source
Studying these markers in the laboratory is not the big deal, since instruments such as Flow cytometers and other sophisticated equipments can do it. But they are not ideal for POCT (Point of care testing). In 2015, this problem was addressed by developing a POCT equipment based on microfluidics. The same group has now come up with improvements in design. The microfluidic biochip is capable of enumerating leukocytes and quantify neutrophil CD64 (nCD64) levels from 10 ml of whole blood without any manual processing. The tech uses whole blood (10ml) which is pumped into the biochip along with lysing and quenching buffers, to lyse erythrocytes. Cells are electrically counted and differentiated based on size using microfabricated electrodes. The CD64+ cells get captured based on their CD64 expression level. The difference in the cell counts is used to calculate nCD64 expression level. See Fig 2.

The authors claim that this technology can have profound results since the assay takes about 30 min and has scope for further improvement. That would be something really usefull to clinicians as a bedside tool for identifying sepsis.

References:

Mervyn Singer et al. The Third International ConsensusDefinitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801-810. doi:10.1001/jama.2016.0287

Mayr F, Yende S, Angus D. Epidemiology of severe sepsis. Virulence. 2013;5(1):4-11.

Wang X, Li ZY, Zeng L, Zhang AQ, Pan W, Gu W, Jiang JX. Neutrophil CD64 expression as a diagnostic marker for sepsis in adult patients: a meta-analysis. Crit Care. 2015 Jun 10;19:245. doi: 10.1186/s13054-015-0972-z.

Hassan U, Reddy B Jr, Damhorst G, Sonoiki O, Ghonge T, Yang C, Bashir R. A microfluidic biochip for complete blood cell counts at the point-of-care. Technology (Singap World Sci). 2015 Dec;3 (4):201-213. DOI: 10.1142/S2339547815500090

Hassan U et al. A point-of-care microfluidic biochip for quantification of CD64 expression from whole blood for sepsis stratification. Nat Commun. 2017 Jul 3;8:15949. doi: 10.1038/ncomms15949.

Monday, July 03, 2017

Lab series #17: Labelling methods for Quantitative Proteomics by MS

In an earlier post, I have talked about the principle of how a mass spectrometry works (Link) and how proteomics by sequencing is done using MS (Link). I had a few readers who suggested the idea that I have talked about MS-based shotgun sequencing but proteomics could be done even without sequencing. For example, MALDI-TOF analysis can tell about the protein identity which doesn't involve sequencing. This is absolutely true. However, such assays are now nearly outdated and sequence information can give us a lot more insight than just predicting protein based on the m/z values. In the earlier post, I ended with a note saying that I will revert back to the topic and talk about proteogenomics, targeted proteomics and quantitative proteomics. In this post, I will talk about labelling methods for quantitative proteomics or sometimes referred to as differential proteomics. If you have not read my earlier posts on MS, I strongly recommend that you read them first.

Let us build an example scenario. You want to learn what are the changes that occur in the cell after a virus infection. The most likely scenario in terms of proteome would be certain proteins will have increased expression and certain will have decreased expression, as a result of interaction with a virus. If you could find out what those proteins are, then there is a good chance that you could predict the pathways that have been disrupted. But for identifying what is the fold change, we have to quantify each protein. In a traditional assay like quantitative ELISA, the protein is directly estimated using a set of standards and then plot a graph. In proteomics, several thousand proteins are estimated in a single run and hence it is not practical to have several standards for every individual protein.  MS technique is originally designed to be a detection methodology and not a quantitative technique.

MS is a very sensitive technique, and there is a statistical chance that certain ions are more easily picked up than others which mean that the peak height or area in a mass spectrum in itself does not accurately reflect the abundance of a peptide in the sample. The main reasons for this are the differences in ionisation efficiency and detectability of peptides. Mathematically the equation would look something like this (I will not get into the actual mathematics since that is not relevant here).

Protein concentration= MS abundance value x Error factor

The error factor depends on each run and will vary from experiment to experiment. Consider this experiment. If you have a cell lysate you run it 10 times in LC-MS/MS analysis the final result will be varied from experiment to experiment. In fact, the number of proteins identified will also significantly change and you can expect a variation of at least 30% between any two runs as shown by multiple studies. If you run 2 independent batches of LC-MS/MS for comparison then the final result will consist only of error for purposes of direct comparison. The best idea would be to compare proteins from test and control in the same run so that the error will be constant. Since the error factor is the same in both cases (which is unknown), relative fold change can be accurately calculated by comparing the abundance value of m/z peak from the experiment. 


So what is required for comparison is to run all the protein preparation that has to be compared in a single mass spec run. Now you need a method to tell which peptide came from whom. That is why we label the peptide library obtained from each case. Let us say you want to run 5 biological test cases against 5 biological control case that would be a 10 plex labelling experiment with each condition being labelled with a different label. The label will tell MS where the peptide originally came from and how much of it is there in t.

Fig 1: Hypothetical example of m/z abundance
as an indicator of fold change.
Fig 1, is a hypothetical example of m/z abundance as an indicator of fold change. Consider you are comparing 3 cases against a control sample. The height of the peak represents the peptide abundance. In comparison to control, the case 1 is slightly elevated, case 2 is drastically down and case 3 is unchanged. This kind of comparison is available for all the peptides that have been detected in MS. The overall finding is then curated by the software and presented as a protein expression data with reference to the control.

Fig 2: Labelling methods for quantification of proteins in Mass Spectrometry.
There are wide varieties of labelling methods available and different literature have a different classification and there is an overlap in some cases. For simplicity, labelling methods can be broadly classified into 3 subtypes- Metabolic, Enzymatic and chemical labelling.See Fig 2 for a summarised classification.  It is not possible to talk about all the methods and intricate details of every method, which would make this post too long. I will stick to explaining a few methods that are more famous in biological practice which will give an idea of what exactly is happening. Chemical labelling is much similar to metabolic labelling except that the label is chemically attached to a particular peptide after extraction unlike doing it metabolically. Enzymatic labelling is almost a chemical labelling except that it is done using an enzymatic process.

Stable isotope labelling by amino acids in cell culture (SILAC)

Fig 3: Example light and heavy amino acids for SILAC.
SILAC labelling was first demonstrated from Matthias Mann lab; 2002. The method is a metabolic labelling method. The core idea is that cells are given essential amino acids that carry heavy stable isotopes continuously, which gets converted into proteins in the cell. This process run for sufficient time, a great majority of the cell proteins contain heavy labelled isotopes which can be picked in the mass spec. In a typical SILAC labelling experiment, lysine and arginine residues are isotope labelled. Since trypsin digestion is commonly used for obtaining peptides this results in labelling of every peptide in the mixture. The labels are available as N terminal and C terminal labelled lysine or arginine. See Fig 3. In addition, leucine, tyrosine and methionine amino acids with incorporated isotopes have also been used as labels. SILAC method has a high efficiency but comes with inherent limitations. Other than the facts that it is time consuming and expensive it requires that the method uses a culture system only those that can be cultured are available to work with this method.

18 0 labelling


The methodology considers the idea of class-2 proteases, such as trypsin, to catalyse the exchange of two 16 O atoms for two 18 O atoms at the C-terminal carboxyl group of proteolytic peptides. Hydrolysis of a protein in H218O by a protease results in the incorporation of one 18 O atom into the carboxyl terminus of each proteolytically generated peptide. Despite its simplicity, the method is not in regular use owing to the difficulty in attaining a high labelling accuracy.

Labelling using Isobaric tags

Fig 4: Structure of TMT tags. Source
This is probably one of the most common labelling methods to be used. Let us take the example of TMT (Tandem mass tags). Labels are basically isobaric compounds (They have same net mass) with a peptide binding site.

Each chemical tag contains a different number of heavy isotopes in the mass reporter region, which gives a unique reporter mass during tandem MS/MS for sample identification and relative quantitation, a mass normaliser which adjusts for the mass and a reactive group.

I have limited the discussion on labelling methods to the basic essence to give you an idea of how the system works. I recommend you read the references to have a detailed picture of the process.

References:

Tabb et al.  Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography-Tandem Mass Spectrometry. J Proteome Res. 2010 Feb 5; 9(2): 761. doi: 10.1021/pr9006365

Ong S, Mann M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nature Protocols. 2007;1(6):2650-2660.

Rauniyar N, Yates J. Isobaric Labeling-Based Relative Quantification in Shotgun Proteomics. Journal of Proteome Research. 2014;13(12):5293-5309.

Sunday, July 02, 2017

Angiostrongylus cantonensis

By the end of April this year, rat lungworm or Angiostrongylus cantonensis raised an alarm with 6 cases which was apparently spreading on the Hawaiian island of Maui. The infection is called as angiostrongyliasis. it basically manifests as meningitis and if not treated can cause permanent damage to the central nervous system or death. A cantonensis is well known in South-east Asian and Pacific region and is one of the most common causes of eosinophilic meningitis. The More recently, the parasite is been found in environments of California, Alabama, Louisiana Leon and Florida regions including Alachua, Saint Johns, Orange, and Hillsborough (Where it is much more common than originally realised).

Photo 1: Adult female of A cantonensis.
Source
A cantonensis is a species related to Strongyloides stercoralis (Order strongylida, and superfamily metastrongyloidea) was first described from CSF of an eosinophilic meningitis case by Nomura and Lim in Taiwan in 1944. Morphologically, they appear like other nematodes unsegmented with a simple fully developed gastrointestinal system. Males are smaller than the females. Photo 1, shows an adult female with a dark red digestive organ, two white reproductive organs, and a transparent cuticle.

Photo 2:cantonensis egg.
Source
A cantonensis is classically an infection of rats and humans are accidental hosts. A cantonensis reside in pulmonary arteries of rats. A female lays up to 15000 eggs a day. Once eggs are laid, they hatch and the first stage larva migrates up the pharynx and are swallowed to be secreted through faeces. Snail forms an intermediary host which is infected with the larva where it develops. The 3rd stage larva in the intermediate host is the infective stage. It enters through contaminated water, ingestion of snails the larva migrates up the brain. The larvae develop into a pre adult stage and then return to the pulmonary artery, where it matures.

Fig 1: Life cycle of A cantonensis.Source
Many species of snails and slugs serve as intermediate hosts for A cantonensis. Some of the well known vectors include Achatina fulica (African land snail), Pila species, golden apple snail etc

Humans acquire the infection accidentally through ingestion of contaminated water, other infected paratenic animals (crabs, freshwater shrimps) or leafy vegetables which are not cleaned and washed well.Fig 1 from CDC page gives a detailed picture of the life cycle.

The clinical presentation will usually involve symptoms such as abdominal discomfort which progress to fever and headache. Often the case will self resolve without treatment and may not be evident. In cases of high load of parasites, the infection progresses to serious meningitis. The laboratory diagnosis is at best, a prediction. Eosinophilic meningitis and imaging studies maybe a clue. To date, there are no definitive diagnostics available. It should be noted that eosinophilic meningitis is not a definitive indicator for A cantonensis. Other agents include Angiostrongylus costaricensis, Gnathostoma spinigerum etc. Though PCR testing is available it is done only in special labs and not widely available.

In a recently published article in Digital Journal by Karen Graham, there is a discussion on what increased identification of A cantonensis means in Florida. As Heather Stockdale Warden comments, "The ability for this historically subtropical nematode to thrive in a more temperate climate is alarming. The reality is that it is probably in more countries than we found it in, and it is also probably more prevalent in the southeastern U.S. than we think".

References:

Stockdale Walden, H., Slapcinsky, J., Roff, S., Mendieta Calle, J., Diaz Goodwin, Z., Stern, J., Corlett, R., Conway, J. and McIntosh, A. (2017). Geographic distribution of Angiostrongylus cantonensis in wild rats (Rattus rattus) and terrestrial snails in Florida, USA. PLOS ONE, 12(5), p.e0177910.